Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Predicting hotspots for threatened plant species in boreal peatlands

Predicting hotspots for threatened plant species in boreal peatlands Understanding the spatial patterns of species distribution and predicting suitable habitats for threatened species are central themes in land use management and planning. In this study, we examined the geographic distribution of threatened mire plant species and iden- tified their national hotspots, i.e. areas with high amounts of suitable habitats for threat- ened mire plant species. We also determined the main environmental correlates related to the distribution patterns of these species. The specific aims were to: (1) identify the envi- ronmental variables that control the distribution of threatened peatland species in a boreal aapa mire zone, Finland; and (2) to identify the richness patterns and hotspots of threat- ened species. Our results showed that the combination of individual species models offers a useful tool for identifying landscape-scale richness patterns for threatened plant species. The modeling performance was high across the modelled species, and the richness patterns generated by single models coincide with the expected richness pattern based on expert knowledge. The method is therefore a powerful tool for basic biodiversity applications. In cases where reliable models for species occurrences and hotspots can be produced, these models can play a significant role in land-use planning and help managers to meet different conservation challenges. Keywords Threatened mire plant species · Modeling · Boreal peatlands · Habitat suitability Introduction Peatlands are core ecosystems of biological diversity and are known for their wide range of ecosystem services (Ramsar Convention Secretariat 2013). As highly productive eco- systems, they are used increasingly to support economic development and human well- being. Drainage and resource exploitation of wetlands are the main reasons why they are among the most threatened ecosystems in the world. For example the area covered Communicated by Frank Chambers. * Miia Saarimaa miia.saarimaa@luke.fi Extended author information available on the last page of the article 1 3 Vol.:(0123456789) 1174 Biodiversity and Conservation (2019) 28:1173–1204 by peatlands (the most widespread wetland type) has reduced by 10–20% since 1800 (Joosten and Clarke 2002). In Finland, over half of peatlands have been drained for for- estry (Finnish Forest Research Institute 2014), which has caused habitat degradation and increased the number of threatened peatland species. At present, there are 223 red- listed vascular plant and bryophyte species with peatlands as their primary habitats (4.5% of all red-listed species) and 420 red-listed species with peatlands as one of their habitats (Rassi et  al. 2010). The ongoing bioeconomy development (Spatial Foresight, SWECO, ÖIR, t33, Nordregio, Berman Group, Infyde 2017) and high interest in Arctic countries for their mineral resources (Boyd et al. 2016) are increasing pressures on peat- lands. These intense activities are expected to have strong, and mostly negative, impacts on peatland biodiversity. Many of the most adverse effects resulting from peatland use can be avoided through careful planning. This requires an analysis of potential ecological values in an area before intensive and/or large-scale use is planned and carried out. Hotspots, or concentrations of threatened species, are important surrogates of biological diversity that have a significant role in conservation and management strategies (Gaston 1994). Although locations of hot- spots should not be the guiding principles in land use planning, they can be used to avoid disturbing valuable sites with high numbers of rare species (Loiselle et al. 2003; Elith and Leathwick 2009). Predictive species-distribution modeling offers a cost-effective method of exploiting the limited empirical data for the evaluation of biodiversity. Statistics-based spatial models are valuable for generating biogeographical information that can be applied across a broad range of fields, including ecology, land use planning and climate change (e.g. Barbet-Massin et  al. 2012; Bolliger et  al. 2007; Thuiller et  al. 2008). Habitat suit- ability models rely on the concept of niche conservatism (the tendency of species to retain ancestral ecological characteristics) and assume that environmental variables will play an important and consistent role in shaping species distributions (Wiens and Graham 2005). Predictive habitat suitability models of species’ geographical distributions and species richness are increasingly used as an alternative for incomplete or spatially biased survey data as a basis for conservation planning (Hirzel and Le Lay 2008; Elith and Leathwick 2009; Freeman et al. 2013; Lemes and Loyola 2013). A traditional way to develop spatial projections of species richness is to directly meas- ure numbers of species from surveyed sites and to relate this information to environmental variables derived from GIS data. The analysis produces models that yield predictions of species richness for unsampled sites. In this study, species are first modelled individually, and species richness is estimated by stacking individual habitat suitability models (see also Algar et  al. 2009; Parviainen et  al. 2009; Mateo et  al. 2012, 2013). The top 5% of grid squares ranked by species richness can be classified as hotspots. Individual models are con- structed by relating species occurrence data to environmental variables and projecting the modelled relationships onto geographical space (Elith et al. 2006). This method may pro- vide some useful advantages, such as better control for poorly modelled species, and easier identification of the set of the most important explanatory variables and of the response shapes between species and their environment in certain subgroups of species. The aim of this study was to provide a comprehensive picture of environmental pre- requisites for a set of threatened peatland species, thereby helping to plan peatland use in a more ecologically sustainable way. The specific aims were: (1) to scrutinize how the occurrence of species is affected by different environmental correlates; and (2) to identify the richness patterns and hotspots of threatened species. The models were developed for the “aapa mire zone” in central and northern parts of Finland. It is worth noting that spe- cies richness per se was the target of our study. Predictive modelling was focused on the 1 3 Biodiversity and Conservation (2019) 28:1173–1204 1175 richness of species that characterize valuable environments that need specific attention in planning. The study thereby provides a new approach to focused biodiversity modeling. Study area The study was carried out in Finland, located between 63° and 68° latitudes in northern Europe (Fig.  1). Biogeographically the study area lies in the middle and northern boreal zones covering almost the entire aapa mire zone, where climate is more continental than in most other parts of northern Europe but with some humid, maritime effect (Ahti et al. 1968). The annual mean temperature declines from south (+ 5  °C) to north (− 2  °C) and the mean annual precipitation sum varies between 450 and 750 mm (Pirinen and Ruuhela 2012). Peatlands and pine and spruce-dominated forest are frequent, as well as numerous lakes and rivers characterizing the landscape of the study area. The peatland habitats of the studied plant species are of three types. Mesotrophic fens are mainly open peatlands with deep peat deposits. The field layer vegetation is character - ized by sedges and herbaceous plants, and the ground layer consists of sphagnum mosses or other bryophytes. Rich fens are open or sparsely wooded peatlands with high species diversity of vascular plant and mosses. They are typically found in areas where the bedrock and soil are calcium-rich. Approximately half of Finland’s threatened peatland species are primarily associated with rich fens (Rassi et al. 2010). Spruce swamp forests are wooded minerotrophic peatlands where the dominant tree species is usually Norway spruce (Picea abies), though deciduous trees may also grow abundantly in Spruce forests that are richer in nutrients. The presence of living and dead trees of different sizes and ages is an impor - tant structural feature for the species diversity of Spruce swamp forests. Finnish mires have been intensively drained in the last century, and more than half of the 10.0 million hectares of originally pristine mires have been drained to improve timber growth (Finnish Forest Research Institute 2014). The study area was divided into grid cells of 25  ha (500  m × 500  m), and cells where peatlands covered less than 5% were excluded from the study. Thus, the study area consists of a total of 500,545 grid cells (125,136 km ). Plant species data We used the occurrence records of threatened mire plant species from the national database of red-listed species (Rassi et al. 2010) (Table 1). The field records produced by voluntary amateur and professional botanists are the most important data sources for this database, but information on species occurrences has also been gathered from the scientific literature and herbaria (Ryttäri et al. 2012; Rassi et al. 2010). Species data included detailed informa- tion on the geographical location of the occurrences (coordinates in the uniform grid sys- tem, Grid 27°E). In total, 48 species with ten or more records in the whole study area were used in the analyses (Fig. 1, Table 1). Only observations with accuracy better than 100 m and presence observations from 1990 or later, were selected for this study. As the databases of red-listed species do not include records for the absence of species, the assumption was made that the absence of a record from a sampled grid square corresponded to true absence of the species, because a quasi-exhaustive sampling could be assumed for most squares with presence records (Guisan and Zimmermann 2000). 1 3 1176 Biodiversity and Conservation (2019) 28:1173–1204 Fig. 1 The location of the study area in a boreal landscape in northern Finland, together with the distribu- tion map with the observational points of the threatened plant species studied. Land cover classification is based on data about the drainage status of peatlands (SYKE) We modelled the habitat requirements for all species by using the same environmen- tal predictors for each species. Based on their different associations with the various envi- ronmental predictors, we grouped the species into five groups as follows: Mesotrophic fen species (n = 6), rich fen species (n = 10), calcareous species (n = 22), spruce swamp forest species (n = 3) and decaying wood species (n = 7). Rich fen species and calcareous species 1 3 Biodiversity and Conservation (2019) 28:1173–1204 1177 Table 1 Number of presence records in the study area, red list category of the species, main habitats of the species and group for the studied plant species Species Number of records Red List Main habitat Group category Rich fen  Carex heleonastes 779 VU Sl VA  Dactylorhiza incarnata subsp. cruenta 252 VU Sl VA  Dactylorhiza incarnata subsp. incarnata 1545 VU Sl VA  Hamatocaulis vernicosus 2453 VU Sl BR  Leiocolea bantriensis 23 NT Sl BR  Lophozia grandiretis 34 EN Sl BR  Meesia longiseta 96 EN Sl BR  Moerckia hibernica 103 VU Sl BR  Riccardia multifida 11 NT Vl BR  Sphagnum contortum 62 NT Sl BR Mesotrophic fen  Carex laxa 123 NT Snr VA  Epilobium laestadii 56 EN Sl VA  Hamatocaulis lapponicus 70 EN Sl BR  Hammarbya paludosa 239 NT Sn VA  Lycopodiella inundata 30 NT Rjt VA  Rhynchospora fusca 261 NT Sla VA Calcareous  Amblyodon dealbatus 33 VU Sl BR  Botrychium virginianum 54 EN Mlt VA  Bryum pseudotriquetrum var. neodamense 98 VU Sl BR  Calypso bulbosa 1265 VU Mltv VA  Campyliadelphus elodes 30 VU Kk BR  Carex appropinquata 178 VU Sl VA  Carex viridula var. bergrothii 126 VU Sl VA  Cypripedium calceolus 1557 NT Mlt VA  Dactylorhiza fuchsii 66 NT Sl VA  Dactylorhiza lapponica 160 VU Sl VA  Dactylorhiza traunsteineri 376 VU Sl VA  Dicranum acutifolium 13 NT Kk BR  Eriophorum brachyantherum 72 VU Slr VA  Malaxis monophyllos 28 EN Sl VA  Palustriella commutata 60 VU Vl BR  Palustriella decipiens 496 NT Vl BR  Palustriella falcata 549 NT Sl BR  Philonotis calcarea 60 EN Vl BR  Pseudocalliergon angustifolium 68 VU Sl BR  Pseudocalliergon lycopodioides 28 VU Sl BR  Saxifraga hirculus 1127 VU Sl VA  Schoenus ferrugineus 35 EN Sl VA Spruce swamp forest  Carex atherodes 43 NT Skr VA 1 3 1178 Biodiversity and Conservation (2019) 28:1173–1204 Table 1 (continued) Species Number of records Red List Main habitat Group category  Epipogium aphyllum 111 VU Mkt VA  Poa remota 21 NT Sk VA Decaying wood  Anastrophyllum hellerianum 225 NT Mktv BR  Calypogeia suecica 17 VU Mktv BR  Jungermannia leiantha 29 NT Mktv BR  Lophozia ascendens 42 VU Mktv BR  Lophozia ciliata 15 NT Mktv BR  Lophozia longiflora 19 NT Mktv BR  Riccardia palmata 51 NT Skv BR Total 13,189 Red list category: EN endangered, VU vulnerable, NT near threatened. Main habitats of the species: Kk rock outcrops (incl. erratic boulders), Mlt dry and mesic herb-rich forests, Mltv dry and mesic herb-rich forests, old-growth forests, Rjt inland open alluvial shores, Sl rich fens, Sla open rich fens (incl. herb-rich sedge fens), Slr rich pine fens, Sn fens, Snr mesotrophic fens, Vl spring complexes. Group: VA vascular plant, BR bryophytes (Rassi et al. 2010) are partially overlapping. The reason for separating these two species groups was that rich fen species can also be found outside the calcareous areas whereas calcareous species are restricted only to calcareous habitats. Environmental correlates We selected a set of quantitative correlates that would reflect the main biophysical gradi- ents with a recognized, physiological influence on plants. In total, 17 environmental vari- ables were calculated for all of the studied grid squares of 25  ha and were then used to explain plant species distribution. Two correlates/variables indicated climate, one topog- raphy, one geology and 13 local habitat features (Table 2). Correlations among these vari- ables were only moderate (Spearman correlation < 0.7) and thus, none of the variables was excluded a priori from the actual modelling. Temperature and moisture requirements reflect the principal limitations on plant growth and survival (Skov and Svenning 2004). Thus, growing degree days (> 5 °C) (GDD) and water balance (mm) (WAB) were calculated for the years 1981–2010 from climate data with 1 km resolution (Finnish Meteorological Institute, Pirinen and Ruuhela 2012). Water balance was used because precipitation alone is not a good measure of the water available for plant growth. A simple water balance variable was calculated as the monthly difference between precipitation and potential evapotranspiration, as suggested by Skov and Svenning (2004). The potential evapotranspiration (PET) was calculated as: PET = 58.92 × T above 0 C, where T above 0 °C is the annual mean of monthly mean temperatures with negative val- ues adjusted to zero (Holdridge 1967; Lugo et al. 1999). 1 3 Biodiversity and Conservation (2019) 28:1173–1204 1179 1 3 Table 2 List of selected environmental variables used as explanatory variables in the modeling experiments Environmental variable Abbreviation Unit Mean [min–max] Data source Growing degree days (> 5 °C) GDD – 910 [517–1154] FMI −1 Mean water balance WABmm year 330 [137–442] FMI Mean topographical wetness index TWI – 14.11[6.85–25.22] NLS, DEM Proportion of undrained peatlands in grid square UNDRAINED % 16.9 [0–100] SYKE Proportion of drained peatlands in grid square DRAINED % 23.4 [0–100] SYKE Proportion of open peatlands in grid square OPEN % 6.6 [0–100] SYKE Proportion of herb-rich site type (peatlands) in grid square KP1 % 0.5 [0–100] Luke, MS-NFI Proportion of Vaccinium myrtillus site type (peatlands) in grid square KP2 % 2.1 [0–100] Luke, MS-NFI Proportion of Vaccinium vitis-idaea site type (peatlands) in grid square KP3 % 10.9 [0–100] Luke, MS-NFI Proportion of Cladina site type (peatlands) in grid square KP6 % 1.5 [0–100] Luke, MS-NFI Proportion of calcareous rock in grid square CALC % 0.4 [0–100] NLS, DBM Mean volume, pine PINE m /ha 34.86 [0–201.50] Luke, NS-NFI Mean volume, spruce SPRUCE m /ha 11.52 [0–254] Luke, NS-NFI Mean volume, birch BIRCH m /ha 12.47 [0–130.50] Luke, NS-NFI Mean volume, other broad-leaved trees OTHER m /ha 12.47 [0–130.5] Luke, NS-NFI Presence of springs SPRING – 0/1 NLS Data sources: FMI Finnish Meteorological Institute, NLD national land survey, DEM digital elevation model, SYKE Finnish Environment Institute, Luke; Natural Resources Institute Finland, NS-NFI multi-source national forest inventory, DBM digital base map 1180 Biodiversity and Conservation (2019) 28:1173–1204 Moisture conditions in peatlands can be related to many ecological processes across landscapes, e.g. species composition and distribution, peatland productivity (Iverson et al. 1997) and hydrology in terms of ombrotrophy and minerotrophy. Topographic wetness index (TWI) was used to describe local relative differences in moisture conditions (Gessler et  al. 2000). High values represent lower catenary positions (wet) and small values upper catenary positions (dry). The moisture level of the study area was calculated by defining the wetness index (or compound topographic index) using the following formula (Bur- rough and McDonnell 1998): TWI = ln(∕tan), where ɑ is the upslope contributing area per width orthogonal to the flow direction, and tanβ is the local slope in radians. Moreover, data about the drainage stage of peatlands with a resolution of 25 m × 25 m (Finnish Environmental Institute 2009) were used to calculate the proportion of undrained (UNDRAINED) and drained (DRAINED) peatland area as percentage cover for each grid square. As many of the threatened plant species require calcareous substrate, the proportion of calcareous rock (CALC) as percentage extent for each grid square was calculated from dig- ital maps of Quaternary deposit and pre-Quaternary rocks (Digital Base Map, NLS) using ArcGIS software (ESRI 1991). The presence of springs (SPRINGS) was used as many of the modelled species benefit from springs. Information on the percentage cover of main peatland site types and site fertility in each 25-hectare grid square was derived from the Multi-source National Forest Inventory (MS- NFI) from 2011 (Natural Resources Institute Finland 2013) with a resolution of 20 m × 20 m. Site fertility classes were selected to match the habitat requirements of the studied plant species as accurately as possible: eutrophic peatlands and corresponding drained peatlands (namely herb-rich types, KP1), mesotrophic mires and fens and corresponding drained peatland forests, (Oxalis-myrtillus type, KP2), meso-oligotrophic natural and drained peat- lands (Vaccinium myrtillus type, KP3), and Sphagnum fuscum-dominated (ombrotrophic) natural and drained peatlands (Cladina type, KP6). The proportions of open peatlands (OPEN MIRE) and spruce swamp forests (SPRUCE SWAMP FOREST) in grid squares were used to reflect general habitat patterns of peatland properties in each grid squares. Moreover, mean volumes (m /ha) of four tree species—pine (PINE) (Pinus sylvestris), spruce (SPRUCE), birch (BIRCH) (Betula pendula and B. pubescens) and other broad- leaved trees (OTHER) were calculated from MS-NFI-data and employed in the modeling. Habitat suitability modelling The presence-only habitat suitability modelling method Maxent v3.3.3  k (Phillips et  al. 2006) was used to predict species distributions across the aapa mire zone. The resulting habitat suitability model represents the relative probability of the species’ distribution over all grid squares in the defined geographic space, where a high probability value indicates that the location is predicted to have suitable environmental conditions for the species (Hir- zel et al. 2002). Maxent has been utilized extensively to model species’ ranges using pres- ence-only data, and it has been shown to perform well even with scarce and noisy presence data subsets collected by different researchers and methodologies (Elith et al. 2006; Frank - lin 2010). Maxent has also performed well in modelling other ecosystem services, such as the distribution of GHG-balances (Parkkari et al. 2017). 1 3 Biodiversity and Conservation (2019) 28:1173–1204 1181 To be able to compare and combine or stack models for multiple species, the same environmental predictors and Maxent parameters were used for all species. Model calcula- tions were made using the Maxent logistic output, rather than raw or cumulative output, in order to facilitate comparisons between species (Merow et al. 2013). Maximum iterations were set at an average of 5000, based on model performance across all target species. The remaining settings were left at the default setting. Moreover, response curves were created to show how the predicted relative probability of occurrence depends on the value of each environmental variable. Model evaluation The models and model predictions were evaluated using the area under the curve (AUC) of a receiver operating characteristic (ROC) plot based on a four-fold cross-validation (Field- ing and Bell 1997), routinely calculated for each run with Maxent. Cross-validation was performed with subsets of the entire dataset, where each subset contained an equal number of randomly selected data points. Each subset was then dropped from the model, the model was recalculated, and predictions were made for the omitted data points. A combination of the predictions from the different subsets was then plotted against the observed data (Lehmann et  al. 2002). Following Swets (1988), model accuracy was considered low if AUC was below 0.7, fair if it was between 0.7 and 0.8, good if between 0.8 and 0.9, and excellent if AUC was above 0.9. To identify the relative importance of various environmental variables for species, we employed two outcomes of the Maxent model: percent contribution and permutation importance of each environmental variable. The percent contribution values are only heu- ristically defined: they depend on the particular path that the Maxent code uses to arrive at the optimal solution, and a different algorithm could give rise to the same solution via a different path, resulting in different percent contribution values. If there are highly cor - related environmental variables, the percent contributions should be interpreted with cau- tion. The permutation importance measure depends only on the final Maxent model, not the path used to obtain it. The contribution for each variable is determined by randomly permuting the values of that variable among the training points (both presence and back- ground) and measuring the resulting decrease in training AUC. A large decrease indicates that the model depends heavily on that variable. Hence, permutation importance appears to be a better measure of a variable’s explanatory power—since it is path—(algorithm-) inde- pendent. Modelling performance was evaluated using the regularized training gain, which describes how much better the Maxent distribution fits the presence data compared to a uniform distribution (Phillips and Dudic 2008). A jackknife test was also run to obtain alternate estimates of variable importance. Each variable was excluded in turn, and a model was created with the remaining variables. The model was then created using each environmental variable in isolation. In addition, a model was created using all variables. For the variables with the highest predictive val- ues, response curves show how each of these environmental variables affects the Maxent predictions (Phillips and Dudík 2008). The curves illustrate how the logistic prediction changes as each environmental variable is varied, while keeping all other environmental variables at their average sample value. The curves thus represent the marginal effect of changing any single variable alone. 1 3 1182 Biodiversity and Conservation (2019) 28:1173–1204 Hotspot maps First, we produced projected distribution maps for individual species at a spatial resolu- tion of 25  ha. The continuous Maxent output maps were reclassified into binary maps of suitable (1) and unsuitable (0), using the averaged species-specific logistic thresh- old value that “maximises training sensitivity plus specificity” (Liu et  al. 2013). This threshold selection method has been shown to perform rather well with presence-only data (Liu et  al. 2005, 2013), and is suitable for this study considering the goal is to predict where current suitable habitats are located. Choosing a relatively high threshold reduces the risk of choosing unsuitable sites by identifying only those areas with the highest suitability (Pearce and Ferrier 2000). Next, to create richness maps, we combined the binary maps representing suitable habitats for individual species and used a simple summation of the predicted suitabili- ties using the Raster Calculator feature in ArcGIS v10.2 for each species group sepa- rately, and also for all 48 species. The reason for doing so was that this allowed us to investigate whether certain species groups are more intimately related to certain envi- ronmental predictors than other groups. Spruce swamp forest species were excluded from the species group analysis, as species richness and hotspot based on only three species is not particularly informative. However, they were included in the analyses of total species richness and summary hotspot based on individual species. We then identified richness hotspots as the top 5% of grid squares ranked by spe- cies richness in each species groups (see Prendergast et al. 1993; Williams et al. 1996). Finally, summary hotspot map was produced by stacking the hotspot maps from all indi- vidual species. Results For all models, the AUC was excellent for the training data (mean AUC 0.924, ranging from 0.841 to 0.989) and good for the evaluation data (mean AUC 0.855, ranging from 0.607 to 0.959) (Table  3). The highest test AUC values were obtained with calcareous species (mean 0.88) and the lowest with decaying wood species (mean 0.80), perhaps because decaying wood environments exist extensively outside peatland habitats. On average, the proportions of drained peatland area, open peatland and undrained peatland area had the highest permutation importance (15.2, 14.7, and 10.2%, respec- tively) across the modelled species groups (Table  4). When considering importance between species groups, proportions of undrained (mainly positive responses) and drained peatland area (negative responses) showed the greatest impact (18.0% and 18.3%) followed by the proportion of open peatland with positive association (8.8%) in rich fen species models (Tables 4, 5). In calcareous species models, the proportion of drained peatland area was the most influential variable with positive response (13.6%), followed by proportion of calcare- ous rock (8.9%, mainly positive responses), and volume of pine (8.9%, mainly negative responses). Increasing proportions of undrained peatland (13.5%), growing degree days (13.5%), and proportion of open peatlands (12.8%) contributed most and positively to mesotrophic fen species models. 1 3 Biodiversity and Conservation (2019) 28:1173–1204 1183 Table 3 Performance of the models applied to individual threatened plant species distribution, as assessed by the AUC values from the training (Training AUC) and evaluation data (Test AUC) and the stability of the models (Standard Deviation) Species Training AUC Test AUC AUC standard deviation Rich fen  Carex heleonastes 0.92 0.90 0.02  Dactylorhiza incarnata subsp. cruenta 0.93 0.89 0.02  Dactylorhiza incarnata subsp. incarnata 0.84 0.84 0.01  Hamatocaulis vernicosus 0.92 0.91 0.01  Leiocolea bantriensis 0.94 0.82 0.09  Lophozia grandiretis 0.85 0.69 0.14  Meesia longiseta 0.97 0.94 0.03  Moerckia hibernica 0.94 0.87 0.04  Riccardia multifida 0.97 0.96 0.02  Sphagnum contortum 0.90 0.77 0.08 0.92 0.86 0.05 Mesotrophic fen  Carex laxa 0.88 0.81 0.04  Epilobium laestadii 0.94 0.88 0.05  Hamatocaulis lapponicus 0.98 0.89 0.08  Hammarbya paludosa 0.92 0.89 0.02  Lycopodiella inundata 0.86 0.69 0.12  Rhynchospora fusca 0.95 0.94 0.01 0.92 0.85 0.05 Calcareous  Amblyodon dealbatus 0.97 0.96 0.03  Botrychium virginianum 0.95 0.88 0.07  Bryum pseudotriquetrum var. neodamense 0.94 0.91 0.03  Calypso bulbosa 0.94 0.93 0.01  Campyliadelphus elodes 0.95 0.86 0.05  Carex appropinquata 0.92 0.87 0.03  Carex viridula var. bergrothii 0.93 0.88 0.04  Cypripedium calceolus 0.90 0.89 0.01  Dactylorhiza fuchsii 0.91 0.80 0.05  Dactylorhiza lapponica 0.97 0.95 0.02  Dactylorhiza traunsteineri 0.85 0.81 0.02  Dicranum acutifolium 0.95 0.91 0.02  Eriophorum brachyantherum 0.91 0.87 0.04  Malaxis monophyllos 0.99 0.88 0.08  Palustriella commutata 0.95 0.91 0.03  Palustriella decipiens 0.91 0.87 0.03  Palustriella falcata 0.92 0.89 0.02  Philonotis calcarea 0.95 0.90 0.06  Pseudocalliergon angustifolium 0.95 0.87 0.10  Pseudocalliergon lycopodioides 0.96 0.84 0.11 1 3 1184 Biodiversity and Conservation (2019) 28:1173–1204 Table 3 (continued) Species Training AUC Test AUC AUC standard deviation  Saxifraga hirculus 0.90 0.89 0.01  Schoenus ferrugineus 0.94 0.86 0.07 0.93 0.88 0.04 Spruce swamp forest  Carex atherodes 0.93 0.84 0.06  Epipogium aphyllum 0.86 0.77 0.05  Poa remota 0.95 0.81 0.08 0.92 0.81 0.06 Decaying wood  Anastrophyllum hellerianum 0.89 0.83 0.03  Calypogeia suecica 0.96 0.81 0.08  Jungermannia leiantha 0.87 0.73 0.10  Lophozia ascendens 0.94 0.88 0.04  Lophozia ciliata 0.92 0.82 0.11  Lophozia longiflora 0.87 0.66 0.10  Riccardia palmata 0.90 0.85 0.06 0.91 0.80 0.07 All species on average 0.924 0.855 0.050 Spruce swamp forest species were mainly negatively associated with proportion of open peatlands (36.4%), drained peatland area (13.0%), and volume of spruce (6.7%). In decaying wood species, the proportion of drained peatland area (19.0%, mainly negative associations), water balance (12.5%), and topographical wetness index (11.5%) showed the greatest impact. Looking to the jackknife evaluations across all modelled species, we found that the proportion of undrained peatland, topographical wetness index, and volume of spruce were the three most effective predictors when used individually (Table  6). In addition, proportions of calcareous rock, open peatlands, and drained peatland area decreased the gain most when they were omitted, and therefore contained information that was not present in any other variable (Table 7). However, species groups differed from each other according to their habitat preferences. In mesotrophic fen species models, the pro- portion of undrained peatland area had the highest gain when used in isolation (Table 6) and the largest decrease in gain when omitted (Table 7). These environmental variables contain information that is useful on its own and not present in other variables. Like- wise, the proportion of calcareous rock provided the most useful and unique information on the distribution of calcareous species. In spruce swamp forest species models, the greatest change occurred when volume of spruce was used in isolation. Predictions of threatened plant species richness, based on the summation of sin- gle-species predictions, and hotpots, are shown in Figs.  2 and 3. Suitable habitats for mesotrophic fen and rich fen species were predicted in the western part of the study area, whereas rich fen species had high suitabilities also in northern parts. Eastern and southeastern parts of the study area had high suitability for presence of decaying wood 1 3 Biodiversity and Conservation (2019) 28:1173–1204 1185 Table 4 Relative importance (%) of single environmental variables for predicting the habitat suitability of five species groups based on permutation importance (in Maxent)  Environmental variable Rich fen Mesotrophic fen Calcareous Spruce Decaying wood Mean swamp forest CAL 3.17 0.38 8.87 2.15 0.52 3.02 GDD 7.38 13.51 8.36 2.42 2.40 6.81 KP1 3.29 4.98 7.26 4.27 7.03 5.37 KP2 0.77 3.56 2.76 1.64 5.74 2.89 KP3 2.93 4.74 1.83 4.79 2.10 3.28 KP6 3.88 2.93 2.67 3.50 5.65 3.73 BIRCH 4.81 5.32 4.76 3.34 3.91 4.42 SPRUCE 3.28 9.71 4.84 6.69 7.03 6.31 SPRINGS 2.70 1.17 1.86 2.15 1.08 1.79 PINE 5.59 3.85 8.89 5.76 4.82 5.78 OTHER 5.09 0.94 3.74 2.18 2.01 2.79 SPRUCE SWAMP FOR- 1.75 4.28 4.81 1.29 0.60 2.55 EST OPEN MIRE 8.82 12.81 8.15 36.40 7.06 14.65 UNDRAINED 18.04 13.49 8.20 4.31 7.19 10.24 DRAINED 18.33 11.99 13.59 12.98 18.96 15.17 TWI 2.23 4.20 6.86 3.96 11.45 5.74 WAB 7.95 2.15 2.56 2.18 12.47 5.46 Total 100 100 100 100 100 100 Three most important variables for each species group and on average are in bold. Abbreviations are explained in Table 2 species. Suitable habitats for calcareous species were, not surprisingly, mainly concen- trated in the areas with much calcareous rock. Discussion Regional-scale biodiversity patterns, i.e. spatial resolutions ranging from 0.5 to 2 km, are an important component of the diversity that occurs in a landscape or region, and also represent the scale at which land use decisions are often made. Sustainable planning of peatland use requires an analysis of potential ecological resources and the effects of their utilization on an area. Without knowledge of the biodiversity values of peatlands, their unsustainable utilization continues to degrade their biodiversity and threaten the remain- ing valuable habitats and species. Predictive habitat suitability modelling may consider- ably increase the efficiency of biodiversity mapping schemes and incorporate un-surveyed regions into decision-making. Using models to predict potential species distributions is also likely to become increasingly important as environmental change and other dynamic processes are incorporated into land use planning efforts (Rondinini et  al. 2006; Under - wood et al. 2010). The aim of this study was not to reflect the full reality, but to construct and evaluate simple and ecologically significant habitat suitability models that approximate 1 3 1186 Biodiversity and Conservation (2019) 28:1173–1204 1 3 Table 5 Summary of the shape of the response curves between modelled plant species (n = 48) and each environmental variable cal gdd kp1 kp2 kp3 kp6 birch spruce springs pine deciduous Spruce open mire undrained drained twi wab swamp forest Rich fen  Carex heleonastes + − Ω Ω + Ω U − N − − + + + − + −  Dactylorhiza incarnata subsp. + Ω Ω Ω + Ω + − N − + Ω + + − + − cruenta  Dactylorhiza incarnata subsp. + − Ω Ω + Ω − − N − − − Ω + − + − incarnata  Hamatocaulis vernicosus + − Ω Ω + Ω U − N − − Ω + + − + −  Leiocolea bantriensis + − + − − − − + N Ω + − − Ω − − +  Lophozia grandiretis + + Ω − − − U − N − − − Ω + − − −  Meesia longiseta − − Ω Ω + Ω − − N − − − + + − + −  Moerckia hibernica + − Ω Ω + Ω − − N − − + + + − + −  Riccardia multifida + + Ω − − − U − N − − − − + − + − Sphagnum contortum + − Ω Ω + Ω U − N − − Ω + + − + − Mesotrophic fen  Carex laxa − − Ω Ω + + U − N − − − Ω + − + −  Epilobium laestadii + − Ω Ω − Ω − + N − − + − + − − −  Hamatocaulis lapponicus − N + + + + Ω Ω N Ω − − + + − + −  Hammarbya paludosa + + Ω + + + U − N U − − Ω + − + −  Lycopodiella inundata − + − − − + − − N − − − + − + + N  Rhynchospora fusca − + − + + Ω − − N − − − + + − + − Calcareous  Amblyodon dealbatus + − Ω + − + − − N − − + − N − N −  Botrychium virginianum + + − − − − − Ω N − − − − − − − −  Bryum pseudotriquetrum var. neo- + − Ω Ω + + − − N − − + + + − + − damense  Calypso bulbosa + Ω Ω − − − Ω Ω N Ω Ω + − Ω − − Ω Biodiversity and Conservation (2019) 28:1173–1204 1187 1 3 Table 5 (continued) cal gdd kp1 kp2 kp3 kp6 birch spruce springs pine deciduous Spruce open mire undrained drained twi wab swamp forest  Campyliadelphus elodes + − Ω − − − − − N − + − − − − − +  Carex appropinquata + Ω Ω Ω Ω Ω − − N − − Ω Ω + − + −  Carex viridula var. bergrothii + − Ω Ω Ω + − − N − − + Ω + − + +  Cypripedium calceolus + Ω Ω Ω − − Ω Ω N Ω Ω + − Ω − − Ω  Dactylorhiza fuchsii + − + Ω − + + + N − − + − + − − +  Dactylorhiza lapponica + − Ω Ω + Ω − − N − − Ω Ω + − + Ω  Dactylorhiza traunsteineri + − Ω Ω Ω Ω U − N − − − Ω + − + +  Dicranum acutifolium N − − − − − − N N − − − − − − − N  Eriophorum brachyantherum + − Ω − − − − Ω N − − − − + − − −  Malaxis monophyllos + + Ω Ω + + − + N − − + − + − + −  Palustriella commutata + − − − − − − − N − − − − − − − −  Palustriella decipiens + − Ω Ω + + − + N − − + − − − − +  Palustriella falcata + Ω Ω Ω − Ω − Ω N Ω − + − Ω − − +  Philonotis calcarea + − Ω − − − − + N − − − − + − − +  Pseudocalliergon angustifolium + − N − − − − + N − − − − − − − −  Pseudocalliergon lycopodioides + − N − − − − + N − − − − N − − −  Saxifraga hirculus + − Ω Ω Ω Ω − − N − − Ω Ω + − + −  Schoenus ferrugineus + − + − − − − − N − − − − N − − − Spruce swamp forest  Carex atherodes + + Ω Ω + − Ω + N − − + − − + − +  Epipogium aphyllum + + − − − − + + N + Ω Ω − − − − +  Poa remota − + − − − − + + N − + + − − − − + Decaying wood sp.  Anastrophyllum hellerianum + − + − − − − + N Ω + − − Ω − − + 1188 Biodiversity and Conservation (2019) 28:1173–1204 1 3 Table 5 (continued) cal gdd kp1 kp2 kp3 kp6 birch spruce springs pine deciduous Spruce open mire undrained drained twi wab swamp forest  Calypogeia suecica − N − − − − N + N + − − − − − − +  Jungermannia leiantha − + − − − − − + N + + − − + − − +  Lophozia ascendens − + − − − − N + N + − − − + − − +  Lophozia ciliata − N − − − − − + N N − − − + − − +  Lophozia longiflora N N − − − − + + N − + − − + − − N  Riccardia palmata − − − − − − − + N − − − − + − − + The direction of the effect indicated with the symbols (+  =  positive linear correlate, = negative linear correlate, Ω = non-linear correlate with a humped response curve, U = non-linear correlate with downward humped response curve; N = no trend). Abbreviations are explained in Table 2 Biodiversity and Conservation (2019) 28:1173–1204 1189 Table 6 Results of jackknife evaluations of relative importance of environmental variables when used in isolation, with only the corresponding feature Rich fen Mesotrophic fen Calcareous Spruce Decaying wood Mean swamp forest CAL 0.14 0.01 0.48 0.17 0.00 0.16 GDD 0.14 0.18 0.23 0.02 0.01 0.12 KP1 0.19 0.08 0.12 0.01 0.03 0.09 KP2 0.12 0.04 0.11 0.02 0.09 0.08 KP3 0.29 0.12 0.11 0.04 0.04 0.12 KP6 0.09 0.22 0.07 0.01 0.04 0.09 BIRCH 0.16 0.29 0.09 0.15 0.01 0.14 SPRUCE 0.13 0.44 0.06 0.21 0.09 0.19 SPRINGS 0.16 0.02 0.13 0.08 0.02 0.08 PINE 0.32 0.30 0.21 0.01 0.01 0.17 DECIDUOUS 0.08 0.15 0.05 0.04 0.01 0.06 SPRUCE 0.03 0.07 0.07 0.13 0.02 0.06 SWAMP FOR- EST OPEN MIRE 0.28 0.27 0.09 0.20 0.06 0.18 UNDRAINED 0.59 0.64 0.20 0.02 0.01 0.29 DRAINED 0.23 0.11 0.20 0.07 0.19 0.16 TWI 0.22 0.33 0.20 0.14 0.36 0.25 WAB 0.16 0.09 0.05 0.02 0.27 0.12 The most important variable for each species group and on average is in bold. Abbreviations are explained in Table 2 this reality and constitute useful tools for land use planning. By targeting on the richness of species of valuable environments also provide a new approach to focused biodiversity modelling. Modelling performance: uncertainty issues Although the predictive performance of the models was rather high across the species, it is important to be aware that the ecological significance of the observed relationships between environmental variables and species occurrences are not always obvious. In this study, species richness is simply predicted by stacking presence–absence predictions of all species. This method hence relies on our ability to model the distributions of individual species, a field that has greatly matured over the last two decades (see Guisan and Thuiller 2005; Elith and Leathwick 2009; Franklin 2010). Thus, the main factors are those control- ling individual species distributions, and often purely abiotic variables are used. One of the main caveats in using individual species models to generate species richness patterns is that it tends to overestimate actual species richness (i.e., commission errors; Algar et al. 2009; Trotta-Moreu and Lobo 2010; Guisan and Rahbek 2010). The individual species models created in this study do not include all environmental, ecological (particu- larly competition), and historical factors that affect species distributions. By solely using environmental variables at rather coarse resolution to construct predictions of a species’ 1 3 1190 Biodiversity and Conservation (2019) 28:1173–1204 Table 7 Results of jackknife evaluations of relative importance of environmental variables when used in isolation, without the corresponding feature Rich fen Mesotrophic fen Calcareous Spruce Decaying wood Mean swamp forest CAL 1.30 1.45 1.19 0.94 0.93 1.16 GDD 1.37 1.37 1.47 1.08 0.93 1.24 KP1 1.37 1.40 1.47 1.05 0.93 1.24 KP2 1.40 1.43 1.52 1.08 0.91 1.27 KP3 1.38 1.42 1.52 1.06 0.93 1.26 KP6 1.39 1.39 1.51 1.05 0.93 1.25 BIRCH 1.40 1.44 1.52 1.07 0.92 1.27 SPRUCE 1.39 1.43 1.50 1.02 0.88 1.24 SPRINGS 1.30 1.44 1.47 1.04 0.93 1.24 PINE 1.38 1.42 1.49 1.02 0.91 1.24 DECIDUOUS 1.40 1.46 1.52 1.05 0.92 1.27 SPRUCE 1.40 1.45 1.51 1.08 0.94 1.28 SWAMP FOR- EST OPEN MIRE 1.37 1.38 1.50 0.95 0.89 1.22 UNDRAINED 1.36 1.35 1.51 1.06 0.89 1.24 DRAINED 1.34 1.41 1.48 1.00 0.87 1.22 TWI 1.40 1.43 1.47 1.06 0.86 1.24 WAB 1.33 1.43 1.51 1.07 0.80 1.23 The most important variable, the exclusion of which decreased the gain most, is in bold. Abbreviations are explained in Table 2 suitable habitat, models fail to incorporate biological, geographical or historical influences on species distributions (e.g., Guisan and Thuiller 2005; Heikkinen et  al. 2006). This, in turn, can lead to an overestimation of species suitable habitats, as only areas of suitable habitats, not their realized distributions, are projected. This kind of overestimation may increase when combining multiple individual models to create species richness maps, as was the case in this study. In the light of these facts, we propose that the habitat suitability models influence on potential over-prediction is the result of the inherent nature of habitat suitability models. Moreover, response curves are just simplifications of reality, and their shape may be strongly dependent on the setting of the study and the variable selection criteria used. First, a local model is fit to a particular region of the geographical space, but the model can dif- fer in different regions of the sample space. Certain abiotic factors, such as topography and land cover, may be important locally, but they generally can be applied only within a lim- ited geographical extent (Thuiller et al. 2003). Thus, conclusions about the response curve of species may only be made within the context of the study area. Second, it is possible that species may respond to a combination of a different set of variables in different parts of its distributional range, as the shape of responses in a multivariate model may depend on the nature of the correlations between the indirect variable and the causal gradients (Franklin 1995; Guisan and Zimmermann 2000). The use of different geographical extents and spa- tial resolutions could provide contradictory answers to the same ecological question. 1 3 Biodiversity and Conservation (2019) 28:1173–1204 1191 Fig. 2 Spatial predictions of species richness by groups for the whole study area: a mesotrophic fen species, b rich fen species, c calcareous species and d decaying wood species It should also be kept in mind that an area of suitable habitat is not occupied by a species if the species is unable to disperse there (Pulliam 2000; Newbold 2010). Dis- persal limitation (Kadmon and Shmida 1990), source-sink dynamics (Pulliam and Dan- ielson 1991) and metapopulation dynamics (Hanski 2005) will result in spatial patterns in species distributions that are at least partly independent of the environment. These 1 3 1192 Biodiversity and Conservation (2019) 28:1173–1204 Fig. 2 (continued) spatial patterns are referred to as “endogenous spatial autocorrelation” (Legendre 1993). Several papers have discussed the importance of measuring spatial autocorrelation when evaluating the importance of different factors to explain species distributions (e.g. Dor - mann et  al. 2007; Hawkins et  al. 2007). However, as the 25 ha grid cells in the model setup were distributed rather sparsely across the whole study area we assumed that the effect of spatial autocorrelation was rather small. Moreover, Parviainen et  al. (2008) 1 3 Biodiversity and Conservation (2019) 28:1173–1204 1193 Fig. 2 (continued) carried out autocorrelation assessment in a similar environment with a similar grid- based approach at the same 25-ha resolution. They found that inclusion of the effect of spatial auto-correlation as autocovariate term reflecting the species occurrences in the surroundings of the focal grid cell, had only a minor effect on the importance of the environmental variables and the shapes of predictor-response curves. 1 3 1194 Biodiversity and Conservation (2019) 28:1173–1204 Fig. 2 (continued) The observed distribution of threatened species is also affected by historical facts that may have restricted current distribution patterns of species (Guisan and Thuiller 2005; Svenning et al. 2008). This may, at least partly, explain the true absence of species in many areas where the environmental conditions are apparently suitable. Moreover, populations of threatened plant species may be extremely small and thus prone to local extinctions aris- ing from stochastic processes in areas with appropriate environmental conditions. In light 1 3 Biodiversity and Conservation (2019) 28:1173–1204 1195 Fig. 3 Spatial predictions of threatened plant species hotspots: by a species groups, and b total species rich- ness (n = 48 species) 1 3 1196 Biodiversity and Conservation (2019) 28:1173–1204 Fig. 3 (continued) 1 3 Biodiversity and Conservation (2019) 28:1173–1204 1197 of this fact, some plant species may actually have had populations in earlier periods of sig- nificantly better availability of suitable habitat, and current land use may have no relevance for their potential occurrence (Lindborg and Eriksson 2004; Helm et al. 2006; Wisz et al. 2007). Thus, the individual habitat suitability modelling approach is limited because with- out adding a dispersal filter, it may incorrectly predict species in areas that appear environ- mentally suitable but that are outside their colonizable or historical range. Threshold selection is one of the many possible biases in habitat suitability modelling. As suggested by Trotta-Moreu and Lobo (2010) and Mateo et al. (2012), the selection of an appropriate suitability threshold can reduce over-prediction in species models. However, selection is not straightforward and the results can vary, sometimes dramatically, depend- ing on the threshold chosen (Milanovicha et al. 2012; Liu et al. 2013). We chose to use the rather conservative maximum training sensitivity plus specificity threshold, as we found it to be a promising selection method for presence-only data. An interesting methodologi- cal line of future research would thus be to study the reliability of different thresholding approaches in modelling, as it may help to reduce over-predictions, at least in some cases. An additional problem in the selection of reliable and stable threshold values is the lack of real absences, as in the present study. When the modelling algorithm has no information on true absences, even small differences in the selected threshold value can have a substantial effect on the model outputs (see Jiménez-Valverde and Lobo 2007). Finally, the lack of floristic data from remote areas may possibly lead to a partial bias in the modelling analyses. The accuracy of the model increases with increased amounts and accuracy of presence and absence data, and may be updated to include new information to further refine distribution predictions (Elith and Leathwick 2009), but we assume that the main drivers of habitat suitability (which were predominantly ecologically plausible) will remain. Model transferability is one important feature in habitat suitability models and thus, developing models that are able to provide reliable predictions of species distributions in new areas or other times is a major challenge. As the results of this study revealed, plant distributions are often critically affected by certain local factors, such as soil or habitat properties or the occurrence of favourable microsites and microclimates (see also Parvi- ainen et  al. 2008; Elith and Leathwick 2009). Our models were generated for the boreal aapa mire landscape, where a relatively high proportion of the peatland landscape has remained in a semi-natural state despite intensive draining. On the other hand, the effect of draining is not limited to the drained peatlands only, but may extend over larger areas within the catchment (Holden et al. 2006). The models used in this study took into account only the local drainage effects within the grid cells. From the ecological viewpoint, our models may not be directly applicable to regions of highly fragmented, intensively used or cultural landscape typical of, for example, western and southern Europe. However, from the technical viewpoint, our approach is applicable over different ecosystems and habitats. Importance of environmental variables to plant species The distribution of plant species is limited by the availability of suitable habitats. For rare plants, especially those with limited geographic ranges, narrow habitat specificity can fur - ther limit distribution. While climate is an important driver of plant species distribution at the continental scale, soil properties and biotic interactions determine habitat avail- ability at smaller scales (Pearson et  al. 2004). Furthermore, competition may also affect species occurrence patterns and persistence capability (Virtanen et  al. 2010). Variations 1 3 1198 Biodiversity and Conservation (2019) 28:1173–1204 in peatland vegetation are the result of many environmental factors at the landscape and local scales, including the origins of the water that feeds the peatland, acidity levels (pH), the availability of main nutrients (nitrogen and phosphorus), the water table level, and the depth of the peat. Seasonal variations in moisture levels have also been found to be related to the composition of vegetation communities (Laitinen 2008). In this study, species responded differently to the analyzed habitat gradients. A mix- ture of unimodal and linear responses was typical of the gradients. As a general observa- tion, with most of the species the importance of variables reflecting local-scale variation in the habitat and land cover was superior to climate variables operating at higher scales. Responses to growing degree days varied according to the geographic distribution of the species; for example, Eriophorum brachyantherum is a northern species, for which suitable habitats occur at low numbers of growing degree days. Our model confirms the importance of particular environmental variables that influence the presence and quality of peatland habitat for the selected threatened plant species. In mesotrophic fen species models, a high amount of undrained open peatlands was the most powerful characteristic forcing distribution patterns of threatened species. Wetness, high variation in site fertility types, and microtopography are distinct characteristics for und- rained peatlands. The wettest peatlands have not been used for intensive forestry and agri- culture, and they therefore continue to be suitable habitats for peatland species. At drained sites, key hydrological characteristics have changed, which has led to the degradation of peatland vegetation (Similä et  al. 2014). Based on our findings, species growing on wet surfaces are most susceptible to the effects of changes in the water table. These include Carex heleonastes, Dactylorhiza incarnata ssp. incarnata, Hamatocaulis vernicosus, Mee- sia longiseta, Carex laxa, Hamatocaulis lapponicus, Dactylorhiza lapponica, Dactylorhiza traunsteineri, and Saxifraga hirculus. Epilobium laestadii is a demanding northern species that is most closely associated with nutrient-rich fens with a sparse and low field layer, and often occurs around springs or in seepage areas. Rhynchospora fusca is a sedge species characteristic of open flark fens. Amblyodon dealbatus, Cypripedium calceolus, and Dactylorhiza traunsteineri are cal- cium-demanding species of nutrient-rich, calcareous fens. As expected, the importance of calcareous bedrock was explicit to species demanding calcium, e.g. Amblyodon dealbatus, Malaxis monophyllos, and Pseudocalliergon lycopodioides. However, suitable habitats for calcareous species were also found in areas where there was not much calcareous rock. This kind of environments may contain for example small outcroppings, springs associated with distant calcareous deposits, or superficial calcareous deposits such as shell-rich sands. For example, Amblyodon dealbatus, Malaxis monophyllos, and Pseudocalliergon lycopodi- oides grow mainly in herb-rich fens, calcareous springs and wet calcareous rocky outcrops (Ulvinen 2001). The results of this study also revealed that numerous threatened species, such as Philonotis calcarea, Palustriella decipiens, and P. falcata, also occur most com- monly in springs which have a neutral pH. Sphagnum contortum is a rich fen species that exists in a rather restricted region within the study area, and most of the variation can be explained by habitat, particularly by the increasing proportion of undrained mires. How- ever, S. contortum does not occur throughout its potential habitat space because it performs best in nutrient- and calcareous-rich habitats and the presence of springs. Consequently, successful models require also other explanatory factors than undrained peatland alone. The models of decaying wood species performed more weakly than those of other spe- cies groups. This may, at least partly, be explained by the fact that the models did not con- tain dead wood as an explanatory variable. Many decaying wood species are dependent on old-growth forest habitats with high amounts of decaying wood (Rassi et al. 2010). We 1 3 Biodiversity and Conservation (2019) 28:1173–1204 1199 can therefore expect that old spruce swamp forests are also important habitats for these species, although this could not be directly seen in the models. Nevertheless, there was a high contribution of spruce and pine in the models. Interestingly, topographical wetness index, with mainly negative association, was important in determining suitable habitats for decaying wood species. This kind of topographical variable can serve as proxies for other environmental variables, such as soil properties and plant-available water, which may drive plant distributions (Lassueur et al. 2006). The species associated with decaying wood require a continuing presence of deadwood at various stages of decay, as well as evenly moist microclimates and shady growth sites (Laaka-Lindberg et al. 2009). Despite favoring moisture, these species do not tolerate wet conditions, which prevail at low elevated sites where water accumulates. Richness patterns and hotpots of species groups The predicted species richness maps and the location of the most species-rich hotspots indicated important differences between species groups. High potential species richness occurred for rich fen species in northern parts of the study area, for mesotrophic fen spe- cies in southwestern parts, and for decaying wood species broadly throughout the central- southeastern part of the study area. These differences reflect the differing habitat require- ments among the species groups. However, part of the differences may also arise from unbalanced species numbers within each group: mesotrophic fen (6), decaying wood (7), rich fen (19), and calcareous species (23). Reliable identification of hotspot areas with a high number of potentially suitable habi- tats for threatened species has a central role in land use and conservation planning. The grouped species approach in the landscape makes the identification of potentially high- quality habitats for rare species more reliable and the argument for sympathetic manage- ment of these habitats more compelling. The species richness analysis indicates that man- agement efforts, such as restoration, would provide the most benefit in northern parts for all threatened mire species as a whole. One reason may be that the amount of drainage decreases generally towards north (Finnish Forest Research Institute 2014), and the deg- radation of peatland habitats has not proceeded as intensively as in the heavily drained habitats further in the south. It must be noted that the hotspots of threatened species are not the only habitats impor- tant for biodiversity. Complementary approaches are needed, whereby typicalness indicates that abundant habitats and species at the centre of their natural range are also important (Latimer 2009). These typical areas also need to be maintained and actions taken to miti- gate, slow down or prevent their degradation. Our approach can also be used to present peatlands’ biodiversity “non-hotspots”. They may be either areas which are important because they hold characteristic assemblages of peatland species, or drained peatlands where forestry practices have degraded all but these ecologically valuable patches. Such areas are also important within the design of land management planning, before large scale peatland re-use is planned and carried out. Management of specific areas on behalf of one species group may not equally benefit all species within the overall species assemblage, since each species has its own habitat requirements. In contrast, summed richness maps can be readily divided into different sub- categories, enabling land use planners to scrutinize the predictions for species with, for example, different endangerment status or species with different characteristics such as vascular plants and bryophytes. Species groups can be adaptively used to address the needs 1 3 1200 Biodiversity and Conservation (2019) 28:1173–1204 of both individual species and groups of species by first setting management targets for a group, then testing the benefits of that management for individual species, and thereafter adjusting management direction to best benefit all species of concern (Wisdom et al. 2001; Wiens et al. 2008). Conclusions Our results demonstrate that the combination of individual species models offers a useful tool for identifying landscape-scale richness patterns for threatened mire plant species. In conclusion, habitat suitability models can help in determining which aspects of the envi- ronment of a given species have a critical impact on its distribution, and thus advance our understanding of the ecological requirements of species, while also providing valuable information concerning where species are likely to be found in insufficiently surveyed land- scapes. The modelling performance was high across the modelled species, and the rich- ness patterns generated by single models coincide with the expected richness pattern based on expert knowledge. These generated richness patterns therefore offer a powerful tool for basic biodiversity applications (e.g., land use planning and conservation). Predictive habitat suitability models and the summed richness maps can provide a valuable means of delimiting potentially valuable geographic areas and focus survey and management efforts onto valuable geographic areas and focus such efforts towards ensuring the preservation of biological diversity in aapa mire landscapes. Thus, when examining larger landscape sites suitable for different land use, the models created in this study offer considerable scope for use as “first filters” for identifying potential locations of hotspots of threatened species in boreal peatland landscapes at the regional scale. It is, however, important to emphasize that areas should not be valued simply on the basis of model predictions of threatened species. Typical species and habitats are also important for the biodiversity. It is also vital that mod- elled valuations should be subject to ground-truth assessment before planning decision- making takes place. Acknowledgements Open access funding provided by Natural Resources Institute Finland (LUKE). A study of this nature would not have been possible without the hundreds of volunteers who contributed their data to the red-listed plant species database. We are thankful for two anonymous referees whose comments and suggestions greatly improved the manuscript. The study is part of the EU LIFE + project LIFEPeat- LandUse (LIFE12 ENV/FI/000150). We also thank the Maj and Tor Nessling Foundation for providing a personal scholarship to M. Parkkari. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 Interna- tional License (http://creat iveco mmons .org/licen ses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. References Ahti T, Hämet-Ahti L, Jalas J (1968) Vegetation zones and their sections in northwestern Europe. Ann Bot Fennici 5:169–211 Algar AC, Kharouba HM, Young ER, Kerr JT (2009) Predicting the future of species diversity: macroeco- logical theory, climate change, and direct tests of alternative forecasting methods. Ecography 32:22–33 Barbet-Massin M, Jiguet F, Albert CH, Thuiller W (2012) Selecting pseudo-absences for species distribu- tion models: how, where and how many? Methods Ecol Evol 3:327–338 1 3 Biodiversity and Conservation (2019) 28:1173–1204 1201 Bolliger J, Kienast F, Soliva R, Rutherford G (2007) Spatial sensitivity of species habitat patterns to sce- narios of land use change (Switzerland). Landsc Ecol 22:773–789 Boyd R, Bjerkgård T, Nordahl B, Schiellerup H (eds.) (2016) Mineral resources in the Arctic. Geological Survey of Norway, Special Publication, p 483 Burrough PA, McDonnell RA (1998) Principles of geographical information systems. Spatial information systems. Oxford University Press, New York Dormann CF, McPherson JM, Araújo MB, Bivand R, Bolliger J, Carl G, Davies RG, Hirzel A, Jetz W, Kissling DW, Kühn I, Ohlemüller R, Peres-Neto PR, Reineking B, Schröder B, Schurr FM, Wilson R (2007) Methods to account for spatial autocorrelation in the analysis of species distributional data: a review. Ecography 30:609–628 Elith J, Leathwick J (2009) Conservation prioritization using species distribution models. In: Moilanen A, Wilson KA, Possingham HP (eds) Spatial conservation prioritization: quantitative methods and com- putational tools. Oxford University Press, Oxford, pp 70–93 Elith J, Graham C, Anderson R, Dudík M, Ferrier S, Guisan A, Hijmans R, Huettmann F, Leathwick J, Lehmann A, Li J, Lohmann L, Loiselle B, Manion G, Moritz C, Nakamura M, Nakazawa Y, Overton J, Peterson AT, Phillips S, Richardson K, Scachetti-Pereira R, Schapire R, Soberón J, Williams S, Wisz M, Zimmermann N (2006) Novel methods improve prediction of species’ distributions from occur- rence data. Ecography 29:129–151 ESRI (1991) ARC/INFO user’s guide. Cell-based modelling with GRID. Analysis, display and manage- ment. Environment Systems Research Institute, Inc., Redlands Fielding A, Bell J (1997) A review of methods for the assessment of prediction errors in conservation pres- ence/absence models. Environ conserv 24:38–49 Finnish Environmental Institute (2009) Finnish environmental institute spatial drainage stage data on peatlands Finnish Forest Research Institute (2014) Finnish statistical yearbook of forestry 2014. Finnish Forest Research Institute, Vantaa Franklin J (1995) Predictive vegetation mapping: geographic modeling of biospatial patterns in relation to environmental gradients. Prog Phys Geogr 19:474–499 Franklin J (2010) Mapping species distributions: spatial inference and prediction. Cambridge Univ, Cam- bridge, UK Freeman LA, Kleypas JA, Miller AJ (2013) Coral reef habitat response to climate change scenarios. PLoS ONE 8:1–14 Gaston KJ (1994) Rarity. Chapman & Hall, London, p 205 Gessler PE, Chadwick OA, Chamran F, Althouse L, Holmes K (2000) Modeling soil-landscape and ecosys- tem properties using terrain attributes. Soil Sci Soc Am J 64:2046–2056 Guisan A, Rahbek C (2010) Predicting spatio-temporal patterns of species assemblages through integration of macroecological and species distribution models with assembly rules and source pool assignments. J Biogeogr 38:1433–1444 Guisan A, Thuiller W (2005) Predicting species distribution: offering more than simple habitat models. Ecol Lett 8:993–1009 Guisan A, Zimmermann NE (2000) Predictive habitat distribution models in ecology. Ecol Model 135:147–186 Hanski I (2005) The shrinking world: ecological consequences of habitat loss. International Ecology Insti- tute, Oldendorf, 307 pp Hawkins BA, Diniz-Filho JAF, Bini LM, De Marco P, Blackburn TM (2007) Red herrings revisited: spatial autocorrelation and parameter estimation in geographical ecology. Ecography 30:375–384 Heikkinen RK, Luoto M, Araujo MB, Virkkala R, Thuiller W, Sykes MT (2006) Methods and uncertainties in bioclimatic envelope modeling under climate change. Prog Phys Geogr 30:751–777 Helm A, Hanski I, Pärtel M (2006) Slow response of plant species richness to habitat loss and fragmenta- tion. Ecol Lett 9:72–77 Hirzel AH, Le Lay G (2008) Habitat suitability modelling and niche theory. J Appl Ecol 45:1372–1381 Hirzel AH, Hausser J, Chessel D, Perrin N (2002) Ecological-niche factor analysis: how to compute habitat- suitability maps without absence data? Ecology 83(7):2027–2036 Holden J, Burt TP, Evans MG, Horton M (2006) Impact of land drainage on peatland hydrology. J Environ Qual 35:1764–1778 Holdridge LR (1967) Life Zone Ecology. Tropical Science Center, San José Iverson LR, Dale ME, Scott CT, Prasad A (1997) A GIS-derived integrated moisture index to predict forest composition and productivity in Ohio forests. Landsc Ecol 12:331–348 Jiménez-Valverde A, Lobo JM (2007) Threshold criteria for conversion of probability of species presence to either-or presence–absence. Acta Ecol 31:361–369 1 3 1202 Biodiversity and Conservation (2019) 28:1173–1204 Joosten H, Clarke D (2002) Wise use of mires and peatlands—background and principles including framework for decision-making. International Mire Conservation Group, International Peat Society, Greifswald, p 304 Kadmon R, Shmida A (1990) Spatiotemporal demographic processes in plant populations: an approach and a case study. Am Nat 135:382–397 Laaka-Lindberg S, Anttila S, ja Syrjänen K (2009) Suomen uhanalaiset sammalet. Suomen ympäristökeskus, Helsinki, Ympäristöopas, p 347 Laitinen J (2008) Vegetational and landscape level responses to water level fluctuations in Finnish, mid- boreal aapa mire – aro wetland environments. Acta Universitatis Ouluensis. A, Scientiae rerum natu- ralium 513 Lassueur T, Joost SP, Randin CF (2006) Very high resolution digital elevation models: do they improve models of plant species distribution? Ecol Model 198:139–153 Latimer W (2009) Assessment of biodiversity at the local scale for environmental impact assessment and land-use planning. Plan Pract Res 24(3):389–408 Legendre P (1993) Spatial autocorrelation: trouble or new paradigm? Ecology 74:1659–1673 Lehmann A, Overton JM, Austin MP (2002) Regression models for spatial prediction: their role for bio- diversity and conservation. Biodivers Conserv 11:2085–2092 Lemes P, Loyola RD (2013) Accommodating species climate-forced dispersal and uncertainties in spa- tial conservation planning. PLoS ONE 8:e54323 Lindborg R, Eriksson O (2004) Historical landscape connectivity affects present plant species diversity. Ecology 85:1840–1845 Liu C, Berry PM, Dawson TP, Pearson RG (2005) Selecting thresholds of occurrence in the prediction of species distributions. Ecography 28:385–393 Liu C, White M, Newell G (2013) Selecting thresholds for the prediction of species occurrence with presence-only data. J Biogeogr 40:778–789 Loiselle BA, Howell CA, Graham CH, Goerck JM, Brooks T, Smith KG, Williams PH (2003) Avoiding pitfalls of using species distribution models in conservation planning. Conserv Biol 17:1591–1600 Lugo AE, Brown SL, Dodson R, Smith TS, Shugart HH (1999) The Holdridge life zones of the conter- minous United States in relation to ecosystem mapping. J Biogeogr 26:1025–1038 Mateo RG, Felicísimo AM, Pottier J, Guisan A, Muñoz J (2012) Do stacked species distribution models reflect altitudinal diversity patterns? PLoS ONE 7:1–9 Mateo RG, Estrella M, Felicísimo ÁM, Muñoz J, Guisan A (2013) A new spin on a compositionalist predictive modeling framework for conservation planning: A tropical case study in Ecuador. Biol Conserv 160:150–161 Merow C, Smith MJ, Silander JA (2013) A practical guide to MaxEnt for modeling species’ distributions: what it does, and why inputs and settings matter. Ecography 36:1058–1069 Milanovicha JR, Petermanb WE, Barrettc K, Hopton ME (2012) Do species distribution models predict species richness in urban and natural green spaces? A case study using amphibians. Landsc Urban Plan 107:409–418 Natural Resources Institute Finland (2013) File service for publicly available data. Natural Resources Institute Finland. http://kartt a.luke.fi/opend ata/valin ta-en.html Newbold T (2010) Applications and limitations of museum data for conservation and ecology, with par- ticular attention to species distribution models. Prog Phys Geogr 34:3–22 Parkkari M, Parviainen M, Ojanen P, Tolvanen A (2017) Spatial modelling provides a novel tool for esti- mating the landscape level distribution of greenhouse gas balances. Ecol Ind 83:380–389 Parviainen M, Luoto M, Ryttäri T, Heikkinen RK (2008) Modeling the occurrence of threatened plant species in taiga landscapes: methodological and ecological perspectives. J Biogeogr 35:1888–1905 Parviainen M, Marmion M, Luoto M, Thuiller W, Heikkinen RK (2009) Using summed individual spe- cies models and state-of-the-art modeling techniques to identify threatened plant species hotspots. Biol Conserv 142:2501–2509 Pearce J, Ferrier S (2000) Evaluating the predictive performance of habitat models developed using logistic regression. Ecol Model 133:225–245 Pearson R, Terence TP, Liu C (2004) Modeling species distributions in Britain: a hierarchical integra- tion of climate and land-cover data. Ecography 27:285–298 Phillips SJ, Dudík M (2008) Modeling of species distributions with Maxent: new extensions and a com- prehensive evaluation. Ecography 31:161–175 Phillips S, Anderson R, Schapire R (2006) Maximum entropy modeling of species geographic distribu- tions. Ecol Model 190:231–259 1 3 Biodiversity and Conservation (2019) 28:1173–1204 1203 Pirinen P, Simola H, Aalto J, Kaukoranta J-P, Karlsson P, Ruuhela R (2012) Climatological statistics of Fin- land 1981-2010. Finnish Meteorological Institute, Reports 2012:1, Finnish Meteorological Institute, p Prendergast JR, Quinn RM, Lawton JH, Eversham BC, Gibbons DW (1993) Rare species, the coincidence of diversity hotspots and conservation strategies. Nature 365:335–337 Pulliam HR (2000) On the relationship between niche and distribution. Ecol Lett 3:349–361 Pulliam HR, Danielson B (1991) Sources, sinks, and habitat selection: a landscape perspective on popula- tion dynamics. Am Nat 137:50–66 Ramsar Convention Secretariat (2013) The Ramsar convention manual: a guide to the convention on wet- lands (Ramsar, Iran, 1971), 6th edn. Ramsar Convention Secretariat, Gland Rassi P, Hyvärinen E, Juslén A, Mannerkoski I (eds) (2010) The 2010 red list of finnish species. Ympäristöministeriö & Suomen ympäristökeskus, Helsinki, p 685 Rondinini C, Wilson KA, Boitani L, Grantham H, Possingham HP (2006) Tradeoffs of different types of species occurrence data for use in systematic conservation planning. Ecol Lett 9:1136–1145 Ryttäri T, Kalliovirta M, Lampinen R (2012) Suomen uhanalaiset kasvit, Tammi, p 384 Similä M, Aapala K, Penttinen J (eds.) (2014) Ecological restoration in drained peatlands—best practices from Finland. Metsähallitus—Natural Heritage Services, Finnish Environment Institute SYKE, p 84 Skov F, Svenning J-C (2004) Potential impact of climate change on the distribution of forest herbs in Europe. Ecography 27:366–380 Spatial Foresight, SWECO, ÖIR, t33, Nordregio, Berman Group, Infyde (2017) Bioeconomy development in EU regions. Mapping of EU member states’/regions’ research and innovation plans and strategies for smart specialisation (RIS3) on bioeconomy for 2014–2020 Svenning JC, Normand S, Kageyama M (2008) Glacial refugia of temperate trees in Europe: insights from species distribution modelling. J Ecol 96:1117–1127 Swets K (1988) Measuring the accuracy of diagnostic system. Science 240:1285–1293 Thuiller W, Araújo MB, Lavorel S (2003) Generalized model vs. classification tree analysis: predicting spa- tial distributions of plant species at different scales. J Veg Sci 14:669–680 Thuiller W, Albert C, Araújo MB, Berry PM, Cabeza M, Guisan A, Hickler T, Midgley GF, Paterson J, Schurr FM, Sykes MT, Zimmermann NE (2008) Predicting global change impacts on plant species’ distributions: future challenges. Persp Plant Ecol, Evol Syst 9:137–152 Trotta-Moreu N, Lobo JM (2010) Deriving the species richness distribution of geotrupinae (Coleop- tera: Scarabaeoidea) in Mexico from the overlap of individual model predictions. Environ Entomol 39:42–49 Ulvinen T (2001) Itämerenvihvilä, valkoyökönlehti ja kenosammal Tervolan letoilla (PeP). Lutukka 17:120–126 Underwood JG, D’Agrosa C, Gerber LR (2010) Identifying conservation areas on the basis of alternative distribution data sets. Conserv Biol 24:162–170 Virtanen R, Luoto M, Rämä T, Mikkola K, Hjort J, Grytnes J-A, Birks HJB (2010) Recent vegetation changes at the high-latitude tree line ecotone are controlled by geomorphological disturbance, produc- tivity and diversity. Glob Ecol Biogeogr 19:810–821 Wiens JJ, Graham CH (2005) Niche conservatism: integrating evolution, ecology, and conservation biology. Annu Rev Ecol Evol Syst 36:519–539 Wiens JA, Hayward GD, Holthausen RS, Wisdom MJ (2008) Using surrogate species and groups for con- servation planning and management. Bioscience 58:241–252 Williams PH, Gibbons DW, Margules CR, Rebelo AG, Humphries CJ, Pressey RL (1996) A comparison of richness hotspots, rarity hotspots and complementary areas for conserving diversity using British birds. Conserv Biol 10:155–174 Wisdom, M., Hayward, G., Shelly, S., Hargis, C., Holthausen, D., Epifanio, J., Parker, L. and Kershner, J. 2001. Using species groups and focal species for assessment of species at risk in forest planning. Flag- staff (AZ), US Department of Agriculture Forest Service, Rocky Mountain Research Station Wisz MS, Walther BA, Rahbek C (2007) Using potential distributions to explore determinants of Western Palaearctic migratory songbird species richness in sub-Saharan Africa. J Biogeogr 34:828–841 Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. 1 3 1204 Biodiversity and Conservation (2019) 28:1173–1204 Affiliations 1 2 2 1 1 Miia Saarimaa  · Kaisu Aapala  · Seppo Tuominen  · Jouni Karhu  · Mari Parkkari  · 1,3 Anne Tolvanen Kaisu Aapala kaisu.aapala@ymparisto.fi Seppo Tuominen seppo.tuominen@ymparisto.fi Jouni Karhu jouni.karhu@luke.fi Mari Parkkari mari.parkkari@luke.fi Anne Tolvanen anne.tolvanen@luke.fi Natural Resources Institute Finland, Oulun Yliopisto, Paavo Havaksen tie 3, 90014 Oulu, Finland Finnish Environment Institute, Latokartanonkaari 11, 00790 Helsinki, Finland Department of Ecology and Genetics, University of Oulu, Oulu, Finland 1 3 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Biodiversity and Conservation Springer Journals

Predicting hotspots for threatened plant species in boreal peatlands

Loading next page...
 
/lp/springer-journals/predicting-hotspots-for-threatened-plant-species-in-boreal-peatlands-Z8yqn6Oa6D

References (73)

Publisher
Springer Journals
Copyright
Copyright © 2019 by The Author(s)
Subject
Life Sciences; Biodiversity; Ecology; Conservation Biology/Ecology; Climate Change/Climate Change Impacts
ISSN
0960-3115
eISSN
1572-9710
DOI
10.1007/s10531-019-01717-8
Publisher site
See Article on Publisher Site

Abstract

Understanding the spatial patterns of species distribution and predicting suitable habitats for threatened species are central themes in land use management and planning. In this study, we examined the geographic distribution of threatened mire plant species and iden- tified their national hotspots, i.e. areas with high amounts of suitable habitats for threat- ened mire plant species. We also determined the main environmental correlates related to the distribution patterns of these species. The specific aims were to: (1) identify the envi- ronmental variables that control the distribution of threatened peatland species in a boreal aapa mire zone, Finland; and (2) to identify the richness patterns and hotspots of threat- ened species. Our results showed that the combination of individual species models offers a useful tool for identifying landscape-scale richness patterns for threatened plant species. The modeling performance was high across the modelled species, and the richness patterns generated by single models coincide with the expected richness pattern based on expert knowledge. The method is therefore a powerful tool for basic biodiversity applications. In cases where reliable models for species occurrences and hotspots can be produced, these models can play a significant role in land-use planning and help managers to meet different conservation challenges. Keywords Threatened mire plant species · Modeling · Boreal peatlands · Habitat suitability Introduction Peatlands are core ecosystems of biological diversity and are known for their wide range of ecosystem services (Ramsar Convention Secretariat 2013). As highly productive eco- systems, they are used increasingly to support economic development and human well- being. Drainage and resource exploitation of wetlands are the main reasons why they are among the most threatened ecosystems in the world. For example the area covered Communicated by Frank Chambers. * Miia Saarimaa miia.saarimaa@luke.fi Extended author information available on the last page of the article 1 3 Vol.:(0123456789) 1174 Biodiversity and Conservation (2019) 28:1173–1204 by peatlands (the most widespread wetland type) has reduced by 10–20% since 1800 (Joosten and Clarke 2002). In Finland, over half of peatlands have been drained for for- estry (Finnish Forest Research Institute 2014), which has caused habitat degradation and increased the number of threatened peatland species. At present, there are 223 red- listed vascular plant and bryophyte species with peatlands as their primary habitats (4.5% of all red-listed species) and 420 red-listed species with peatlands as one of their habitats (Rassi et  al. 2010). The ongoing bioeconomy development (Spatial Foresight, SWECO, ÖIR, t33, Nordregio, Berman Group, Infyde 2017) and high interest in Arctic countries for their mineral resources (Boyd et al. 2016) are increasing pressures on peat- lands. These intense activities are expected to have strong, and mostly negative, impacts on peatland biodiversity. Many of the most adverse effects resulting from peatland use can be avoided through careful planning. This requires an analysis of potential ecological values in an area before intensive and/or large-scale use is planned and carried out. Hotspots, or concentrations of threatened species, are important surrogates of biological diversity that have a significant role in conservation and management strategies (Gaston 1994). Although locations of hot- spots should not be the guiding principles in land use planning, they can be used to avoid disturbing valuable sites with high numbers of rare species (Loiselle et al. 2003; Elith and Leathwick 2009). Predictive species-distribution modeling offers a cost-effective method of exploiting the limited empirical data for the evaluation of biodiversity. Statistics-based spatial models are valuable for generating biogeographical information that can be applied across a broad range of fields, including ecology, land use planning and climate change (e.g. Barbet-Massin et  al. 2012; Bolliger et  al. 2007; Thuiller et  al. 2008). Habitat suit- ability models rely on the concept of niche conservatism (the tendency of species to retain ancestral ecological characteristics) and assume that environmental variables will play an important and consistent role in shaping species distributions (Wiens and Graham 2005). Predictive habitat suitability models of species’ geographical distributions and species richness are increasingly used as an alternative for incomplete or spatially biased survey data as a basis for conservation planning (Hirzel and Le Lay 2008; Elith and Leathwick 2009; Freeman et al. 2013; Lemes and Loyola 2013). A traditional way to develop spatial projections of species richness is to directly meas- ure numbers of species from surveyed sites and to relate this information to environmental variables derived from GIS data. The analysis produces models that yield predictions of species richness for unsampled sites. In this study, species are first modelled individually, and species richness is estimated by stacking individual habitat suitability models (see also Algar et  al. 2009; Parviainen et  al. 2009; Mateo et  al. 2012, 2013). The top 5% of grid squares ranked by species richness can be classified as hotspots. Individual models are con- structed by relating species occurrence data to environmental variables and projecting the modelled relationships onto geographical space (Elith et al. 2006). This method may pro- vide some useful advantages, such as better control for poorly modelled species, and easier identification of the set of the most important explanatory variables and of the response shapes between species and their environment in certain subgroups of species. The aim of this study was to provide a comprehensive picture of environmental pre- requisites for a set of threatened peatland species, thereby helping to plan peatland use in a more ecologically sustainable way. The specific aims were: (1) to scrutinize how the occurrence of species is affected by different environmental correlates; and (2) to identify the richness patterns and hotspots of threatened species. The models were developed for the “aapa mire zone” in central and northern parts of Finland. It is worth noting that spe- cies richness per se was the target of our study. Predictive modelling was focused on the 1 3 Biodiversity and Conservation (2019) 28:1173–1204 1175 richness of species that characterize valuable environments that need specific attention in planning. The study thereby provides a new approach to focused biodiversity modeling. Study area The study was carried out in Finland, located between 63° and 68° latitudes in northern Europe (Fig.  1). Biogeographically the study area lies in the middle and northern boreal zones covering almost the entire aapa mire zone, where climate is more continental than in most other parts of northern Europe but with some humid, maritime effect (Ahti et al. 1968). The annual mean temperature declines from south (+ 5  °C) to north (− 2  °C) and the mean annual precipitation sum varies between 450 and 750 mm (Pirinen and Ruuhela 2012). Peatlands and pine and spruce-dominated forest are frequent, as well as numerous lakes and rivers characterizing the landscape of the study area. The peatland habitats of the studied plant species are of three types. Mesotrophic fens are mainly open peatlands with deep peat deposits. The field layer vegetation is character - ized by sedges and herbaceous plants, and the ground layer consists of sphagnum mosses or other bryophytes. Rich fens are open or sparsely wooded peatlands with high species diversity of vascular plant and mosses. They are typically found in areas where the bedrock and soil are calcium-rich. Approximately half of Finland’s threatened peatland species are primarily associated with rich fens (Rassi et al. 2010). Spruce swamp forests are wooded minerotrophic peatlands where the dominant tree species is usually Norway spruce (Picea abies), though deciduous trees may also grow abundantly in Spruce forests that are richer in nutrients. The presence of living and dead trees of different sizes and ages is an impor - tant structural feature for the species diversity of Spruce swamp forests. Finnish mires have been intensively drained in the last century, and more than half of the 10.0 million hectares of originally pristine mires have been drained to improve timber growth (Finnish Forest Research Institute 2014). The study area was divided into grid cells of 25  ha (500  m × 500  m), and cells where peatlands covered less than 5% were excluded from the study. Thus, the study area consists of a total of 500,545 grid cells (125,136 km ). Plant species data We used the occurrence records of threatened mire plant species from the national database of red-listed species (Rassi et al. 2010) (Table 1). The field records produced by voluntary amateur and professional botanists are the most important data sources for this database, but information on species occurrences has also been gathered from the scientific literature and herbaria (Ryttäri et al. 2012; Rassi et al. 2010). Species data included detailed informa- tion on the geographical location of the occurrences (coordinates in the uniform grid sys- tem, Grid 27°E). In total, 48 species with ten or more records in the whole study area were used in the analyses (Fig. 1, Table 1). Only observations with accuracy better than 100 m and presence observations from 1990 or later, were selected for this study. As the databases of red-listed species do not include records for the absence of species, the assumption was made that the absence of a record from a sampled grid square corresponded to true absence of the species, because a quasi-exhaustive sampling could be assumed for most squares with presence records (Guisan and Zimmermann 2000). 1 3 1176 Biodiversity and Conservation (2019) 28:1173–1204 Fig. 1 The location of the study area in a boreal landscape in northern Finland, together with the distribu- tion map with the observational points of the threatened plant species studied. Land cover classification is based on data about the drainage status of peatlands (SYKE) We modelled the habitat requirements for all species by using the same environmen- tal predictors for each species. Based on their different associations with the various envi- ronmental predictors, we grouped the species into five groups as follows: Mesotrophic fen species (n = 6), rich fen species (n = 10), calcareous species (n = 22), spruce swamp forest species (n = 3) and decaying wood species (n = 7). Rich fen species and calcareous species 1 3 Biodiversity and Conservation (2019) 28:1173–1204 1177 Table 1 Number of presence records in the study area, red list category of the species, main habitats of the species and group for the studied plant species Species Number of records Red List Main habitat Group category Rich fen  Carex heleonastes 779 VU Sl VA  Dactylorhiza incarnata subsp. cruenta 252 VU Sl VA  Dactylorhiza incarnata subsp. incarnata 1545 VU Sl VA  Hamatocaulis vernicosus 2453 VU Sl BR  Leiocolea bantriensis 23 NT Sl BR  Lophozia grandiretis 34 EN Sl BR  Meesia longiseta 96 EN Sl BR  Moerckia hibernica 103 VU Sl BR  Riccardia multifida 11 NT Vl BR  Sphagnum contortum 62 NT Sl BR Mesotrophic fen  Carex laxa 123 NT Snr VA  Epilobium laestadii 56 EN Sl VA  Hamatocaulis lapponicus 70 EN Sl BR  Hammarbya paludosa 239 NT Sn VA  Lycopodiella inundata 30 NT Rjt VA  Rhynchospora fusca 261 NT Sla VA Calcareous  Amblyodon dealbatus 33 VU Sl BR  Botrychium virginianum 54 EN Mlt VA  Bryum pseudotriquetrum var. neodamense 98 VU Sl BR  Calypso bulbosa 1265 VU Mltv VA  Campyliadelphus elodes 30 VU Kk BR  Carex appropinquata 178 VU Sl VA  Carex viridula var. bergrothii 126 VU Sl VA  Cypripedium calceolus 1557 NT Mlt VA  Dactylorhiza fuchsii 66 NT Sl VA  Dactylorhiza lapponica 160 VU Sl VA  Dactylorhiza traunsteineri 376 VU Sl VA  Dicranum acutifolium 13 NT Kk BR  Eriophorum brachyantherum 72 VU Slr VA  Malaxis monophyllos 28 EN Sl VA  Palustriella commutata 60 VU Vl BR  Palustriella decipiens 496 NT Vl BR  Palustriella falcata 549 NT Sl BR  Philonotis calcarea 60 EN Vl BR  Pseudocalliergon angustifolium 68 VU Sl BR  Pseudocalliergon lycopodioides 28 VU Sl BR  Saxifraga hirculus 1127 VU Sl VA  Schoenus ferrugineus 35 EN Sl VA Spruce swamp forest  Carex atherodes 43 NT Skr VA 1 3 1178 Biodiversity and Conservation (2019) 28:1173–1204 Table 1 (continued) Species Number of records Red List Main habitat Group category  Epipogium aphyllum 111 VU Mkt VA  Poa remota 21 NT Sk VA Decaying wood  Anastrophyllum hellerianum 225 NT Mktv BR  Calypogeia suecica 17 VU Mktv BR  Jungermannia leiantha 29 NT Mktv BR  Lophozia ascendens 42 VU Mktv BR  Lophozia ciliata 15 NT Mktv BR  Lophozia longiflora 19 NT Mktv BR  Riccardia palmata 51 NT Skv BR Total 13,189 Red list category: EN endangered, VU vulnerable, NT near threatened. Main habitats of the species: Kk rock outcrops (incl. erratic boulders), Mlt dry and mesic herb-rich forests, Mltv dry and mesic herb-rich forests, old-growth forests, Rjt inland open alluvial shores, Sl rich fens, Sla open rich fens (incl. herb-rich sedge fens), Slr rich pine fens, Sn fens, Snr mesotrophic fens, Vl spring complexes. Group: VA vascular plant, BR bryophytes (Rassi et al. 2010) are partially overlapping. The reason for separating these two species groups was that rich fen species can also be found outside the calcareous areas whereas calcareous species are restricted only to calcareous habitats. Environmental correlates We selected a set of quantitative correlates that would reflect the main biophysical gradi- ents with a recognized, physiological influence on plants. In total, 17 environmental vari- ables were calculated for all of the studied grid squares of 25  ha and were then used to explain plant species distribution. Two correlates/variables indicated climate, one topog- raphy, one geology and 13 local habitat features (Table 2). Correlations among these vari- ables were only moderate (Spearman correlation < 0.7) and thus, none of the variables was excluded a priori from the actual modelling. Temperature and moisture requirements reflect the principal limitations on plant growth and survival (Skov and Svenning 2004). Thus, growing degree days (> 5 °C) (GDD) and water balance (mm) (WAB) were calculated for the years 1981–2010 from climate data with 1 km resolution (Finnish Meteorological Institute, Pirinen and Ruuhela 2012). Water balance was used because precipitation alone is not a good measure of the water available for plant growth. A simple water balance variable was calculated as the monthly difference between precipitation and potential evapotranspiration, as suggested by Skov and Svenning (2004). The potential evapotranspiration (PET) was calculated as: PET = 58.92 × T above 0 C, where T above 0 °C is the annual mean of monthly mean temperatures with negative val- ues adjusted to zero (Holdridge 1967; Lugo et al. 1999). 1 3 Biodiversity and Conservation (2019) 28:1173–1204 1179 1 3 Table 2 List of selected environmental variables used as explanatory variables in the modeling experiments Environmental variable Abbreviation Unit Mean [min–max] Data source Growing degree days (> 5 °C) GDD – 910 [517–1154] FMI −1 Mean water balance WABmm year 330 [137–442] FMI Mean topographical wetness index TWI – 14.11[6.85–25.22] NLS, DEM Proportion of undrained peatlands in grid square UNDRAINED % 16.9 [0–100] SYKE Proportion of drained peatlands in grid square DRAINED % 23.4 [0–100] SYKE Proportion of open peatlands in grid square OPEN % 6.6 [0–100] SYKE Proportion of herb-rich site type (peatlands) in grid square KP1 % 0.5 [0–100] Luke, MS-NFI Proportion of Vaccinium myrtillus site type (peatlands) in grid square KP2 % 2.1 [0–100] Luke, MS-NFI Proportion of Vaccinium vitis-idaea site type (peatlands) in grid square KP3 % 10.9 [0–100] Luke, MS-NFI Proportion of Cladina site type (peatlands) in grid square KP6 % 1.5 [0–100] Luke, MS-NFI Proportion of calcareous rock in grid square CALC % 0.4 [0–100] NLS, DBM Mean volume, pine PINE m /ha 34.86 [0–201.50] Luke, NS-NFI Mean volume, spruce SPRUCE m /ha 11.52 [0–254] Luke, NS-NFI Mean volume, birch BIRCH m /ha 12.47 [0–130.50] Luke, NS-NFI Mean volume, other broad-leaved trees OTHER m /ha 12.47 [0–130.5] Luke, NS-NFI Presence of springs SPRING – 0/1 NLS Data sources: FMI Finnish Meteorological Institute, NLD national land survey, DEM digital elevation model, SYKE Finnish Environment Institute, Luke; Natural Resources Institute Finland, NS-NFI multi-source national forest inventory, DBM digital base map 1180 Biodiversity and Conservation (2019) 28:1173–1204 Moisture conditions in peatlands can be related to many ecological processes across landscapes, e.g. species composition and distribution, peatland productivity (Iverson et al. 1997) and hydrology in terms of ombrotrophy and minerotrophy. Topographic wetness index (TWI) was used to describe local relative differences in moisture conditions (Gessler et  al. 2000). High values represent lower catenary positions (wet) and small values upper catenary positions (dry). The moisture level of the study area was calculated by defining the wetness index (or compound topographic index) using the following formula (Bur- rough and McDonnell 1998): TWI = ln(∕tan), where ɑ is the upslope contributing area per width orthogonal to the flow direction, and tanβ is the local slope in radians. Moreover, data about the drainage stage of peatlands with a resolution of 25 m × 25 m (Finnish Environmental Institute 2009) were used to calculate the proportion of undrained (UNDRAINED) and drained (DRAINED) peatland area as percentage cover for each grid square. As many of the threatened plant species require calcareous substrate, the proportion of calcareous rock (CALC) as percentage extent for each grid square was calculated from dig- ital maps of Quaternary deposit and pre-Quaternary rocks (Digital Base Map, NLS) using ArcGIS software (ESRI 1991). The presence of springs (SPRINGS) was used as many of the modelled species benefit from springs. Information on the percentage cover of main peatland site types and site fertility in each 25-hectare grid square was derived from the Multi-source National Forest Inventory (MS- NFI) from 2011 (Natural Resources Institute Finland 2013) with a resolution of 20 m × 20 m. Site fertility classes were selected to match the habitat requirements of the studied plant species as accurately as possible: eutrophic peatlands and corresponding drained peatlands (namely herb-rich types, KP1), mesotrophic mires and fens and corresponding drained peatland forests, (Oxalis-myrtillus type, KP2), meso-oligotrophic natural and drained peat- lands (Vaccinium myrtillus type, KP3), and Sphagnum fuscum-dominated (ombrotrophic) natural and drained peatlands (Cladina type, KP6). The proportions of open peatlands (OPEN MIRE) and spruce swamp forests (SPRUCE SWAMP FOREST) in grid squares were used to reflect general habitat patterns of peatland properties in each grid squares. Moreover, mean volumes (m /ha) of four tree species—pine (PINE) (Pinus sylvestris), spruce (SPRUCE), birch (BIRCH) (Betula pendula and B. pubescens) and other broad- leaved trees (OTHER) were calculated from MS-NFI-data and employed in the modeling. Habitat suitability modelling The presence-only habitat suitability modelling method Maxent v3.3.3  k (Phillips et  al. 2006) was used to predict species distributions across the aapa mire zone. The resulting habitat suitability model represents the relative probability of the species’ distribution over all grid squares in the defined geographic space, where a high probability value indicates that the location is predicted to have suitable environmental conditions for the species (Hir- zel et al. 2002). Maxent has been utilized extensively to model species’ ranges using pres- ence-only data, and it has been shown to perform well even with scarce and noisy presence data subsets collected by different researchers and methodologies (Elith et al. 2006; Frank - lin 2010). Maxent has also performed well in modelling other ecosystem services, such as the distribution of GHG-balances (Parkkari et al. 2017). 1 3 Biodiversity and Conservation (2019) 28:1173–1204 1181 To be able to compare and combine or stack models for multiple species, the same environmental predictors and Maxent parameters were used for all species. Model calcula- tions were made using the Maxent logistic output, rather than raw or cumulative output, in order to facilitate comparisons between species (Merow et al. 2013). Maximum iterations were set at an average of 5000, based on model performance across all target species. The remaining settings were left at the default setting. Moreover, response curves were created to show how the predicted relative probability of occurrence depends on the value of each environmental variable. Model evaluation The models and model predictions were evaluated using the area under the curve (AUC) of a receiver operating characteristic (ROC) plot based on a four-fold cross-validation (Field- ing and Bell 1997), routinely calculated for each run with Maxent. Cross-validation was performed with subsets of the entire dataset, where each subset contained an equal number of randomly selected data points. Each subset was then dropped from the model, the model was recalculated, and predictions were made for the omitted data points. A combination of the predictions from the different subsets was then plotted against the observed data (Lehmann et  al. 2002). Following Swets (1988), model accuracy was considered low if AUC was below 0.7, fair if it was between 0.7 and 0.8, good if between 0.8 and 0.9, and excellent if AUC was above 0.9. To identify the relative importance of various environmental variables for species, we employed two outcomes of the Maxent model: percent contribution and permutation importance of each environmental variable. The percent contribution values are only heu- ristically defined: they depend on the particular path that the Maxent code uses to arrive at the optimal solution, and a different algorithm could give rise to the same solution via a different path, resulting in different percent contribution values. If there are highly cor - related environmental variables, the percent contributions should be interpreted with cau- tion. The permutation importance measure depends only on the final Maxent model, not the path used to obtain it. The contribution for each variable is determined by randomly permuting the values of that variable among the training points (both presence and back- ground) and measuring the resulting decrease in training AUC. A large decrease indicates that the model depends heavily on that variable. Hence, permutation importance appears to be a better measure of a variable’s explanatory power—since it is path—(algorithm-) inde- pendent. Modelling performance was evaluated using the regularized training gain, which describes how much better the Maxent distribution fits the presence data compared to a uniform distribution (Phillips and Dudic 2008). A jackknife test was also run to obtain alternate estimates of variable importance. Each variable was excluded in turn, and a model was created with the remaining variables. The model was then created using each environmental variable in isolation. In addition, a model was created using all variables. For the variables with the highest predictive val- ues, response curves show how each of these environmental variables affects the Maxent predictions (Phillips and Dudík 2008). The curves illustrate how the logistic prediction changes as each environmental variable is varied, while keeping all other environmental variables at their average sample value. The curves thus represent the marginal effect of changing any single variable alone. 1 3 1182 Biodiversity and Conservation (2019) 28:1173–1204 Hotspot maps First, we produced projected distribution maps for individual species at a spatial resolu- tion of 25  ha. The continuous Maxent output maps were reclassified into binary maps of suitable (1) and unsuitable (0), using the averaged species-specific logistic thresh- old value that “maximises training sensitivity plus specificity” (Liu et  al. 2013). This threshold selection method has been shown to perform rather well with presence-only data (Liu et  al. 2005, 2013), and is suitable for this study considering the goal is to predict where current suitable habitats are located. Choosing a relatively high threshold reduces the risk of choosing unsuitable sites by identifying only those areas with the highest suitability (Pearce and Ferrier 2000). Next, to create richness maps, we combined the binary maps representing suitable habitats for individual species and used a simple summation of the predicted suitabili- ties using the Raster Calculator feature in ArcGIS v10.2 for each species group sepa- rately, and also for all 48 species. The reason for doing so was that this allowed us to investigate whether certain species groups are more intimately related to certain envi- ronmental predictors than other groups. Spruce swamp forest species were excluded from the species group analysis, as species richness and hotspot based on only three species is not particularly informative. However, they were included in the analyses of total species richness and summary hotspot based on individual species. We then identified richness hotspots as the top 5% of grid squares ranked by spe- cies richness in each species groups (see Prendergast et al. 1993; Williams et al. 1996). Finally, summary hotspot map was produced by stacking the hotspot maps from all indi- vidual species. Results For all models, the AUC was excellent for the training data (mean AUC 0.924, ranging from 0.841 to 0.989) and good for the evaluation data (mean AUC 0.855, ranging from 0.607 to 0.959) (Table  3). The highest test AUC values were obtained with calcareous species (mean 0.88) and the lowest with decaying wood species (mean 0.80), perhaps because decaying wood environments exist extensively outside peatland habitats. On average, the proportions of drained peatland area, open peatland and undrained peatland area had the highest permutation importance (15.2, 14.7, and 10.2%, respec- tively) across the modelled species groups (Table  4). When considering importance between species groups, proportions of undrained (mainly positive responses) and drained peatland area (negative responses) showed the greatest impact (18.0% and 18.3%) followed by the proportion of open peatland with positive association (8.8%) in rich fen species models (Tables 4, 5). In calcareous species models, the proportion of drained peatland area was the most influential variable with positive response (13.6%), followed by proportion of calcare- ous rock (8.9%, mainly positive responses), and volume of pine (8.9%, mainly negative responses). Increasing proportions of undrained peatland (13.5%), growing degree days (13.5%), and proportion of open peatlands (12.8%) contributed most and positively to mesotrophic fen species models. 1 3 Biodiversity and Conservation (2019) 28:1173–1204 1183 Table 3 Performance of the models applied to individual threatened plant species distribution, as assessed by the AUC values from the training (Training AUC) and evaluation data (Test AUC) and the stability of the models (Standard Deviation) Species Training AUC Test AUC AUC standard deviation Rich fen  Carex heleonastes 0.92 0.90 0.02  Dactylorhiza incarnata subsp. cruenta 0.93 0.89 0.02  Dactylorhiza incarnata subsp. incarnata 0.84 0.84 0.01  Hamatocaulis vernicosus 0.92 0.91 0.01  Leiocolea bantriensis 0.94 0.82 0.09  Lophozia grandiretis 0.85 0.69 0.14  Meesia longiseta 0.97 0.94 0.03  Moerckia hibernica 0.94 0.87 0.04  Riccardia multifida 0.97 0.96 0.02  Sphagnum contortum 0.90 0.77 0.08 0.92 0.86 0.05 Mesotrophic fen  Carex laxa 0.88 0.81 0.04  Epilobium laestadii 0.94 0.88 0.05  Hamatocaulis lapponicus 0.98 0.89 0.08  Hammarbya paludosa 0.92 0.89 0.02  Lycopodiella inundata 0.86 0.69 0.12  Rhynchospora fusca 0.95 0.94 0.01 0.92 0.85 0.05 Calcareous  Amblyodon dealbatus 0.97 0.96 0.03  Botrychium virginianum 0.95 0.88 0.07  Bryum pseudotriquetrum var. neodamense 0.94 0.91 0.03  Calypso bulbosa 0.94 0.93 0.01  Campyliadelphus elodes 0.95 0.86 0.05  Carex appropinquata 0.92 0.87 0.03  Carex viridula var. bergrothii 0.93 0.88 0.04  Cypripedium calceolus 0.90 0.89 0.01  Dactylorhiza fuchsii 0.91 0.80 0.05  Dactylorhiza lapponica 0.97 0.95 0.02  Dactylorhiza traunsteineri 0.85 0.81 0.02  Dicranum acutifolium 0.95 0.91 0.02  Eriophorum brachyantherum 0.91 0.87 0.04  Malaxis monophyllos 0.99 0.88 0.08  Palustriella commutata 0.95 0.91 0.03  Palustriella decipiens 0.91 0.87 0.03  Palustriella falcata 0.92 0.89 0.02  Philonotis calcarea 0.95 0.90 0.06  Pseudocalliergon angustifolium 0.95 0.87 0.10  Pseudocalliergon lycopodioides 0.96 0.84 0.11 1 3 1184 Biodiversity and Conservation (2019) 28:1173–1204 Table 3 (continued) Species Training AUC Test AUC AUC standard deviation  Saxifraga hirculus 0.90 0.89 0.01  Schoenus ferrugineus 0.94 0.86 0.07 0.93 0.88 0.04 Spruce swamp forest  Carex atherodes 0.93 0.84 0.06  Epipogium aphyllum 0.86 0.77 0.05  Poa remota 0.95 0.81 0.08 0.92 0.81 0.06 Decaying wood  Anastrophyllum hellerianum 0.89 0.83 0.03  Calypogeia suecica 0.96 0.81 0.08  Jungermannia leiantha 0.87 0.73 0.10  Lophozia ascendens 0.94 0.88 0.04  Lophozia ciliata 0.92 0.82 0.11  Lophozia longiflora 0.87 0.66 0.10  Riccardia palmata 0.90 0.85 0.06 0.91 0.80 0.07 All species on average 0.924 0.855 0.050 Spruce swamp forest species were mainly negatively associated with proportion of open peatlands (36.4%), drained peatland area (13.0%), and volume of spruce (6.7%). In decaying wood species, the proportion of drained peatland area (19.0%, mainly negative associations), water balance (12.5%), and topographical wetness index (11.5%) showed the greatest impact. Looking to the jackknife evaluations across all modelled species, we found that the proportion of undrained peatland, topographical wetness index, and volume of spruce were the three most effective predictors when used individually (Table  6). In addition, proportions of calcareous rock, open peatlands, and drained peatland area decreased the gain most when they were omitted, and therefore contained information that was not present in any other variable (Table 7). However, species groups differed from each other according to their habitat preferences. In mesotrophic fen species models, the pro- portion of undrained peatland area had the highest gain when used in isolation (Table 6) and the largest decrease in gain when omitted (Table 7). These environmental variables contain information that is useful on its own and not present in other variables. Like- wise, the proportion of calcareous rock provided the most useful and unique information on the distribution of calcareous species. In spruce swamp forest species models, the greatest change occurred when volume of spruce was used in isolation. Predictions of threatened plant species richness, based on the summation of sin- gle-species predictions, and hotpots, are shown in Figs.  2 and 3. Suitable habitats for mesotrophic fen and rich fen species were predicted in the western part of the study area, whereas rich fen species had high suitabilities also in northern parts. Eastern and southeastern parts of the study area had high suitability for presence of decaying wood 1 3 Biodiversity and Conservation (2019) 28:1173–1204 1185 Table 4 Relative importance (%) of single environmental variables for predicting the habitat suitability of five species groups based on permutation importance (in Maxent)  Environmental variable Rich fen Mesotrophic fen Calcareous Spruce Decaying wood Mean swamp forest CAL 3.17 0.38 8.87 2.15 0.52 3.02 GDD 7.38 13.51 8.36 2.42 2.40 6.81 KP1 3.29 4.98 7.26 4.27 7.03 5.37 KP2 0.77 3.56 2.76 1.64 5.74 2.89 KP3 2.93 4.74 1.83 4.79 2.10 3.28 KP6 3.88 2.93 2.67 3.50 5.65 3.73 BIRCH 4.81 5.32 4.76 3.34 3.91 4.42 SPRUCE 3.28 9.71 4.84 6.69 7.03 6.31 SPRINGS 2.70 1.17 1.86 2.15 1.08 1.79 PINE 5.59 3.85 8.89 5.76 4.82 5.78 OTHER 5.09 0.94 3.74 2.18 2.01 2.79 SPRUCE SWAMP FOR- 1.75 4.28 4.81 1.29 0.60 2.55 EST OPEN MIRE 8.82 12.81 8.15 36.40 7.06 14.65 UNDRAINED 18.04 13.49 8.20 4.31 7.19 10.24 DRAINED 18.33 11.99 13.59 12.98 18.96 15.17 TWI 2.23 4.20 6.86 3.96 11.45 5.74 WAB 7.95 2.15 2.56 2.18 12.47 5.46 Total 100 100 100 100 100 100 Three most important variables for each species group and on average are in bold. Abbreviations are explained in Table 2 species. Suitable habitats for calcareous species were, not surprisingly, mainly concen- trated in the areas with much calcareous rock. Discussion Regional-scale biodiversity patterns, i.e. spatial resolutions ranging from 0.5 to 2 km, are an important component of the diversity that occurs in a landscape or region, and also represent the scale at which land use decisions are often made. Sustainable planning of peatland use requires an analysis of potential ecological resources and the effects of their utilization on an area. Without knowledge of the biodiversity values of peatlands, their unsustainable utilization continues to degrade their biodiversity and threaten the remain- ing valuable habitats and species. Predictive habitat suitability modelling may consider- ably increase the efficiency of biodiversity mapping schemes and incorporate un-surveyed regions into decision-making. Using models to predict potential species distributions is also likely to become increasingly important as environmental change and other dynamic processes are incorporated into land use planning efforts (Rondinini et  al. 2006; Under - wood et al. 2010). The aim of this study was not to reflect the full reality, but to construct and evaluate simple and ecologically significant habitat suitability models that approximate 1 3 1186 Biodiversity and Conservation (2019) 28:1173–1204 1 3 Table 5 Summary of the shape of the response curves between modelled plant species (n = 48) and each environmental variable cal gdd kp1 kp2 kp3 kp6 birch spruce springs pine deciduous Spruce open mire undrained drained twi wab swamp forest Rich fen  Carex heleonastes + − Ω Ω + Ω U − N − − + + + − + −  Dactylorhiza incarnata subsp. + Ω Ω Ω + Ω + − N − + Ω + + − + − cruenta  Dactylorhiza incarnata subsp. + − Ω Ω + Ω − − N − − − Ω + − + − incarnata  Hamatocaulis vernicosus + − Ω Ω + Ω U − N − − Ω + + − + −  Leiocolea bantriensis + − + − − − − + N Ω + − − Ω − − +  Lophozia grandiretis + + Ω − − − U − N − − − Ω + − − −  Meesia longiseta − − Ω Ω + Ω − − N − − − + + − + −  Moerckia hibernica + − Ω Ω + Ω − − N − − + + + − + −  Riccardia multifida + + Ω − − − U − N − − − − + − + − Sphagnum contortum + − Ω Ω + Ω U − N − − Ω + + − + − Mesotrophic fen  Carex laxa − − Ω Ω + + U − N − − − Ω + − + −  Epilobium laestadii + − Ω Ω − Ω − + N − − + − + − − −  Hamatocaulis lapponicus − N + + + + Ω Ω N Ω − − + + − + −  Hammarbya paludosa + + Ω + + + U − N U − − Ω + − + −  Lycopodiella inundata − + − − − + − − N − − − + − + + N  Rhynchospora fusca − + − + + Ω − − N − − − + + − + − Calcareous  Amblyodon dealbatus + − Ω + − + − − N − − + − N − N −  Botrychium virginianum + + − − − − − Ω N − − − − − − − −  Bryum pseudotriquetrum var. neo- + − Ω Ω + + − − N − − + + + − + − damense  Calypso bulbosa + Ω Ω − − − Ω Ω N Ω Ω + − Ω − − Ω Biodiversity and Conservation (2019) 28:1173–1204 1187 1 3 Table 5 (continued) cal gdd kp1 kp2 kp3 kp6 birch spruce springs pine deciduous Spruce open mire undrained drained twi wab swamp forest  Campyliadelphus elodes + − Ω − − − − − N − + − − − − − +  Carex appropinquata + Ω Ω Ω Ω Ω − − N − − Ω Ω + − + −  Carex viridula var. bergrothii + − Ω Ω Ω + − − N − − + Ω + − + +  Cypripedium calceolus + Ω Ω Ω − − Ω Ω N Ω Ω + − Ω − − Ω  Dactylorhiza fuchsii + − + Ω − + + + N − − + − + − − +  Dactylorhiza lapponica + − Ω Ω + Ω − − N − − Ω Ω + − + Ω  Dactylorhiza traunsteineri + − Ω Ω Ω Ω U − N − − − Ω + − + +  Dicranum acutifolium N − − − − − − N N − − − − − − − N  Eriophorum brachyantherum + − Ω − − − − Ω N − − − − + − − −  Malaxis monophyllos + + Ω Ω + + − + N − − + − + − + −  Palustriella commutata + − − − − − − − N − − − − − − − −  Palustriella decipiens + − Ω Ω + + − + N − − + − − − − +  Palustriella falcata + Ω Ω Ω − Ω − Ω N Ω − + − Ω − − +  Philonotis calcarea + − Ω − − − − + N − − − − + − − +  Pseudocalliergon angustifolium + − N − − − − + N − − − − − − − −  Pseudocalliergon lycopodioides + − N − − − − + N − − − − N − − −  Saxifraga hirculus + − Ω Ω Ω Ω − − N − − Ω Ω + − + −  Schoenus ferrugineus + − + − − − − − N − − − − N − − − Spruce swamp forest  Carex atherodes + + Ω Ω + − Ω + N − − + − − + − +  Epipogium aphyllum + + − − − − + + N + Ω Ω − − − − +  Poa remota − + − − − − + + N − + + − − − − + Decaying wood sp.  Anastrophyllum hellerianum + − + − − − − + N Ω + − − Ω − − + 1188 Biodiversity and Conservation (2019) 28:1173–1204 1 3 Table 5 (continued) cal gdd kp1 kp2 kp3 kp6 birch spruce springs pine deciduous Spruce open mire undrained drained twi wab swamp forest  Calypogeia suecica − N − − − − N + N + − − − − − − +  Jungermannia leiantha − + − − − − − + N + + − − + − − +  Lophozia ascendens − + − − − − N + N + − − − + − − +  Lophozia ciliata − N − − − − − + N N − − − + − − +  Lophozia longiflora N N − − − − + + N − + − − + − − N  Riccardia palmata − − − − − − − + N − − − − + − − + The direction of the effect indicated with the symbols (+  =  positive linear correlate, = negative linear correlate, Ω = non-linear correlate with a humped response curve, U = non-linear correlate with downward humped response curve; N = no trend). Abbreviations are explained in Table 2 Biodiversity and Conservation (2019) 28:1173–1204 1189 Table 6 Results of jackknife evaluations of relative importance of environmental variables when used in isolation, with only the corresponding feature Rich fen Mesotrophic fen Calcareous Spruce Decaying wood Mean swamp forest CAL 0.14 0.01 0.48 0.17 0.00 0.16 GDD 0.14 0.18 0.23 0.02 0.01 0.12 KP1 0.19 0.08 0.12 0.01 0.03 0.09 KP2 0.12 0.04 0.11 0.02 0.09 0.08 KP3 0.29 0.12 0.11 0.04 0.04 0.12 KP6 0.09 0.22 0.07 0.01 0.04 0.09 BIRCH 0.16 0.29 0.09 0.15 0.01 0.14 SPRUCE 0.13 0.44 0.06 0.21 0.09 0.19 SPRINGS 0.16 0.02 0.13 0.08 0.02 0.08 PINE 0.32 0.30 0.21 0.01 0.01 0.17 DECIDUOUS 0.08 0.15 0.05 0.04 0.01 0.06 SPRUCE 0.03 0.07 0.07 0.13 0.02 0.06 SWAMP FOR- EST OPEN MIRE 0.28 0.27 0.09 0.20 0.06 0.18 UNDRAINED 0.59 0.64 0.20 0.02 0.01 0.29 DRAINED 0.23 0.11 0.20 0.07 0.19 0.16 TWI 0.22 0.33 0.20 0.14 0.36 0.25 WAB 0.16 0.09 0.05 0.02 0.27 0.12 The most important variable for each species group and on average is in bold. Abbreviations are explained in Table 2 this reality and constitute useful tools for land use planning. By targeting on the richness of species of valuable environments also provide a new approach to focused biodiversity modelling. Modelling performance: uncertainty issues Although the predictive performance of the models was rather high across the species, it is important to be aware that the ecological significance of the observed relationships between environmental variables and species occurrences are not always obvious. In this study, species richness is simply predicted by stacking presence–absence predictions of all species. This method hence relies on our ability to model the distributions of individual species, a field that has greatly matured over the last two decades (see Guisan and Thuiller 2005; Elith and Leathwick 2009; Franklin 2010). Thus, the main factors are those control- ling individual species distributions, and often purely abiotic variables are used. One of the main caveats in using individual species models to generate species richness patterns is that it tends to overestimate actual species richness (i.e., commission errors; Algar et al. 2009; Trotta-Moreu and Lobo 2010; Guisan and Rahbek 2010). The individual species models created in this study do not include all environmental, ecological (particu- larly competition), and historical factors that affect species distributions. By solely using environmental variables at rather coarse resolution to construct predictions of a species’ 1 3 1190 Biodiversity and Conservation (2019) 28:1173–1204 Table 7 Results of jackknife evaluations of relative importance of environmental variables when used in isolation, without the corresponding feature Rich fen Mesotrophic fen Calcareous Spruce Decaying wood Mean swamp forest CAL 1.30 1.45 1.19 0.94 0.93 1.16 GDD 1.37 1.37 1.47 1.08 0.93 1.24 KP1 1.37 1.40 1.47 1.05 0.93 1.24 KP2 1.40 1.43 1.52 1.08 0.91 1.27 KP3 1.38 1.42 1.52 1.06 0.93 1.26 KP6 1.39 1.39 1.51 1.05 0.93 1.25 BIRCH 1.40 1.44 1.52 1.07 0.92 1.27 SPRUCE 1.39 1.43 1.50 1.02 0.88 1.24 SPRINGS 1.30 1.44 1.47 1.04 0.93 1.24 PINE 1.38 1.42 1.49 1.02 0.91 1.24 DECIDUOUS 1.40 1.46 1.52 1.05 0.92 1.27 SPRUCE 1.40 1.45 1.51 1.08 0.94 1.28 SWAMP FOR- EST OPEN MIRE 1.37 1.38 1.50 0.95 0.89 1.22 UNDRAINED 1.36 1.35 1.51 1.06 0.89 1.24 DRAINED 1.34 1.41 1.48 1.00 0.87 1.22 TWI 1.40 1.43 1.47 1.06 0.86 1.24 WAB 1.33 1.43 1.51 1.07 0.80 1.23 The most important variable, the exclusion of which decreased the gain most, is in bold. Abbreviations are explained in Table 2 suitable habitat, models fail to incorporate biological, geographical or historical influences on species distributions (e.g., Guisan and Thuiller 2005; Heikkinen et  al. 2006). This, in turn, can lead to an overestimation of species suitable habitats, as only areas of suitable habitats, not their realized distributions, are projected. This kind of overestimation may increase when combining multiple individual models to create species richness maps, as was the case in this study. In the light of these facts, we propose that the habitat suitability models influence on potential over-prediction is the result of the inherent nature of habitat suitability models. Moreover, response curves are just simplifications of reality, and their shape may be strongly dependent on the setting of the study and the variable selection criteria used. First, a local model is fit to a particular region of the geographical space, but the model can dif- fer in different regions of the sample space. Certain abiotic factors, such as topography and land cover, may be important locally, but they generally can be applied only within a lim- ited geographical extent (Thuiller et al. 2003). Thus, conclusions about the response curve of species may only be made within the context of the study area. Second, it is possible that species may respond to a combination of a different set of variables in different parts of its distributional range, as the shape of responses in a multivariate model may depend on the nature of the correlations between the indirect variable and the causal gradients (Franklin 1995; Guisan and Zimmermann 2000). The use of different geographical extents and spa- tial resolutions could provide contradictory answers to the same ecological question. 1 3 Biodiversity and Conservation (2019) 28:1173–1204 1191 Fig. 2 Spatial predictions of species richness by groups for the whole study area: a mesotrophic fen species, b rich fen species, c calcareous species and d decaying wood species It should also be kept in mind that an area of suitable habitat is not occupied by a species if the species is unable to disperse there (Pulliam 2000; Newbold 2010). Dis- persal limitation (Kadmon and Shmida 1990), source-sink dynamics (Pulliam and Dan- ielson 1991) and metapopulation dynamics (Hanski 2005) will result in spatial patterns in species distributions that are at least partly independent of the environment. These 1 3 1192 Biodiversity and Conservation (2019) 28:1173–1204 Fig. 2 (continued) spatial patterns are referred to as “endogenous spatial autocorrelation” (Legendre 1993). Several papers have discussed the importance of measuring spatial autocorrelation when evaluating the importance of different factors to explain species distributions (e.g. Dor - mann et  al. 2007; Hawkins et  al. 2007). However, as the 25 ha grid cells in the model setup were distributed rather sparsely across the whole study area we assumed that the effect of spatial autocorrelation was rather small. Moreover, Parviainen et  al. (2008) 1 3 Biodiversity and Conservation (2019) 28:1173–1204 1193 Fig. 2 (continued) carried out autocorrelation assessment in a similar environment with a similar grid- based approach at the same 25-ha resolution. They found that inclusion of the effect of spatial auto-correlation as autocovariate term reflecting the species occurrences in the surroundings of the focal grid cell, had only a minor effect on the importance of the environmental variables and the shapes of predictor-response curves. 1 3 1194 Biodiversity and Conservation (2019) 28:1173–1204 Fig. 2 (continued) The observed distribution of threatened species is also affected by historical facts that may have restricted current distribution patterns of species (Guisan and Thuiller 2005; Svenning et al. 2008). This may, at least partly, explain the true absence of species in many areas where the environmental conditions are apparently suitable. Moreover, populations of threatened plant species may be extremely small and thus prone to local extinctions aris- ing from stochastic processes in areas with appropriate environmental conditions. In light 1 3 Biodiversity and Conservation (2019) 28:1173–1204 1195 Fig. 3 Spatial predictions of threatened plant species hotspots: by a species groups, and b total species rich- ness (n = 48 species) 1 3 1196 Biodiversity and Conservation (2019) 28:1173–1204 Fig. 3 (continued) 1 3 Biodiversity and Conservation (2019) 28:1173–1204 1197 of this fact, some plant species may actually have had populations in earlier periods of sig- nificantly better availability of suitable habitat, and current land use may have no relevance for their potential occurrence (Lindborg and Eriksson 2004; Helm et al. 2006; Wisz et al. 2007). Thus, the individual habitat suitability modelling approach is limited because with- out adding a dispersal filter, it may incorrectly predict species in areas that appear environ- mentally suitable but that are outside their colonizable or historical range. Threshold selection is one of the many possible biases in habitat suitability modelling. As suggested by Trotta-Moreu and Lobo (2010) and Mateo et al. (2012), the selection of an appropriate suitability threshold can reduce over-prediction in species models. However, selection is not straightforward and the results can vary, sometimes dramatically, depend- ing on the threshold chosen (Milanovicha et al. 2012; Liu et al. 2013). We chose to use the rather conservative maximum training sensitivity plus specificity threshold, as we found it to be a promising selection method for presence-only data. An interesting methodologi- cal line of future research would thus be to study the reliability of different thresholding approaches in modelling, as it may help to reduce over-predictions, at least in some cases. An additional problem in the selection of reliable and stable threshold values is the lack of real absences, as in the present study. When the modelling algorithm has no information on true absences, even small differences in the selected threshold value can have a substantial effect on the model outputs (see Jiménez-Valverde and Lobo 2007). Finally, the lack of floristic data from remote areas may possibly lead to a partial bias in the modelling analyses. The accuracy of the model increases with increased amounts and accuracy of presence and absence data, and may be updated to include new information to further refine distribution predictions (Elith and Leathwick 2009), but we assume that the main drivers of habitat suitability (which were predominantly ecologically plausible) will remain. Model transferability is one important feature in habitat suitability models and thus, developing models that are able to provide reliable predictions of species distributions in new areas or other times is a major challenge. As the results of this study revealed, plant distributions are often critically affected by certain local factors, such as soil or habitat properties or the occurrence of favourable microsites and microclimates (see also Parvi- ainen et  al. 2008; Elith and Leathwick 2009). Our models were generated for the boreal aapa mire landscape, where a relatively high proportion of the peatland landscape has remained in a semi-natural state despite intensive draining. On the other hand, the effect of draining is not limited to the drained peatlands only, but may extend over larger areas within the catchment (Holden et al. 2006). The models used in this study took into account only the local drainage effects within the grid cells. From the ecological viewpoint, our models may not be directly applicable to regions of highly fragmented, intensively used or cultural landscape typical of, for example, western and southern Europe. However, from the technical viewpoint, our approach is applicable over different ecosystems and habitats. Importance of environmental variables to plant species The distribution of plant species is limited by the availability of suitable habitats. For rare plants, especially those with limited geographic ranges, narrow habitat specificity can fur - ther limit distribution. While climate is an important driver of plant species distribution at the continental scale, soil properties and biotic interactions determine habitat avail- ability at smaller scales (Pearson et  al. 2004). Furthermore, competition may also affect species occurrence patterns and persistence capability (Virtanen et  al. 2010). Variations 1 3 1198 Biodiversity and Conservation (2019) 28:1173–1204 in peatland vegetation are the result of many environmental factors at the landscape and local scales, including the origins of the water that feeds the peatland, acidity levels (pH), the availability of main nutrients (nitrogen and phosphorus), the water table level, and the depth of the peat. Seasonal variations in moisture levels have also been found to be related to the composition of vegetation communities (Laitinen 2008). In this study, species responded differently to the analyzed habitat gradients. A mix- ture of unimodal and linear responses was typical of the gradients. As a general observa- tion, with most of the species the importance of variables reflecting local-scale variation in the habitat and land cover was superior to climate variables operating at higher scales. Responses to growing degree days varied according to the geographic distribution of the species; for example, Eriophorum brachyantherum is a northern species, for which suitable habitats occur at low numbers of growing degree days. Our model confirms the importance of particular environmental variables that influence the presence and quality of peatland habitat for the selected threatened plant species. In mesotrophic fen species models, a high amount of undrained open peatlands was the most powerful characteristic forcing distribution patterns of threatened species. Wetness, high variation in site fertility types, and microtopography are distinct characteristics for und- rained peatlands. The wettest peatlands have not been used for intensive forestry and agri- culture, and they therefore continue to be suitable habitats for peatland species. At drained sites, key hydrological characteristics have changed, which has led to the degradation of peatland vegetation (Similä et  al. 2014). Based on our findings, species growing on wet surfaces are most susceptible to the effects of changes in the water table. These include Carex heleonastes, Dactylorhiza incarnata ssp. incarnata, Hamatocaulis vernicosus, Mee- sia longiseta, Carex laxa, Hamatocaulis lapponicus, Dactylorhiza lapponica, Dactylorhiza traunsteineri, and Saxifraga hirculus. Epilobium laestadii is a demanding northern species that is most closely associated with nutrient-rich fens with a sparse and low field layer, and often occurs around springs or in seepage areas. Rhynchospora fusca is a sedge species characteristic of open flark fens. Amblyodon dealbatus, Cypripedium calceolus, and Dactylorhiza traunsteineri are cal- cium-demanding species of nutrient-rich, calcareous fens. As expected, the importance of calcareous bedrock was explicit to species demanding calcium, e.g. Amblyodon dealbatus, Malaxis monophyllos, and Pseudocalliergon lycopodioides. However, suitable habitats for calcareous species were also found in areas where there was not much calcareous rock. This kind of environments may contain for example small outcroppings, springs associated with distant calcareous deposits, or superficial calcareous deposits such as shell-rich sands. For example, Amblyodon dealbatus, Malaxis monophyllos, and Pseudocalliergon lycopodi- oides grow mainly in herb-rich fens, calcareous springs and wet calcareous rocky outcrops (Ulvinen 2001). The results of this study also revealed that numerous threatened species, such as Philonotis calcarea, Palustriella decipiens, and P. falcata, also occur most com- monly in springs which have a neutral pH. Sphagnum contortum is a rich fen species that exists in a rather restricted region within the study area, and most of the variation can be explained by habitat, particularly by the increasing proportion of undrained mires. How- ever, S. contortum does not occur throughout its potential habitat space because it performs best in nutrient- and calcareous-rich habitats and the presence of springs. Consequently, successful models require also other explanatory factors than undrained peatland alone. The models of decaying wood species performed more weakly than those of other spe- cies groups. This may, at least partly, be explained by the fact that the models did not con- tain dead wood as an explanatory variable. Many decaying wood species are dependent on old-growth forest habitats with high amounts of decaying wood (Rassi et al. 2010). We 1 3 Biodiversity and Conservation (2019) 28:1173–1204 1199 can therefore expect that old spruce swamp forests are also important habitats for these species, although this could not be directly seen in the models. Nevertheless, there was a high contribution of spruce and pine in the models. Interestingly, topographical wetness index, with mainly negative association, was important in determining suitable habitats for decaying wood species. This kind of topographical variable can serve as proxies for other environmental variables, such as soil properties and plant-available water, which may drive plant distributions (Lassueur et al. 2006). The species associated with decaying wood require a continuing presence of deadwood at various stages of decay, as well as evenly moist microclimates and shady growth sites (Laaka-Lindberg et al. 2009). Despite favoring moisture, these species do not tolerate wet conditions, which prevail at low elevated sites where water accumulates. Richness patterns and hotpots of species groups The predicted species richness maps and the location of the most species-rich hotspots indicated important differences between species groups. High potential species richness occurred for rich fen species in northern parts of the study area, for mesotrophic fen spe- cies in southwestern parts, and for decaying wood species broadly throughout the central- southeastern part of the study area. These differences reflect the differing habitat require- ments among the species groups. However, part of the differences may also arise from unbalanced species numbers within each group: mesotrophic fen (6), decaying wood (7), rich fen (19), and calcareous species (23). Reliable identification of hotspot areas with a high number of potentially suitable habi- tats for threatened species has a central role in land use and conservation planning. The grouped species approach in the landscape makes the identification of potentially high- quality habitats for rare species more reliable and the argument for sympathetic manage- ment of these habitats more compelling. The species richness analysis indicates that man- agement efforts, such as restoration, would provide the most benefit in northern parts for all threatened mire species as a whole. One reason may be that the amount of drainage decreases generally towards north (Finnish Forest Research Institute 2014), and the deg- radation of peatland habitats has not proceeded as intensively as in the heavily drained habitats further in the south. It must be noted that the hotspots of threatened species are not the only habitats impor- tant for biodiversity. Complementary approaches are needed, whereby typicalness indicates that abundant habitats and species at the centre of their natural range are also important (Latimer 2009). These typical areas also need to be maintained and actions taken to miti- gate, slow down or prevent their degradation. Our approach can also be used to present peatlands’ biodiversity “non-hotspots”. They may be either areas which are important because they hold characteristic assemblages of peatland species, or drained peatlands where forestry practices have degraded all but these ecologically valuable patches. Such areas are also important within the design of land management planning, before large scale peatland re-use is planned and carried out. Management of specific areas on behalf of one species group may not equally benefit all species within the overall species assemblage, since each species has its own habitat requirements. In contrast, summed richness maps can be readily divided into different sub- categories, enabling land use planners to scrutinize the predictions for species with, for example, different endangerment status or species with different characteristics such as vascular plants and bryophytes. Species groups can be adaptively used to address the needs 1 3 1200 Biodiversity and Conservation (2019) 28:1173–1204 of both individual species and groups of species by first setting management targets for a group, then testing the benefits of that management for individual species, and thereafter adjusting management direction to best benefit all species of concern (Wisdom et al. 2001; Wiens et al. 2008). Conclusions Our results demonstrate that the combination of individual species models offers a useful tool for identifying landscape-scale richness patterns for threatened mire plant species. In conclusion, habitat suitability models can help in determining which aspects of the envi- ronment of a given species have a critical impact on its distribution, and thus advance our understanding of the ecological requirements of species, while also providing valuable information concerning where species are likely to be found in insufficiently surveyed land- scapes. The modelling performance was high across the modelled species, and the rich- ness patterns generated by single models coincide with the expected richness pattern based on expert knowledge. These generated richness patterns therefore offer a powerful tool for basic biodiversity applications (e.g., land use planning and conservation). Predictive habitat suitability models and the summed richness maps can provide a valuable means of delimiting potentially valuable geographic areas and focus survey and management efforts onto valuable geographic areas and focus such efforts towards ensuring the preservation of biological diversity in aapa mire landscapes. Thus, when examining larger landscape sites suitable for different land use, the models created in this study offer considerable scope for use as “first filters” for identifying potential locations of hotspots of threatened species in boreal peatland landscapes at the regional scale. It is, however, important to emphasize that areas should not be valued simply on the basis of model predictions of threatened species. Typical species and habitats are also important for the biodiversity. It is also vital that mod- elled valuations should be subject to ground-truth assessment before planning decision- making takes place. Acknowledgements Open access funding provided by Natural Resources Institute Finland (LUKE). A study of this nature would not have been possible without the hundreds of volunteers who contributed their data to the red-listed plant species database. We are thankful for two anonymous referees whose comments and suggestions greatly improved the manuscript. The study is part of the EU LIFE + project LIFEPeat- LandUse (LIFE12 ENV/FI/000150). We also thank the Maj and Tor Nessling Foundation for providing a personal scholarship to M. Parkkari. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 Interna- tional License (http://creat iveco mmons .org/licen ses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. References Ahti T, Hämet-Ahti L, Jalas J (1968) Vegetation zones and their sections in northwestern Europe. Ann Bot Fennici 5:169–211 Algar AC, Kharouba HM, Young ER, Kerr JT (2009) Predicting the future of species diversity: macroeco- logical theory, climate change, and direct tests of alternative forecasting methods. Ecography 32:22–33 Barbet-Massin M, Jiguet F, Albert CH, Thuiller W (2012) Selecting pseudo-absences for species distribu- tion models: how, where and how many? Methods Ecol Evol 3:327–338 1 3 Biodiversity and Conservation (2019) 28:1173–1204 1201 Bolliger J, Kienast F, Soliva R, Rutherford G (2007) Spatial sensitivity of species habitat patterns to sce- narios of land use change (Switzerland). Landsc Ecol 22:773–789 Boyd R, Bjerkgård T, Nordahl B, Schiellerup H (eds.) (2016) Mineral resources in the Arctic. Geological Survey of Norway, Special Publication, p 483 Burrough PA, McDonnell RA (1998) Principles of geographical information systems. Spatial information systems. Oxford University Press, New York Dormann CF, McPherson JM, Araújo MB, Bivand R, Bolliger J, Carl G, Davies RG, Hirzel A, Jetz W, Kissling DW, Kühn I, Ohlemüller R, Peres-Neto PR, Reineking B, Schröder B, Schurr FM, Wilson R (2007) Methods to account for spatial autocorrelation in the analysis of species distributional data: a review. Ecography 30:609–628 Elith J, Leathwick J (2009) Conservation prioritization using species distribution models. In: Moilanen A, Wilson KA, Possingham HP (eds) Spatial conservation prioritization: quantitative methods and com- putational tools. Oxford University Press, Oxford, pp 70–93 Elith J, Graham C, Anderson R, Dudík M, Ferrier S, Guisan A, Hijmans R, Huettmann F, Leathwick J, Lehmann A, Li J, Lohmann L, Loiselle B, Manion G, Moritz C, Nakamura M, Nakazawa Y, Overton J, Peterson AT, Phillips S, Richardson K, Scachetti-Pereira R, Schapire R, Soberón J, Williams S, Wisz M, Zimmermann N (2006) Novel methods improve prediction of species’ distributions from occur- rence data. Ecography 29:129–151 ESRI (1991) ARC/INFO user’s guide. Cell-based modelling with GRID. Analysis, display and manage- ment. Environment Systems Research Institute, Inc., Redlands Fielding A, Bell J (1997) A review of methods for the assessment of prediction errors in conservation pres- ence/absence models. Environ conserv 24:38–49 Finnish Environmental Institute (2009) Finnish environmental institute spatial drainage stage data on peatlands Finnish Forest Research Institute (2014) Finnish statistical yearbook of forestry 2014. Finnish Forest Research Institute, Vantaa Franklin J (1995) Predictive vegetation mapping: geographic modeling of biospatial patterns in relation to environmental gradients. Prog Phys Geogr 19:474–499 Franklin J (2010) Mapping species distributions: spatial inference and prediction. Cambridge Univ, Cam- bridge, UK Freeman LA, Kleypas JA, Miller AJ (2013) Coral reef habitat response to climate change scenarios. PLoS ONE 8:1–14 Gaston KJ (1994) Rarity. Chapman & Hall, London, p 205 Gessler PE, Chadwick OA, Chamran F, Althouse L, Holmes K (2000) Modeling soil-landscape and ecosys- tem properties using terrain attributes. Soil Sci Soc Am J 64:2046–2056 Guisan A, Rahbek C (2010) Predicting spatio-temporal patterns of species assemblages through integration of macroecological and species distribution models with assembly rules and source pool assignments. J Biogeogr 38:1433–1444 Guisan A, Thuiller W (2005) Predicting species distribution: offering more than simple habitat models. Ecol Lett 8:993–1009 Guisan A, Zimmermann NE (2000) Predictive habitat distribution models in ecology. Ecol Model 135:147–186 Hanski I (2005) The shrinking world: ecological consequences of habitat loss. International Ecology Insti- tute, Oldendorf, 307 pp Hawkins BA, Diniz-Filho JAF, Bini LM, De Marco P, Blackburn TM (2007) Red herrings revisited: spatial autocorrelation and parameter estimation in geographical ecology. Ecography 30:375–384 Heikkinen RK, Luoto M, Araujo MB, Virkkala R, Thuiller W, Sykes MT (2006) Methods and uncertainties in bioclimatic envelope modeling under climate change. Prog Phys Geogr 30:751–777 Helm A, Hanski I, Pärtel M (2006) Slow response of plant species richness to habitat loss and fragmenta- tion. Ecol Lett 9:72–77 Hirzel AH, Le Lay G (2008) Habitat suitability modelling and niche theory. J Appl Ecol 45:1372–1381 Hirzel AH, Hausser J, Chessel D, Perrin N (2002) Ecological-niche factor analysis: how to compute habitat- suitability maps without absence data? Ecology 83(7):2027–2036 Holden J, Burt TP, Evans MG, Horton M (2006) Impact of land drainage on peatland hydrology. J Environ Qual 35:1764–1778 Holdridge LR (1967) Life Zone Ecology. Tropical Science Center, San José Iverson LR, Dale ME, Scott CT, Prasad A (1997) A GIS-derived integrated moisture index to predict forest composition and productivity in Ohio forests. Landsc Ecol 12:331–348 Jiménez-Valverde A, Lobo JM (2007) Threshold criteria for conversion of probability of species presence to either-or presence–absence. Acta Ecol 31:361–369 1 3 1202 Biodiversity and Conservation (2019) 28:1173–1204 Joosten H, Clarke D (2002) Wise use of mires and peatlands—background and principles including framework for decision-making. International Mire Conservation Group, International Peat Society, Greifswald, p 304 Kadmon R, Shmida A (1990) Spatiotemporal demographic processes in plant populations: an approach and a case study. Am Nat 135:382–397 Laaka-Lindberg S, Anttila S, ja Syrjänen K (2009) Suomen uhanalaiset sammalet. Suomen ympäristökeskus, Helsinki, Ympäristöopas, p 347 Laitinen J (2008) Vegetational and landscape level responses to water level fluctuations in Finnish, mid- boreal aapa mire – aro wetland environments. Acta Universitatis Ouluensis. A, Scientiae rerum natu- ralium 513 Lassueur T, Joost SP, Randin CF (2006) Very high resolution digital elevation models: do they improve models of plant species distribution? Ecol Model 198:139–153 Latimer W (2009) Assessment of biodiversity at the local scale for environmental impact assessment and land-use planning. Plan Pract Res 24(3):389–408 Legendre P (1993) Spatial autocorrelation: trouble or new paradigm? Ecology 74:1659–1673 Lehmann A, Overton JM, Austin MP (2002) Regression models for spatial prediction: their role for bio- diversity and conservation. Biodivers Conserv 11:2085–2092 Lemes P, Loyola RD (2013) Accommodating species climate-forced dispersal and uncertainties in spa- tial conservation planning. PLoS ONE 8:e54323 Lindborg R, Eriksson O (2004) Historical landscape connectivity affects present plant species diversity. Ecology 85:1840–1845 Liu C, Berry PM, Dawson TP, Pearson RG (2005) Selecting thresholds of occurrence in the prediction of species distributions. Ecography 28:385–393 Liu C, White M, Newell G (2013) Selecting thresholds for the prediction of species occurrence with presence-only data. J Biogeogr 40:778–789 Loiselle BA, Howell CA, Graham CH, Goerck JM, Brooks T, Smith KG, Williams PH (2003) Avoiding pitfalls of using species distribution models in conservation planning. Conserv Biol 17:1591–1600 Lugo AE, Brown SL, Dodson R, Smith TS, Shugart HH (1999) The Holdridge life zones of the conter- minous United States in relation to ecosystem mapping. J Biogeogr 26:1025–1038 Mateo RG, Felicísimo AM, Pottier J, Guisan A, Muñoz J (2012) Do stacked species distribution models reflect altitudinal diversity patterns? PLoS ONE 7:1–9 Mateo RG, Estrella M, Felicísimo ÁM, Muñoz J, Guisan A (2013) A new spin on a compositionalist predictive modeling framework for conservation planning: A tropical case study in Ecuador. Biol Conserv 160:150–161 Merow C, Smith MJ, Silander JA (2013) A practical guide to MaxEnt for modeling species’ distributions: what it does, and why inputs and settings matter. Ecography 36:1058–1069 Milanovicha JR, Petermanb WE, Barrettc K, Hopton ME (2012) Do species distribution models predict species richness in urban and natural green spaces? A case study using amphibians. Landsc Urban Plan 107:409–418 Natural Resources Institute Finland (2013) File service for publicly available data. Natural Resources Institute Finland. http://kartt a.luke.fi/opend ata/valin ta-en.html Newbold T (2010) Applications and limitations of museum data for conservation and ecology, with par- ticular attention to species distribution models. Prog Phys Geogr 34:3–22 Parkkari M, Parviainen M, Ojanen P, Tolvanen A (2017) Spatial modelling provides a novel tool for esti- mating the landscape level distribution of greenhouse gas balances. Ecol Ind 83:380–389 Parviainen M, Luoto M, Ryttäri T, Heikkinen RK (2008) Modeling the occurrence of threatened plant species in taiga landscapes: methodological and ecological perspectives. J Biogeogr 35:1888–1905 Parviainen M, Marmion M, Luoto M, Thuiller W, Heikkinen RK (2009) Using summed individual spe- cies models and state-of-the-art modeling techniques to identify threatened plant species hotspots. Biol Conserv 142:2501–2509 Pearce J, Ferrier S (2000) Evaluating the predictive performance of habitat models developed using logistic regression. Ecol Model 133:225–245 Pearson R, Terence TP, Liu C (2004) Modeling species distributions in Britain: a hierarchical integra- tion of climate and land-cover data. Ecography 27:285–298 Phillips SJ, Dudík M (2008) Modeling of species distributions with Maxent: new extensions and a com- prehensive evaluation. Ecography 31:161–175 Phillips S, Anderson R, Schapire R (2006) Maximum entropy modeling of species geographic distribu- tions. Ecol Model 190:231–259 1 3 Biodiversity and Conservation (2019) 28:1173–1204 1203 Pirinen P, Simola H, Aalto J, Kaukoranta J-P, Karlsson P, Ruuhela R (2012) Climatological statistics of Fin- land 1981-2010. Finnish Meteorological Institute, Reports 2012:1, Finnish Meteorological Institute, p Prendergast JR, Quinn RM, Lawton JH, Eversham BC, Gibbons DW (1993) Rare species, the coincidence of diversity hotspots and conservation strategies. Nature 365:335–337 Pulliam HR (2000) On the relationship between niche and distribution. Ecol Lett 3:349–361 Pulliam HR, Danielson B (1991) Sources, sinks, and habitat selection: a landscape perspective on popula- tion dynamics. Am Nat 137:50–66 Ramsar Convention Secretariat (2013) The Ramsar convention manual: a guide to the convention on wet- lands (Ramsar, Iran, 1971), 6th edn. Ramsar Convention Secretariat, Gland Rassi P, Hyvärinen E, Juslén A, Mannerkoski I (eds) (2010) The 2010 red list of finnish species. Ympäristöministeriö & Suomen ympäristökeskus, Helsinki, p 685 Rondinini C, Wilson KA, Boitani L, Grantham H, Possingham HP (2006) Tradeoffs of different types of species occurrence data for use in systematic conservation planning. Ecol Lett 9:1136–1145 Ryttäri T, Kalliovirta M, Lampinen R (2012) Suomen uhanalaiset kasvit, Tammi, p 384 Similä M, Aapala K, Penttinen J (eds.) (2014) Ecological restoration in drained peatlands—best practices from Finland. Metsähallitus—Natural Heritage Services, Finnish Environment Institute SYKE, p 84 Skov F, Svenning J-C (2004) Potential impact of climate change on the distribution of forest herbs in Europe. Ecography 27:366–380 Spatial Foresight, SWECO, ÖIR, t33, Nordregio, Berman Group, Infyde (2017) Bioeconomy development in EU regions. Mapping of EU member states’/regions’ research and innovation plans and strategies for smart specialisation (RIS3) on bioeconomy for 2014–2020 Svenning JC, Normand S, Kageyama M (2008) Glacial refugia of temperate trees in Europe: insights from species distribution modelling. J Ecol 96:1117–1127 Swets K (1988) Measuring the accuracy of diagnostic system. Science 240:1285–1293 Thuiller W, Araújo MB, Lavorel S (2003) Generalized model vs. classification tree analysis: predicting spa- tial distributions of plant species at different scales. J Veg Sci 14:669–680 Thuiller W, Albert C, Araújo MB, Berry PM, Cabeza M, Guisan A, Hickler T, Midgley GF, Paterson J, Schurr FM, Sykes MT, Zimmermann NE (2008) Predicting global change impacts on plant species’ distributions: future challenges. Persp Plant Ecol, Evol Syst 9:137–152 Trotta-Moreu N, Lobo JM (2010) Deriving the species richness distribution of geotrupinae (Coleop- tera: Scarabaeoidea) in Mexico from the overlap of individual model predictions. Environ Entomol 39:42–49 Ulvinen T (2001) Itämerenvihvilä, valkoyökönlehti ja kenosammal Tervolan letoilla (PeP). Lutukka 17:120–126 Underwood JG, D’Agrosa C, Gerber LR (2010) Identifying conservation areas on the basis of alternative distribution data sets. Conserv Biol 24:162–170 Virtanen R, Luoto M, Rämä T, Mikkola K, Hjort J, Grytnes J-A, Birks HJB (2010) Recent vegetation changes at the high-latitude tree line ecotone are controlled by geomorphological disturbance, produc- tivity and diversity. Glob Ecol Biogeogr 19:810–821 Wiens JJ, Graham CH (2005) Niche conservatism: integrating evolution, ecology, and conservation biology. Annu Rev Ecol Evol Syst 36:519–539 Wiens JA, Hayward GD, Holthausen RS, Wisdom MJ (2008) Using surrogate species and groups for con- servation planning and management. Bioscience 58:241–252 Williams PH, Gibbons DW, Margules CR, Rebelo AG, Humphries CJ, Pressey RL (1996) A comparison of richness hotspots, rarity hotspots and complementary areas for conserving diversity using British birds. Conserv Biol 10:155–174 Wisdom, M., Hayward, G., Shelly, S., Hargis, C., Holthausen, D., Epifanio, J., Parker, L. and Kershner, J. 2001. Using species groups and focal species for assessment of species at risk in forest planning. Flag- staff (AZ), US Department of Agriculture Forest Service, Rocky Mountain Research Station Wisz MS, Walther BA, Rahbek C (2007) Using potential distributions to explore determinants of Western Palaearctic migratory songbird species richness in sub-Saharan Africa. J Biogeogr 34:828–841 Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. 1 3 1204 Biodiversity and Conservation (2019) 28:1173–1204 Affiliations 1 2 2 1 1 Miia Saarimaa  · Kaisu Aapala  · Seppo Tuominen  · Jouni Karhu  · Mari Parkkari  · 1,3 Anne Tolvanen Kaisu Aapala kaisu.aapala@ymparisto.fi Seppo Tuominen seppo.tuominen@ymparisto.fi Jouni Karhu jouni.karhu@luke.fi Mari Parkkari mari.parkkari@luke.fi Anne Tolvanen anne.tolvanen@luke.fi Natural Resources Institute Finland, Oulun Yliopisto, Paavo Havaksen tie 3, 90014 Oulu, Finland Finnish Environment Institute, Latokartanonkaari 11, 00790 Helsinki, Finland Department of Ecology and Genetics, University of Oulu, Oulu, Finland 1 3

Journal

Biodiversity and ConservationSpringer Journals

Published: Feb 22, 2019

There are no references for this article.