Modeling desirable futures at local scale by combining the nature futures framework and multi-objective optimization

Chihiro Haga; Marimi Maeda; Wataru Hotta; Takanori Matsui; Masahiro Nakaoka; Junko Morimoto; Hideaki Shibata; Shizuka Hashimoto; Osamu Saito; Sana Okayasu; HyeJin Kim; Garry Peterson

doi:10.1007/s11625-023-01301-8

Modeling desirable futures at local scale by combining the nature futures framework and multi-objective optimization

Haga, Chihiro; Maeda, Marimi; Hotta, Wataru; Matsui, Takanori; Nakaoka, Masahiro; Morimoto, Junko; Shibata, Hideaki; Hashimoto, Shizuka; Saito, Osamu; Okayasu, Sana; Kim, HyeJin; Peterson, Garry 2023-03-10 00:00:00 Envisioning positive scenarios that recognize the multiple values of nature is fundamental for designing transformative changes in local socio-ecological systems. This study developed a protocol with three specifications for operationalizing the Nature Futures Framework (NFF) in a landscape scenario analysis using a multi-objective optimization framework composed of: (1) exploring nature-positive futures, (2) seeking alternative pathways for targets satisfying visions of plural values, and (3) screening key direct drivers to achieve the targets. This research conducted a case study of a rural landscape in northeastern Japan. First, 110 strategies of landscape management options were simulated from 2015 to 2100 using a forest landscape model, LANDIS-II. The simulation developed a data frame of four integrated indicators of the NFF values for each year and strategy. Second, nature-positive strategies were screened using the common values. Pareto optimal strate- gies were then identified to obtain equally good solutions. Finally, the key response options to achieve good nature-positive futures were identified using decision tree analysis. Our protocol identified (1) multiple, but few nature-positive and Pareto optimal strategies that satisfied NFF visions, (2) nature-positive, but not Pareto optimal strategies, and (3) non-nature-positive strategies. In most Pareto optimal strategies, the maximized value perspectives changed over time. Our protocol also iden- tified key response options to achieve three different NFF value perspectives in the case study area: (1) clear or selective cutting in forestry and (2) solar PV installation on abandoned pastureland in agriculture and energy sectors. We discussed the implication for local landscape management, localizing NFF narratives to develop future scenarios and modeling prac- tice of NFF. The protocol does not depend on a specific model and indicator. Thus, our scalable protocol can be applied to scenarios and model practices in any region to support envisioning plausible, feasible, and positive futures, and designing future stakeholder collaboration. Keywords Scenario analysis · Forestry · Agriculture · Forest landscape model · Nature positive Handled by Carolyn Lundquist, University of Auckland, New Zealand. * Chihiro Haga The University of Tokyo, Bunkyo-Ku, Japan chihiro.haga@ge.see.eng.osaka-u.ac.jp Institute for Global Environmental Strategies, Hayama, Japan 1 7 Osaka University, 2-1, Yamadaoka, Suita, PBL Netherlands Environmental Assessment Agency, Osaka M3-405565-0871, Japan Den Haag, The Netherlands 2 8 NEWJEC Inc., Osaka, Japan German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany Hokkaido University, Sapporo, Japan Stockholm University, Stockholm, Sweden Hokkaido University, Akkeshi, Japan Vol.:(0123456789) 1 3 Sustainability Science by (1) expressing the value of nature from three perspec- Introduction tives: Nature for Nature (NN) (intrinsic value), Nature for Society (NS) (instrumental value), and Nature as Culture/ A holistic and integrated approach is essential for prevent- One with Nature (NC) (relational value); (2) identifying a ing biodiversity decline (IPBES 2019; Leclère et al. 2020; set of solutions rather than a specific solution to a desired Secretariat of the Convention on Biological Diversity future vision; (3) including social and ecological system 2020). Maintaining ecological systems in good condition interactions; and (4) application at the local scale (Pereira is essential to achieving sustainable development goals et al. 2020). (SDGs) because it is a prerequisite for a sustainable social Several studies have conducted qualitative scenario analy- system that avoids overshooting and shortfalls (Raworth ses of ecosystem services using the NFF by identifying the 2017; Rockström et al. 2009). Countermeasures to climate key direct and indirect drivers of biodiversity loss (Rosa change and biodiversity degradation have been considered et al. 2020). Sarkar et al. (2020) reviewed the drivers of the in individual fields such as the Intergovernmental Panel on changes in tropical wetlands in India and Brazil. They found Climate Change (IPCC) and Intergovernmental Science- common drivers of agricultural intensification, infrastructure Policy Platform on Biodiversity and Ecosystem Services development, and lack of concrete policies, while some driv- (IPBES) (Pörtner et al. 2021). However, in recent years, ers differed between countries. Moreover, 20 indicators of nature-based solutions have become a central issue in cli- wetland values were classified into three value perspectives mate change mitigation and adaptation (Chausson et al. of NFF; there were both common indicators among value 2020; Seddon et al. 2020, 2021). In 2021, the IPCC and perspectives and specific indicators for each value perspec- IPBES jointly reported that climate change and biodiver- tive (Sarkar et al. 2020). Siqueira-Gay et al. (2020) described sity problems are inextricably connected (Pörtner et al. the drivers of current deforestation in the Amazon using 2021). Degradation of ecological systems is a wicked the Driver, Pressure, State, Impact, and Response (DPSIR) problem; thus, identification of critical levers and lever - framework, and qualitatively envisioned the changes in age points to trigger the transformation of the entire socio- nature’s contributions to people (NCP) under alternative ecological system is an urgent and important task (Abson future scenarios. Resende et al. (2020) also described the et al. 2017; Chan et al. 2020; Meadows 1999). drivers of changes in water quality and quantity using the A place-based scenario analysis that considers inter- DPSIR framework, and qualitatively evaluated the future actions between site-specific socio-ecological systems is direction of a business as usual (BaU) and four alternative helpful in designing local transformations (TNFD 2022; scenarios. Lembi et al. (2020) graphically visualized three Wu 2013, 2021). Therefore, a large number of previous alternative future socio-ecological systems in urban areas in studies have conducted scenario analyses of landscape and Brazil by applying the NFF concept. All these studies quali- ecological management at local scales worldwide (Ren tatively draw the interlinkage between indirect drivers, such et al. 2019; Shifley et al. 2017; Verburg et al. 2004). How - as population and socio-economic conditions, direct drivers, ever, the comparison and integration of knowledge from such as land use and land cover (LULC) change, natural each scenario analysis were difficult because the scenarios resource management, climate change, and natural ecosys- were developed for each local-specific context (Rosa et al. tems. Narratives of SSP scenarios in climate change research 2017). Shared socio-economic pathways (SSPs) are fre- have been translated into quantitative socio-economic status quently used in biodiversity studies. However, a global using gridded population models and LULC change models assessment report demonstrated that even SSP1-RCP2.6, (Chen et al. 2020; Shi et al. 2021). Thus, establishing a sce- which is the most sustainable scenario, cannot halt biodi- nario simulation methodology that quantitatively translates versity degradation (IPBES 2019). NFF narratives using models that simulate the impacts of a Thus, to explore nature-positive futures (CBD 2021), wide variety of drivers is a key task. the IPBES task force on scenarios and models developed There are two expected tasks for NFF modeling at the a framework for designing multiscale and integrative local scale. One is to simulate all the key indirect and nature–people scenarios: the Nature Futures Framework: direct drivers to represent the holistic transformation of a flexible tool to support the development of scenarios socio-ecological systems, considering the diversity of and models of desirable futures for people, nature, and value, learning, and ecosystem management activities Mother Earth (henceforth the NFF) (Schoolenberg et al. between stakeholder agents (Kim et al. 2021; Pereira 2020; Okayasu et al. 2019a, b; Pereira et al. 2020; Rosa et al. 2020). This task requires the integration of land- et al. 2017). This is a framework for scenario development scape change models and socio-economic dynamics mod- that enables comparisons between case studies of desir- els and the implementation of stakeholder agents into the able futures, while also considering the local context in integrated model (Chopin et al. 2019). The other task is collaboration with stakeholders. The NFF is characterized to inform interlinkages between the three different value 1 3 Sustainability Science perspectives of the NFF to strengthen and catalyze col- the interlinkages between the three value perspectives of laboration between stakeholders (Pereira et al. 2020). the NFF. This information is useful for considering key leverage Specification 3. Screening key direct drivers to achieve points and levers in a landscape (Pasalodos-Tato et al. targets: This protocol identifies the conditions of direct driv - 2013; Pereira et al. 2020; Scheller 2020). Thus, this study ers to achieve all pathways identified in Specification 2. focused on the second task, in which the existing land- This study conducted an NFF modeling case study in scape change models can contribute to developing a pro- Japan to assess the utility of the protocol. In this paper, we tocol for modeling the NFF in a landscape. The following discuss whether our protocol can inform collaborations section describes the specific requirements of the devel- among stakeholders with diverse values; we also discuss oped protocol. research needs to expand the applicability of NFF in a cer- Specification 1. Exploring nature-positive futures: tain landscape context. This protocol explores solution sets that satisfy plausible, nature-positive, and NFF visions using landscape change models, which can simulate various human interventions. Materials and methods Specification 2. Seeking alternative pathways for targets satisfying visions of plural values: This protocol visual- This study modeled the NFF-based scenarios by applying izes multiple alternative pathways to reach an agreed-upon the concept of multi-objective optimization and a land- target in the NFF state space. The protocol also visualizes scape change model to explore desirable futures, seek tar- gets in the NFF state space, and screen response options Fig. 1 Overall protocol developed by this study. The right bottom figure was modified from the description of scenarios by Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) (https:// ipbes. net/ scena rios) 1 3 Sustainability Science for reaching these targets in the Bekambeushi River Site description watershed in northeastern Japan (Fig. 1). First, a land- scape change model simulated changes in NFF indicators The Bekambeushi River watershed in northeastern Japan toward 2100 under plausible landscape management strat- was selected as the case study area (Fig. 2A). The total egies (hereafter, plausible strategies) (Step 1). Second, area of the watershed is 700 km , with a small difference strategies that satisfied the nature-positive constraints in elevation from 0 to 141 m (GSI 2019). The current (hereafter, nature-positive strategies) were extracted (Step monthly mean air temperature ranges from − 8 to 20 °C, 2). Third, Pareto optimal strategies and dominated strate- and the annual precipitation is 1200 mm (Esgf-CoG 2017). gies among NN, NS, and NC indicators were identified Forest and pasturelands cover 70% and 20% of the water- to embed them in the NFF state space (Kim et al. 2021) shed, respectively (Fig. 2B; Biodiversity Center of Japan (Step 3–1). Then, our protocol identified how values of 2017). The Bekambeushi River wetland, located in the each nature-positive strategy transitioned in the NFF state middle of the watershed, was listed in the Ramsar Con- space toward 2100 (Step 3–2). Finally, the key response vention in 1993 (Akkeshi Town 2019a). In the national options to reach a certain condition in the NFF state space forest in the northern areas, the dominant species for tree were identified (Step 4). All analyses and visualizations plantation is Larix kaempferi (Lamb.) Carrière. In the for this process were conducted using R v4.1.2 (R Core private forest in the southern areas, the dominant species Team 2021) and Julia v1.6 (Bezanson et al. 2017). are Sakhalin fir (Abies sachalinensis (F. Schmid)] Mast.) for tree plantation and a natural mixed forest of Sakha- lin fir and Japanese oak (Quercus crispula Blume). The Fig. 2 Description of the study area. a Location of the Bekambeushi River watershed (red dot), b vegetation distribution, and c zoning of the study area 1 3 Sustainability Science Bekambeushi River flows from the north into Lake Akke - Step 1. Developing an NFF indicator data frame shi, a 32 km brackish water lake, connecting Akkeshi using a model Bay and the Pacific Ocean through a narrow ~ 400 m-wide channel. Upstream land use changes in the watershed Model description affect the water quality and productivity of the lake and bay through nutrient concentration changes in the river This study simulated LULC change and vegetation succes- water (Nakaoka et al. 2018). sion using the LANDIS-II model (Scheller et al. 2007) under The main industries in the watershed are dairy farming, different forest and pastureland management scenarios at a tourism, forestry in the national and private forests, and 100 m resolution under climate change. LANDIS-II is one fisheries and aquaculture in the bay, lake, and offshore area of the leading forest landscape models (e.g., Petter et al. (Akkeshi Town 2018). The watershed stakeholders include 2020; Scheller et al. 2011; Shifley et al. 2017 ; Thompson local ecosystem managers, such as foresters and farmers, et al. 2016) that has already been localized in this area, as and those who use local ecosystems, such as citizens, local described previously. A significant difference from the other governments, and tourists (Tajima et al. 2021). One of the land use change models, which simulate discrete state space stakeholders’ concerns is the impact of LULC changes in the transitions (e.g., Estman 2022), is that LANDIS-II simu- watershed on river water quantity and quality, which may lates the impact of anthropogenic interventions on vegeta- affect fundamental industries such as fisheries and aquacul- tion dynamics in each grid cell. LANDIS-II is a raster-based ture. Moreover, the total residential population in the water- spatially explicit model that represents an entire landscape shed was 8604 in 2010 and is projected to decrease to 4980 as a collection of grid cells and computes the vegetation by 2050 (NIPSSR 2018). This depopulation has affected dynamics of each plant cohort in each grid cell. The model local ecosystem management. The areas of clear-cutting and calculates spatially explicit plant cohort recruitment due to thinning of larch and Sakhalin fir for timber and pulp pro- seed dispersal from surrounding grids, natural disturbances, duction have decreased in recent years (Hokkaido Prefecture forest management, growth, competition, mortality, above- 2019). Local administrative documents reflect the concern ground biomass, and ecosystem carbon and nitrogen cycles that the abandonment of pastureland will increase because of influenced by climate change. The model requires initial for - the declining population (Akkeshi Town 2019b). However, est vegetation and climate conditions, life history, functional other administrative documents plan to maintain or expand data by tree species, and landscape management options. dairy farming (Akkeshi Town 2014). As these contrasting The model outputs time-series changes in biomass by tree plans suggest, it is uncertain whether the future direction of species, carbon sequestration rate, and timber and pasture land use management will be toward land sharing or land production. This study used LANDIS-II NECN Succession sparing (Immovilli and Kok 2020). extension v6.4 (Scheller et al. 2011) (hereafter, NECN v6.4), The study area has been subjected to multiple scenario which can represent responses to climate change according analyses. Tajima et al. (2021) conducted a questionnaire sur- to the traits of each plant species. The calculation process vey of residents and found that the objects supporting local is detailed both in Scheller et al. (2011) and the GitHub identity differed depending on the stakeholders. Objects repository of the simulation model (https:// landis- ii- found related to marine ecosystems, such as fisheries, oysters, ation.gi thub. io/ Extens ion- NECN- Succes sion/). A previous kelp, clams, and Pacific saury, produced capitals, such as study has described the parameterization, calibration, and the Akkeshi Bridge and roadside stations, and landscapes validation of the model (Haga et al. 2019, 2020). The list of and livelihoods, such as dairy farming, were found to sup- simulated species is shown in Table S1 in the Supplementary port local identities (Tajima et al. 2021). On the other hand, Materials. modeling studies have shown that changes in local land use Table 1 describes the major direct drivers and response and forest management intensities as populations decline options, i.e., human interventions, which could be simulated would alter ecosystem services, such as vegetation distri- using the LANDIS-II model. The LANDIS-II Biomass Har- bution, carbon fixation, wood supply, and pasture supply vest extension v4.1 calculates harvesting methods and tim- using the LANDIS-II model (Haga et al. 2019). Moreover, ing for plant cohorts at the stand level and harvesting plans another study has suggested that the introduction of renew- based on zoning at the landscape level. Clearing, thinning, able energy as a climate change mitigation measure would and selective cutting can be simulated by setting the type and decrease the habitat of the mountain hawk eagle and Blak- amount of trees to be harvested and the timing of harvesting iston's fish owls within the area (Haga et al. 2020). Based on in each forest stand. Even in pasturelands, model users can these studies, this study translated the stakeholder’s prefer- control the timing of vegetation succession owing to seed ences (Tajima et al. 2021) into an NFF perspective and used dispersal from the surrounding area. the LANDIS-II model to visualize the interlinkages, such as We used the RCP2.6, which is expected to minimize the trade-offs or synergies, among different values. impacts of climate change on biodiversity, because the NFF 1 3 Sustainability Science Table 1 Direct drivers and Ecosystems Direct drivers Player Response options response options, which LANDIS-II can simulate. The Forest Landscape level: Forest manager Promotion of sustainable response options refer to the Zoning Local government forest management PANCES Project (PANCES Stand level: Forest owner Formulation of forest Project 2022) Clear-cut Power generation company zoning Selective cutting Promotion of plantation Thinning forest management Planting Guidance from a single- Site preparation storied forest to a multi- Rotation period storied forest Guidance to a natural forest Introduction of forest environment tax to man- age abandoned forests Support for new employ- ment in forestry and training of leaders Promotion of utilization of woody biomass Pastureland Continuous management Farmer Promotion of direct pay- Pastureland abandonment Local government ment system to agricul- Solar PV installation on Land owner tural workers abandoned pastureland Power generation company Consolidate and increase Plantation of trees on aban- the scale of agricultural doned pastureland land Promotion of mechaniza- tion in the agricultural sector Diversion of abandoned cultivated land (natural regeneration or tree plantation) Promotion of solar PV installation assumes desirable visions of future human–nature relation- and regional forest plans (Akkeshi Town 2017; Hokkaido ships (Pereira et al. 2020). The same bias-corrected RCP2.6 Prefecture 2017a, 2017b, 2017c; MAFF 2017). The current future climate data of the CMIP5 MRI-CGCM3 model were conservation practices on the current protected forests for used, as in previous studies (Haga et al. 2019, 2020). Under disaster prevention and mitigation and wildlife conserva- these climate conditions, the average temperature in the tion will be continued until 2100. Other forests intended for region would increase by 1.4 °C by 2100 (Haga et al. 2019, timber production were designated as management areas. 2020). The remaining environmental conditions were the The pasturelands were zoned into two areas based on geo- same as those described by Haga et al. (2020). The simula- graphical, topographical, and social factors: the conserva- tion period was 85 years, from 2015 to 2100, with time steps tion areas to be guided to the natural forest after abandon- for material cycling set to one month and seed dispersal and ment, and management areas to support instrumental or ecosystem management set to one year. relational values. First, we identified pasturelands that were topographically and socially difficult to manage, likely to Designing plausible direct human interventions to nature be abandoned, and used these to simulate the expansion of abandoned land (Haga et al. 2020). For pasturelands aban- First, in all plausible strategies, conservation areas where doned in management areas, a grid search was conducted human intervention should be avoided and management to determine whether solar PV should be introduced after areas where ecosystem management is conducted were abandonment or whether biomass energy should be used by determined according to the current policy of forest and converting pastures into forests after abandonment. Solar pastureland management in the area (Fig. 2C). Forests were PV was introduced from abandoned pasturelands located far first zoned into conservation areas and management areas from the forest edge. See Haga et al. (2020) for a detailed by forest stands, referring to the forest register database algorithm for abandoned pastureland expansion. 1 3 Sustainability Science Second, combinations of plausible forest and pasture- they were considered to have the potential to become wet- land management in the management areas, i.e., plausible lands (Kaneko et al. 2008; Morimoto et al. 2017). strategies (Fig. 3 and Supplementary Materials S2), were designed. Individual forest and pastureland management Indicator selection by translating visions and values of NFF options are plausible and difficult to determine superior or to the local context inferior relationships from the viewpoint of plural values of nature. Therefore, we conducted a grid search over the We assumed that whether the study area is nature posi- 110 combinations of plausible strategies comprising two tive can be evaluated by the condition of the fundamental contrasting groups: 21 land-sparing-oriented strategies, landscape structure and ecosystem functioning. Thus, we which decrease timber production and maintain or rewild selected five common indicators to quantitatively evaluate pastureland as the population decreases (Fig. 3 orange four values (Table 2). The visions and values of each vertex and Table S2-1), and 89 land-sharing-oriented strategies, of the NFF were translated into the local context and mapped which continue landscape management despite depopu- to variables that could be output by the LANDIS-II model lation to maintain a supply of NCPs from the landscape (Table 2 and Supplementary Materials S3). In this study, (Fig. 3 green, Tables S2-2, and S2-3). The forest manage- we set up common visions and values to evaluate nature- ment in this study refers to standard practices as follows positive criteria, which is the basic idea of NFF, and spe- (Akkeshi Town 2017; Hokkaido Prefecture 2017c). The cific visions and values to enable the quantitative evaluation older forest stand that reached the standard harvest period of the value each vertex aims for. First, we assumed that was harvested first. We limited the maximum clear-cut the nature-positive state recovered fundamental landscape area of each operation to 20 ha for biodiversity conserva- structures and ecosystem functions by the 2030s and 2050s tion, referring to the standard practice. To conserve and compared to the current level (Locke et al. 2021). For this expand riparian forests, abandoned pastureland within purpose, we calculated the Dissimilarity-based Satoyama 300 m of the river was rewilded by natural regeneration. In Index (DSI) (Yoshioka et al. 2017) and proportion of non- addition, pasturelands that were once wetlands in the past artificial landscape area to evaluate landscape structure, net were not used as a source of renewable energy because ecosystem productivity (NEP) as regulating NCP, vegetation biomass, and its diversity (Table 2). Fig. 3 Combinations of forest and pastureland management strategies 1 3 Sustainability Science Table 2 Indicators for evaluating the common value and three NFF values. The Supplementary Materials describe the calculation of each indica- tor Category Vision Value Indicator which can be calcu- Stakeholder (Tajima et al. lated from LANDIS-II output 2021) Common Fundamental landscape char- Landscape structure Dissimilarity-based Satoy- Forest, land, and marine acteristics and ecosystem ama Index (DSI) (Yoshioka function will be maintained et al. 2017) Landscape structure Area of natural landscape Forest, land, and marine (ha) Regulating NCP Net Ecosystem productivity Forest, land, and marine –1 (NEP) (g-C y ) Biomass of plant species Total aboveground biomass Forest, land, and marine of trees in the watershed (g-dry weight) Diversity of plant species Simpson’s diversity index of Forest, land, and marine aboveground biomass of trees in the watershed Nature for Nature Preserve nature's diversity Habitat of fauna Habitat suitability index of Forest and functions Blakiston’s fish owl (Yoshii et al. 2018) Habitat suitability index of the mountain hawk eagle (Itoh et al. 2012) Amount of flora Aboveground biomass of Forest native tree species (g-dry weight) (Editorial Commit- tee of History of Akkeshi Town 2012) Nature for Society Maintain or maximize instru- Yield of agriculture and Pasture yield (g-dry weight Forest, land, and outside –1 mental values of nature forestry y ) the area Timber yield (g-dry weight Forest, land, and outside –1 y ) the area Energy production Energy production of woody Forest, land, marine, and –1 biomass (J y ) outside the area Energy production of solar –1 PV (J y ) Nature as Culture/ Maintain the livelihoods and Supporting local identities Proportion of the natural Forest and land One with Nature the natural landscape which and learning and inspiration landscape in the viewshed support local identity and from residential areas and culture roads Eco-tourism (physical and Proportion of the natural Land psychological experiences) landscape in the viewshed from tourism resources: canoe route, local cultural heritage, and natural park Connectivity of dairy farming Biomass of riparian forests Land and marine and aquaculture (regulation (g-dry weight), which regu- of freshwater and coastal lates water quality water quality) Scenario-specific indicators were introduced by con- NN was evaluated using the habitat suitability index (HSI) sidering each of the three value perspectives of the NFF. for rare representative species in the area and native plant The NN primarily aims to preserve biodiversity and eco- species biomass (Table 2). For the fauna, Blakiston's fish system functions (Pereira et al. 2020). In this study, this owl (Ketupa blakistoni blakistoni) (Yoshii et al. 2018), narrative was translated as encouraging a rewilding by which uses riparian forests, and the mountain hawk eagle withdrawing from human intervention in forest and pas- (Spizaetus nipalensis orientalis), which uses forest edges tureland as the population declined. Thus, the value of (Itoh et al. 2012), were selected. 1 3 Sustainability Science NS and NC primarily aim to maintain the provision of 2020 (CBD 2021). Therefore, we herein define “nature-pos- NCPs: instrumental values in NS and relational values in itive strategies” as strategies that indicate higher values of NC (Immovilli and Kok 2020; Pereira et al. 2020). Thus, common indicators in both the 2030s and 2050s than the the NS vision in this area was to maximize the supply of mean common indicator value from 2015 to 2029. grass, timber, and renewable energy by managing landscapes through mechanization, efficiency, and land consolidation, Step 3. Embedding nature‑positive strategies to NFF even under population decline. The provision of instru- state space by identifying the Pareto front mental value in NS was evaluated by timber and pasture production and the potential of solar power generation on All vertices of the NFF state space are plausible and desir- abandoned land and renewable energy from woody biomass able futures, although the values differ among them (Kim of woody plants (Table 2). et al. 2021; Pereira et al. 2020). Therefore, combinations However, NC emphasizes the relational values of a region of vertices that can achieve different values simultaneously, (Immovilli and Kok 2020; Pereira et al. 2020). For this rea- which show trade-off relationships, or strike a balance son, NC’s vision is to maintain the local identity supported between different values, can exist. This information about by local livelihoods. A previous study revealed that local the interlinkages between different values of NFF state space identities in this area are provided by fisheries and unique will visualize local problems in landscape management and natural terrestrial landscapes that support tourism and dairy suggest possible collaboration between stakeholders. There- farming (Tajima et al. 2021). Thus, the proportion of the fore, this study visualizes the interlinkages between the val- natural landscape in the viewshed from residential areas ues of each vertex of the NFF state space (Kim et al. 2021) and roads and from tourism resources, such as canoe routes, using the concept of multi-objective optimization for the local cultural heritage sites, and natural parks, and the bio- 2030s, 2050s, and 2090s. mass of riparian forests, which regulate water quality, were All scenario narratives drawn in the NFF are desirable used as indicators. futures (Pereira et al. 2020; IPBES 2022), i.e., there are no Finally, the 15 indicators in Table 2 were summarized superior or inferior relationships between scenarios, but each into four categories to develop a time-series NFF indica- scenario is better than the others in at least one value of tor data frame (four indicators × 110 strategies × 85 years) nature. Therefore, exploring management strategies result- (Fig. 1). First, each individual indicator was min–max nor- ing in such scenarios is equal to exploring Pareto optimal malized timewise. Then, integrated indicator time series by solutions (Benson 2009) in the three-dimensional NFF state categories were calculated as the arithmetic mean of the space (Kim et al. 2021; IPBES 2022). An example is shown min–max normalized indicators for each year and category in Fig. 4. In the NN and NS plane, plots on the colored curve as follows: in Fig. 4A are Pareto optimal strategies: there are no other strategies that increase NN or NS value without decreasing w × Y c,i the other value. The colored curve is called the Pareto front, i=1 c,i,y Yinteg = , c,y ∑ a set of Pareto optimal strategies, which shows trade-offs c,i i=1 between the two values. The other colored plots and black plots in Fig. 4A are dominated strategies: there is at least one where Yinteg are the integrated indicators of category c c,y strategy in which both NN and NS values are better. Repeat- in year y , N is the number of individual indicators within ing this procedure for the NN and NC plane and NC and NS category c , and Y is the min–max normalized individual c,i,y plane as shown in Fig. 4B, C, all nature-positive strategies indicator i within category c in year y . w is the weight of c,i can be classified into two groups: (1) Pareto optimal strate- indicator i within category c . It should be noted that the gies in which there are no other strategies that increase an higher indicator values show better conditions. We assume NFF value without decreasing another value or (2) domi- that each indicator has the same weight ( w = 1) to demon- c,i nated strategies in which there is at least one strategy with strate how our protocol performs. Sensitivity analysis of w c,i higher three NFF values than themselves (Fig. 4D left). are shown in the supplementary materials S7. Next, our protocol classified the Pareto optimal strategies into seven NFF categories to show which NFF perspectives Step 2. Screening nature‑positive strategies using will be achieved (Fig. 4D). The Pareto optimal strategies the simulated common integrated indicator with top 10% values of each integrated indicator (Fig. 4A–C) were classified to be the categories which can achieve the Step 2 screened nature-positive strategies from 110 manage- vertices of NFF triangles: NN category, NS category, and ment strategies using the common integrated indicator. The NC category. The remaining Pareto optimal strategies were post-2020 target aims to stop the net decline in biodiversity classified into the categories that balance multiple value per - loss by 2030 and sufficiently recover by 2050 compared with spectives: NN–NS Pareto category, NS–NC Pareto category, 1 3 Sustainability Science Fig. 4 How to embed the nature-positive strategies (hereinafter, Pareto optimal strategies; small black plots are dominated strategies. nature-positive strategies) into the Nature Futures Framework (NFF) The Pareto optimal strategies are then classified into the NFF catego- state space. All nature-positive strategies are plotted in A NN and NS, ries shown in D B NN and NC, and C NC and NS planes. Large colored plots are the NN–NC Pareto category, and NN–NS–NC Pareto category. over the 2050s and 2090s, depending on ecosystem manage- As a result, the Pareto optimal strategies can be embedded in ment strategies (Table 3). The mean and median values for a three-dimensional state space of NFF integrated indicators each category increased moderately through the 2050s and (hereafter, NFF state space (Kim et al. 2021)). Step 4. Identifying pathways to achieve NFF Table 3 Summary of four integrated indicators by category and time horizon for all 110 strategies category Category Year Mean ± SD Min Median Max Step 3 labels the non-nature-positive strategies, dominated Common Current 0.49 ± 0.05 0.39 0.49 0.68 strategies, and seven NFF categories for each management 2030s 0.50 ± 0.06 0.32 0.50 0.69 strategy defined in Step 1. Therefore, decision tree analysis 2050s 0.48 ± 0.09 0.20 0.48 0.73 was used to visualize the key response options to reach each 2090s 0.59 ± 0.14 0.24 0.61 0.85 NFF category avoiding non-nature-positive conditions. From NN Current 0.31 ± 0.04 0.24 0.30 0.55 the results, the critical indirect drivers, leverage points, and 2030s 0.36 ± 0.05 0.22 0.37 0.61 levers behind them were also discussed. 2050s 0.35 ± 0.08 0.12 0.35 0.62 2090s 0.51 ± 0.13 0.22 0.50 0.83 NS Current 0.32 ± 0.09 0.04 0.32 0.57 Results 2030s 0.32 ± 0.12 0.00 0.31 0.67 2050s 0.38 ± 0.15 0.06 0.35 0.69 Step 1. Summary of the NFF indicators time‑series 2090s 0.42 ± 0.18 0.12 0.40 0.75 data frame NC Current 0.69 ± 0.03 0.57 0.69 0.81 2030s 0.71 ± 0.06 0.49 0.72 0.87 The summary of the four integrated indicators of 110 strate- 2050s 0.66 ± 0.13 0.21 0.70 0.89 gies organized according to time horizon shows that the indi- 2090s 0.7 ± 0.2 0.2 0.8 1.0 cator values diverged into decreasing or increasing trends 1 3 Sustainability Science further through the 2090s. The common, NN, and NC indi- to the 2030s was small, and the mean of the indicator val- cators showed that the maximum value increased through ues decreased from the 2030s to the 2070s (Fig. 5). For the 2090s, whereas the minimum value decreased, indicating individual indicators, DSI was lower than in the nature- that the ecological condition diverged due to human inter- positive strategies, the natural landscape was monotoni- vention. However, the results of NS showed that the mini- cally decreasing, and the other indicators were lower than mum, mean, median, and maximum values monotonically those of the nature-positive strategies (Fig. S4). increased over the 2090s, suggesting an increasing trend in every strategy. Step 3‑1. Identifying Pareto optimal strategies Step 2. Screening nature‑positive strategies by common indicators The nature-positive strategies in the three-dimensional NFF state space show synergies and trade-off relation- By screening 110 strategies in which the common integrated ships among the three value perspectives (Figs. 6, 8). indicator increased from the current to the 2030s and 2050s, The dominated strategies (Fig. 6 small colored plots and 51 nature-positive strategies were identified, which were Fig. 7 small black plots) are nature-positive strategies, but positive in all three time horizons (blue lines) (Fig. 5 and are inferior to the Pareto optimal strategies (Fig. 6 large S4). Our sensitivity analysis also identified 50 ± 10 nature- colored plots and Fig. 7 large colored plots). For all indica- positive strategies (Supplementary Material S7). Nature- tor combinations, the Pareto optimal strategy and the set positive strategies in blue lines show a recovery from the of dominated strategies separated by the year approached current state to the 2030s, followed by an increase through 2100, and a trade-off relationship between the indicators 2090. The DSI, proportion of natural landscape, and total emerged (Fig. 7). The correlation coefficients between (1) tree biomass were high in these nature-positive strategies NN and NS indicators and (2) NS and NC indicators were (Fig. S4). consistently negative and the Pareto front was confirmed, However, as indicated by the gray dashed line, the suggesting a trade-off between these indicators (Fig. 7, majority were labeled as non-nature-positive strategies 1st column). On the other hand, the NN and NC indica- (Figs. 5 and S4): non-positive in the 2030s (N = 2), non- tors were positively correlated for all time horizons, but a positive in the 2050s (N = 10), non-positive in the 2030s Pareto front emerged in the 2090s, suggesting a trade-off and 2050s (N = 23), and non-positive in the three time (Fig. 7, 3rd column). horizons (N = 23). In the non-nature-positive strategies, 6–18% of the nature-positive strategies were Pareto the recovery in the mean indicator values from the current optimal strategies by three time horizons and were classi- fied into six NFF categories (Table 4). Five of the catego- ries were predetermined, as shown in Fig. 4B: the vertex categories of NN, NS, and NC, the NN–NS Pareto cat- egory, and the NN–NC Pareto category (Table 4), whereas the NS–NC Pareto category and NN–NS–NC Pareto cat- egory were not identified. In addition, an unexpected cat- egory, the NN and NC bundle category, was identified that maximized NN and NC indicators simultaneously (Table 4). This category appeared because the NN and NC indicators showed a synergy relationship in the 2030s and 2050s. In the 2090s, the trade-off between NN and NC indicators emerged (Fig. 7, 3rd column); then this NN and NC bundle category was changed into the NC category (Fig. 8). The number of Pareto optimal strategies varied from 20 to 7 over time (Table 4). The sensitivity analy- sis also showed that only 30–60% of the nature-positive strategies were classified as Pareto optimal strategies on average (Supplementary Material S7). The majority were Fig. 5 Common integrated indicator by 110 management strategies. classified as the NN–NS Pareto category. Only one or two Black horizontal line shows the mean value from 2016 to 2029 of strategies were classified as NN, NS, and NC categories business as usual (BaU). Blue lines show nature-positive strategies for each time horizon, suggesting that the feasible solution whose mean values for the 2030s and 2050s are higher than the black space to reach each NFF vertex is very small. horizontal line (N = 51); gray dashed lines show non-nature-positive strategies (N = 59) 1 3 Sustainability Science Fig. 6 Visualization of Nature for Nature (NN), Nature for Society (NS), and Nature as Culture/One with Nature (NC) integrated indicators in the 2030s, 2050s, and 2090s in three-dimensional space. The light blue, red, and blue plots show 51 nature-positive strate- gies in the 2030s, 2050s, and 2090s. Large and small plots denote the Pareto optimal and dominated strategies, respec- tively. All indicators are the minimum at the right bottom origin O (0, 0, 0) and the maxi- mum at the left top (1, 1, 1). The small gray plots show the projection of the 51 strategies on each plane surface of NN, NS, and NC Step 3‑2. Embedding Pareto optimal strategies to the NFF a different nature future (Fig. 9). In all strategies, the NN- integrated indicator increased through the 2030s, plateaued state space through the 2050s, and then showed a monotonically increasing trend (Fig. 6). The HSI of Blakiston’s fish owl Approximately 50% of the nature-positive strategies transi- tioned to other NFF categories or dominated strategies over increased monotonically through 2100, because there was no development or deforestation near the river (Fig. S5-1). The time (Fig. 8A). The NN category in the 2030s transitioned to the dominated strategy after the 2050s. Strategies classi- HSI of the mountain hawk eagle decreased in the long term as forest edges decreased due to the conversion of abandoned fied as NN and NC bundle category in the 2030s and 2050s, which achieved NN and NC simultaneously, were classified pasturelands to forest lands (Fig. S5-1). The total biomass of native trees decreased after 2040, but increased thereafter as NC category in the 2090s. In the NN–NS Pareto category (N = 16), most strategies were inferior after the 2050s. The (Fig. S5-1). The NS-integrated indicator showed that the supply of timber and pasture grasses varied widely from year remainder of the NN–NS Pareto category transitioned to the NN or NN–NC Pareto categories in the 2090s. Later, 12% to year, but in some strategies increased, others remained flat, and in others decreased until 2100 (Fig. 6). The NC- of dominated strategies in the 2030s and 2050s also moved to Pareto optimal strategies. integrated indicator showed an increase in 2030, leveling off in 2040, and a monotonically increasing trend thereafter The NFF classification results for the 2090s were embed- ded in the three-dimensional NFF state space, as shown in (Fig. 6). The total biomass of riparian forests decreased after 2040, but increased thereafter (Fig. S5-1). The proportion Fig. 8B. The Euclidean distances between the vertices of (1) NN and NC, (2) NN and NS, and (3) NS and NC were (1) of natural landscapes decreased with the introduction of renewable energy (Fig. S5-1). Because the NS indicator has 0.28, (2) 0.58, and (3) 0.81, respectively, unlike the equilat- eral triangle shape assumed in Fig. 4A. This indicates that a trade-off relationship with both the NN and NC indicators (Fig. 8), NS indicators were low in strategies where NC was the visions of NN and NC are in close space in the NFF state space, while NS is isolated, reflecting how similar the maximized at a certain time horizon; conversely, NC indica- tors were low in strategies where NN and NS indicators were visions are. high (Fig. 9, left and middle panels). Only three strategies were Pareto optimal strategies in all Step 3‑3. What will alternative nature futures be like? three time horizons (Fig. 9 left panels). The majority of the NFF category transition pattern was that the NN–NS Pareto Twenty-four strategies were Pareto optimal strategies in at least one or more time horizons, with each arriving at category in the 2030s shifted to the dominated strategy in 1 3 Sustainability Science Fig. 7 Visualization of NN-, NS-, and NC-integrated indica- tors in the 2030s, 2050s, and 2090s in two-dimensional space. Large colored and small black plots denote the Pareto optimal and dominated strate- gies in each two-dimensional area, respectively. The colors show the NFF category (Fig. 4b). Black solid lines show the Pareto front. Numbers in each panel show the Pearson correlation coefficient both the 2050s and 2090s (N = 11) (Fig. D1 right panels). Thus, short-, mid-, and long-term milestones for ecosystem In strategies classified as the NS category in the 2030s and management are important for reducing uncertainty. the NN–NS Pareto category in later years, the NC indicator decreased through the 2050s, indicating a significant trade- Step 4. Key direct driver identification off. On the other hand, each indicator was maintained at a moderate level in the NN–NS Pareto category in all time Although the rules for classifying NFF categories differed by horizons. Although this strategy was not a Pareto optimal decade, the forest harvesting method and percentage of solar strategy in terms of the NC indicator, it was almost a bal- PV on abandoned pastureland were critical explanatory vari- anced solution between the three value perspectives. The ables (Fig. 10 and Supplementary Material S6). Here, we show most frequent strategies were NN–NS Pareto in the 2030s, the decision tree for the 2090s, as discussed in Fig. 8B as an and the dominated strategies in the 2050s and 2090s (N = 11) example (Fig. 8). First, the final cutting method of the forest (Fig. 9 right panels). These results suggest that ecosystem stands was divided into two groups: one containing nature- management goals in the short term are not sufficient to positive strategies and the other containing non-nature-posi- manage changes in mid- and long-term ecosystem values. tive strategies (Node 1). The clear-cutting generally resulted 1 3 Sustainability Science Table 4 Summary of the NFF NFF category Number of strategies and mean value of NN-, NS-, and NC-integrated category by time horizon indicators 2030s 2050s 2090s NN N = 1 N = 0 N = 1 (0.42, 0.17, 0.77) (NA) (0.75, 0.35, 0.90) NS N = 2 N = 1 N = 1 (0.36, 0.41, 0.67) (0.35, 0.59, 0.59) (0.51, 0.72, 0.67) NC N = 0 N = 0 N = 1 (NA) (NA) (0.70, 0.13, 0.99) NN and NC bundle N = 1 N = 1 N = 0 (0.59, 0.06, 0.85) (0.61, 0.08, 0.89) (NA) NN–NS Pareto N = 16 N = 5 N = 5 (0.39, 0.29, 0.73) (0.38, 0.40, 0.70) (0.62, 0.50, 0.77) NN–NC Pareto N = 0 N = 0 N = 1 (NA) (NA) (0.71, 0.26, 0.91) Total N = 20 N = 7 N = 9 (0.40, 0.29, 0.74) (0.41, 0.38, 0.71) (0.64, 0.44, 0.81) The numbers indicate the number of strategies belonging to each NFF category. The numbers in parenthe- ses indicate the mean value of integrated indicators within each category for NN, NS, and NC, respectively Fig. 8 a Transition of NFF category of 51 nature-positive strategies from the 2030s to the 2090s. b Embedded NFF classification results of the 2090s in the NFF state space. Colors show the NFF categories in non-nature-positive strategies, whereas the strategies with category (N = 2) (Node 10 left). The rotation period of forestry, gradually withdrawn clear-cutting in forests but maintained as well as the method of reforestation after final cutting and the pastureland were selected as NCs (Node 25). Next, the use expansion speed of abandoned pastureland, all contributed to of woody biomass without introducing solar PV on aban- the divergence of these individual strategies. doned pastureland resulted in the NN–NC Pareto (N = 1), NN (N = 1), and dominated strategy (N = 35) categories (Node 2 left). The strategy with solar PV installed on all abandoned Discussion pasturelands was classie fi d as non-nature-positive strategies (Node 10 right). On the other hand, a mix of solar PV and By simulating a combination of plausible management strat- woody biomass use resulted in 10 nature-positive strategies, egies, this study was able to indicate the range of nature- some of which were NS solutions (N = 1) or the NN–NS Pareto positive futures (Specification 1), while simultaneously 1 3 Sustainability Science Fig. 9 Strategies where one or more of the nature-positive strategies were classified as Pareto optimal solutions in at least one or more time horizons (N = 24). The line colors indicate the transition pattern of NFF categories in the 2030s, 2050s, and 2090s. The left panel represents strategies with Pareto optimal strategies in all years (N = 3), the right panel represents strategies classified as the NN–NS Pareto category in the 2030s, which shifted to the dominated strategy in both the 2050s and 2090s (N = 11), and the middle panel represents the other strategies (N = 10) identifying the desired targets in terms of NFF, pathways An ecosystem management option did not always guaran- to those targets (Specification 2), and key response options tee maximizing or maintaining the same value in all time (Specification 3). This study defined NFF categories at each horizons. vertex and edge of the NFF triangle as Pareto optimal strate- Several landscape models have applied a multi-objective gies between NFF indicators. This definition can contribute optimization framework to visualize Pareto fronts between to the quantification of diverse desirable alternatives in NFF. agricultural production and environmental indicators, as well Moreover, this study found that NFF categories transition as between forest carbon fixation, biomass, and yield (Cole - over time, demonstrating that the values in each management man et al. 2017; Marques et al. 2020). This study shows that strategy were unstable in the future. Some of the nature- NFF can be applied to existing landscape change modeling positive futures were classified as the vertex or edge of the to analyze the interlinkages between different visions con- NFF triangle (Figs. 7, 9), but the number of these strategies strained by biodiversity conservation goals. Our protocol is was only 25 out of 110 (Fig. 9). The sensitivity analysis also scalable because it is not dependent on a specific model or showed the strategies that satisfy the NFF visions are limited landscape. Furthermore, to propose various candidates for regardless of the weight w (Supplementary Material S7). nature future scenarios, it is encouraged to exhaustively test c,i 1 3 Sustainability Science Fig. 10 Decision tree of the 2090s the combinations of ecosystem management. In this study, selective cutting (Akkeshi Town 2017; Hokkaido Prefec- we performed a grid search over 110 pre-defined strategies: ture 2017c). The results of this study suggest that the use of all strategies assumed stakeholders will not change strategy forest management practices to balance timber production toward 2100 to reduce iterations because of the limitations and ecosystem conservation will contribute to the nature- of our computational resources. However, metaheuristic positive future of plantation forests. Among the response optimization, such as evolutionary algorithms, would also options identified in the PANCES Project (see Table 1), the be useful to explore optimal strategies from a huge number following should be considered: promotion of sustainable of combinations (Groot and Rossing 2011). forest management, formulation of forest zoning, promotion In the following, we discuss: (1) implications for local of plantation forest management, guidance from a single- policy design and (2) future scenarios and modeling of the storied forest to a multi-storied forest or natural forest, intro- NFF to support a transformative change in socio-ecological duction of forest environmental tax to manage abandoned systems in local communities. forests, support for new employment in forestry, and training of leaders (PANCES Project 2022). Implications for local agenda setting and policy In terms of pastureland management, the features of the design Pareto optimal strategies were: (1) pastureland is main- tained, or if pastureland is abandoned, it is (2) converted to Can our protocol help identify alternative policy options? forest by natural regeneration, or (3) a mix of solar PV and natural regeneration is installed. Depopulation and outflow The final cutting in forests and utilization of abandoned from rural areas to urban areas (NIPSSR 2018) and the intro- pastureland contributed to the classification of NFF catego- duction of renewable energy to the landscape for decarboni- ries (Figs. 10 and S6), suggesting that nature future sce- zation (METI 2021) are current megatrends in Japan. Thus, narios are oriented by a combination of response options. the priority areas for agricultural landscape management and In Japan, coniferous plantation forests expanded post-WWII renewable energy should be identified, reflecting the local (Tanimoto 2006), but the conversion to broadleaf forests is characteristics and stakeholders' interests. Response options considered throughout the country for biodiversity conser- for agriculture, such as the promotion of a direct payment vation (Forest Agency 2021). The forest plan for this area system, consolidating and increasing the scale of agricul- also describes the conversion to broadleaf forests through tural land, and promoting mechanization in the agricultural 1 3 Sustainability Science sector, should be implemented to maintain the agricultural through sharing central value perspectives among stake- landscape while at the same time carefully designing the holders. conversion of abandoned pastureland (Table 1). Thus, our 2. The supply of energy, which alters the landscape struc- process successfully identie fi d policy implications to achieve ture and influences the NN, NC, and NS indicators, desirable futures that avoid non-nature-positive states by should be community driven to internalize externalities treating the direct drivers among multiple sectors in a land- and telecoupling. Moreover, to minimize the ecologi- scape change model. cal impacts, energy demand within the region should be reduced in line with future population decline and decar- Can our approach draw insights to transform indirect bonization measures. drivers? Our results also demonstrate that balancing intrinsic and Indirect drivers in multiple sectors, such as forestry, dairy instrumental values in the short term does not always guar- farming, the energy industry, and fisheries, influence the antee long-term balanced futures. For example, the major- direct drivers that lead to the divergence of nature futures. ity of strategies classified as the NN–NS Pareto category in Therefore, cross-sectoral cooperation among stakeholders the 2030s resulted in the dominated strategy category after with different value perspectives is a key lever in this study the 2050s. Furthermore, rural areas in Japan are expecting area (IPBES 2019). The vertices of the NN and NC were an increase in migration and related populations, who con- similar in the NFF state space (Table 4 and Fig. 8B) without tinuously have relationships with a specific region against a clear trade-off relationship. The reason why NN and NC depopulation (Hori et al. 2021; Naitou et al. 2019). Thus, indicators did not show the trade-off, i.e., Pareto front, was the structure and relationships of stakeholders in the future the similarity of indicators. In this region, the previous ques- assumed in the NFF might differ from the current situation. tionnaire survey to stakeholders revealed that local identi- For envisioning long-term nature future scenarios, the inclu- ties (Akkeshi-ness) were supported by fisheries, agriculture, sion of future generations' voices is also essential to consider and tourism resources closely related to natural landscapes changes in stakeholders (Rana et al. 2020). evaluated by NN indicators (Tajima et al. 2021). Two habitat suitability indices of NN and two viewshed indicators of Implications for future scenarios and models NC decreased with increasing installation of solar PV (Fig. of the NFF S5). Moreover, the aboveground biomass of native tree spe- cies and that of riparian forests showed similar dynamics Localizing NFF narratives consistent with local policies (Fig. S5). Thus, both NN and NC indicators increased at the same time and did not show the Pareto front (Fig. 7). The When developing NFF visions, values, and indicators at result suggested that it is possible to design win–win coop- local scales, such as those shown in Table 2, mapping to eration among the forest, land, and marine stakeholders in various existing local strategies is necessary. The 2020s is a the region who have different visions: (1) preserve nature’s decade of action; for example, in Japan, local municipalities diversity and functions and (2) maintain the livelihoods and are developing plans to achieve the SDGs, climate change the natural landscape which support local identity and cul- mitigation and adaptation, and biodiversity conservation ture (Table 2). (CAO 2022; MOE 2021, 2022). Since these existing envi- In contrast, the NS indicator showed a clear trade-off rela- ronmental and sustainability policies might overlap with tionship with the NC and NN indicators. Thus, if the stake- the visions and values described in the NFF, a procedure holders of an ecosystem service are outside the region, there for translating them into the NFF context will help a local may be hidden conflicts with the stakeholders in the water - administrator operationalize the NFF. shed. In particular, unplanned renewable energy installations Variety in the definition of a desirable future should also cause conflicts between energy production, local communi- be considered in future research. This study used the narra- ties, and local SDG (Akita et al. 2020; Schumacher 2017; tive of nature positive as a constraint to select positive strate- Schwanitz et al. 2017). Therefore, cross-sectoral cooperation gies, but the degree of recovery is also important. Moreover, among stakeholders inside and outside the region is another there are other candidates for the constraints, such as area- important lever. For example, these levers should work on based conservation measures, decarbonization targets, and the following leverage points (IPBES 2019): other socio-economic targets, such as food self-sufficiency, labor, and financial requirements. Considering local nature 1. Unleash values by discussing local identities and futures with global-, regional-, or national-scale megatrends embracing diverse visions of a good quality of life as boundary conditions is important. The sub-national- scale SSP scenario narratives and their spatially explicit 1 3 Sustainability Science socio-economic dataset can be used as the constraints of impact spatially explicit forest management (Sotnik et al. NFF modeling (Chen et al. 2020; NIES 2021). 2021). Including citizen agents of the target landscape and The indicators used to evaluate NFF visions and values stakeholder agents outside the region will allow for an evalu- are multidimensional (Sarkar et al. 2020; Siqueira-Gay et al. ation of the influence of indirect factors. 2020); thus, it is necessary to develop a local reference indi- On the other hand, connecting with the models of indirect cator set to analyze these trade-offs. Our 15 NFF indicators drivers helps quantify the pathway to transform entire socio- were developed from administrative planning documents and ecological systems. For example, the discussion of socio- previous studies that can be evaluated by LULC, biomass, economic changes in climate change research is supported and other outputs of landscape change models. The chal- by integrated assessment models that connect global climate lenge is to evaluate relational values ascribed to nature, such models with socio-economic models. A local-scale study as a way of life and sense of place (Saito et al. 2022). In successfully coupled a macro socio-economic model and addition, the state variables of the social system that influ- an optimization model of renewable energy installation and ence stakeholders' values and behaviors, such as localized visualized the future (Hori et al. 2020). Off-line coupling of SDG indicators, are another challenge. the local landscape change models and socio-economic fac- Moreover, this study calculated the integrated indicator tors through the social demand of nature's value and social value as the arithmetic means of the min–max scaled indi- resource constraints will enable a more comprehensive cators for the Common, NN, NS, and NC categories with implication for transforming socio-ecological systems. equal w . The sensitivity analysis showed that the number c,i of nature-positive strategies and Pareto optimal strategies are both sensitive for the w values with complex interactions c,i (Supplementary Material S7). The use of analytic hierar- Conclusion chy process (AHP) for integrating indicators is one option to reflect different current stakeholders' preferences (e.g., The NFF is a tool that can evaluate nature from different per - Abelson et al. 2021). However, in mid- to long-term simula- spectives: intrinsic, instrumental, and relational values. This tion, (1) the choice of indicators and (2) setting a plausible study developed a protocol for applying NFF to scenarios stakeholders’ preference, i.e., w , for each decade (2030s, and models at a landscape scale. We used the nature-positive c,i 2050s, and 2090s) are important issues for future research concept as a constraint to filter plausible and feasible solu- because the stakeholders themselves are also dynamic. tions, evaluated Pareto optimality in the three value aspects Another option is to identify Pareto optimal solutions among of the NFF, and identified pathways to reach the vertices and all individual indicators to visualize trade-off and bundle edges of the triangle. Ecosystem management strategies that relationships (e.g., Groot et al. 2010; Hu et al. 2015). reached the vertices and edges of the NFF triangles existed, but the number of these strategies was small. Selective cut- Coupling landscape models and indirect driver models ting of forestry was the important key response option to achieve nature-positive futures. The response options to A major limitation of this study was that only direct driv- achieve Pareto optimal strategies differed between NFF ers were modeled. Thus, policy implications for indirect visions. Not only forestry and pastureland management, but drivers, leverage points, and levers are limited to those also renewable energy installation altered the consequences. closely related to ecosystem management activities. In In addition, only a few strategies were able to consistently climate change research, a broad range of indirect drivers maximize or maintain the same value perspectives, suggest- are discussed, including lifestyle changes and food system ing the importance of setting visions for landscape man- transformation. For example, the identification of hotspots agement that can be sustained medium to long term. These with a high impact on C O emissions enabled the design results also imply the potential for stakeholder collaboration of levers and leverage points for lifestyle changes using a within the region. Further local practices in scenarios and participatory approach at the city scale (IGES 2019; Koide modeling that explicitly incorporate changing stakeholder et al. 2021a, b). values and indirect drivers are needed to envision nature- Some landscape modeling studies have incorporated positive and holistic transformative changes. agents of ecosystem managers to simulate feedback between Supplementary Information The online version contains supplemen- socio-ecological systems (Kim et al. 2021). These studies tary material available at https://doi. or g/10. 1007/ s11625- 023- 01301-8 . have primarily focused on agent-based landscape change modeling (Gibon et al. 2010; Sotnik 2018; Sotnik et al. Acknowledgements The authors thank Dr. Takahiro Inoue for helping with the species parameter and environment data survey. This research 2021), which focuses on specific stakeholders such as for - contributes to Japan Long Term Ecological Research (JaLTER) and the estry and agricultural workers. For example, an extension of International Long Term Ecological Research (ILTER) network. The LANDIS-II can simulate the social learning of agents and 1 3 Sustainability Science authors would like to thank Editage (www. edita ge. com) for English Akkeshi town (2018) Statistics of Akkeshi town 2017 (in Japanese). language editing.https:// www. akkes hi- town. jp/ gyosei/ tokei/ tokei sho/ Akkeshi town (2019a) Bekambeushi watershed. https://www .akk eshi- Author contributions Conceptualization: CH, MM, TM, SH, and OS; town. jp/ kanko/ kanko 10/ bekan nbeus hi/ methodology: CH, MM, WH, TM, MN, JM, HS, SH; formal analysis Akkeshi town (2019b) Self-reliance promotion project in Akkeshi and investigation: CH, MM, TM; writing—original draft preparation: town. (in Japanese). https:// www. akkes hi- town. jp/ file/ conte nts/ CH, MM, writing—review and editing: all authors; resources: JM, HS; 292/ 5223/ h29- R02ka sokei kaku. pdf funding acquisition: CH, JM, HS, OS; supervision: TM, MN, JM, HS, Benson HP (2009) Multi-objective optimization: pareto optimal solu- SH, OS, SO, HJK, GP. tions, properties multi-objective optimization: pareto optimal solutions, properties. In: Floudas CA, Pardalos PM (eds) Ency- Funding Open access funding provided by Osaka University. This clopedia of optimization. Springer US, Boston, pp 2478–2481 research was funded by the Environment Research and Technol- Bezanson J, Edelman A, Karpinski S, Shah VB (2017) Julia: a fresh ogy Development Fund (JPMEERF16S11500 and 1FS-2201) of the approach to numerical computing. SIAM Rev 59:65–98. https:// Environmental Restoration and Conservation Agency of Japan; JSPS doi. org/ 10. 1137/ 14100 0671 KAKENHI Grant numbers 17H01516 and 18J20266; JST e-ASIA Biodiversity Center of Japan (2017) GIS data of 1:25,000 scale vegeta- JRP Grant number JPMJSC20E6; JST Belmont Forum Grant number tion map. http:// gis. biodic. go. jp/ webgis/ JPMJBF2102; and Grant Program for Doctoral Course Students from CAO (2022) Regional Revitalization SDGs/“Environmental Future Sompo Environment Foundation. City” Concept-Regional Revitalization Promotion Secretariat. https:// www. chisou. go. jp/ tiiki/ kankyo/ index. html. Accessed 24 Data availability Input data and analysis scripts have deposited in our Jun 2022 GitHub repository: https://git hub.com/ hag achi/ Pr oject- bek ambe- NFF . 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The images or other third party material in this article are 10. 1016/j. biocon. 2019. 03. 046 included in the article's Creative Commons licence, unless indicated Coleman K, Muhammed SE, Milne AE et al (2017) The landscape otherwise in a credit line to the material. If material is not included in model: a model for exploring trade-offs between agricultural pro- the article's Creative Commons licence and your intended use is not duction and the environment. Sci Total Environ 609:1483–1499. permitted by statutory regulation or exceeds the permitted use, you will https:// doi. org/ 10. 1016/j. scito tenv. 2017. 07. 193 need to obtain permission directly from the copyright holder. To view a Editorial Committee of history of akkeshi town (2012) New history of copy of this licence, visit http://cr eativ ecommons. or g/licen ses/ b y/4.0/ . Akkeshi town. Gyousei, Akkeshi town ESGF-CoG: CMIP-5 (2017). https://esgf- node. llnl. go v/sear ch/cmip5/ . Accessed 08 Feb 2018. 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Publisher: Springer Journals
Copyright: Copyright © The Author(s) 2023
ISSN: 1862-4065
eISSN: 1862-4057
DOI: 10.1007/s11625-023-01301-8
Publisher site: See Article on Publisher Site

Abstract

Envisioning positive scenarios that recognize the multiple values of nature is fundamental for designing transformative changes in local socio-ecological systems. This study developed a protocol with three specifications for operationalizing the Nature Futures Framework (NFF) in a landscape scenario analysis using a multi-objective optimization framework composed of: (1) exploring nature-positive futures, (2) seeking alternative pathways for targets satisfying visions of plural values, and (3) screening key direct drivers to achieve the targets. This research conducted a case study of a rural landscape in northeastern Japan. First, 110 strategies of landscape management options were simulated from 2015 to 2100 using a forest landscape model, LANDIS-II. The simulation developed a data frame of four integrated indicators of the NFF values for each year and strategy. Second, nature-positive strategies were screened using the common values. Pareto optimal strate- gies were then identified to obtain equally good solutions. Finally, the key response options to achieve good nature-positive futures were identified using decision tree analysis. Our protocol identified (1) multiple, but few nature-positive and Pareto optimal strategies that satisfied NFF visions, (2) nature-positive, but not Pareto optimal strategies, and (3) non-nature-positive strategies. In most Pareto optimal strategies, the maximized value perspectives changed over time. Our protocol also iden- tified key response options to achieve three different NFF value perspectives in the case study area: (1) clear or selective cutting in forestry and (2) solar PV installation on abandoned pastureland in agriculture and energy sectors. We discussed the implication for local landscape management, localizing NFF narratives to develop future scenarios and modeling prac- tice of NFF. The protocol does not depend on a specific model and indicator. Thus, our scalable protocol can be applied to scenarios and model practices in any region to support envisioning plausible, feasible, and positive futures, and designing future stakeholder collaboration. Keywords Scenario analysis · Forestry · Agriculture · Forest landscape model · Nature positive Handled by Carolyn Lundquist, University of Auckland, New Zealand. * Chihiro Haga The University of Tokyo, Bunkyo-Ku, Japan chihiro.haga@ge.see.eng.osaka-u.ac.jp Institute for Global Environmental Strategies, Hayama, Japan 1 7 Osaka University, 2-1, Yamadaoka, Suita, PBL Netherlands Environmental Assessment Agency, Osaka M3-405565-0871, Japan Den Haag, The Netherlands 2 8 NEWJEC Inc., Osaka, Japan German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany Hokkaido University, Sapporo, Japan Stockholm University, Stockholm, Sweden Hokkaido University, Akkeshi, Japan Vol.:(0123456789) 1 3 Sustainability Science by (1) expressing the value of nature from three perspec- Introduction tives: Nature for Nature (NN) (intrinsic value), Nature for Society (NS) (instrumental value), and Nature as Culture/ A holistic and integrated approach is essential for prevent- One with Nature (NC) (relational value); (2) identifying a ing biodiversity decline (IPBES 2019; Leclère et al. 2020; set of solutions rather than a specific solution to a desired Secretariat of the Convention on Biological Diversity future vision; (3) including social and ecological system 2020). Maintaining ecological systems in good condition interactions; and (4) application at the local scale (Pereira is essential to achieving sustainable development goals et al. 2020). (SDGs) because it is a prerequisite for a sustainable social Several studies have conducted qualitative scenario analy- system that avoids overshooting and shortfalls (Raworth ses of ecosystem services using the NFF by identifying the 2017; Rockström et al. 2009). Countermeasures to climate key direct and indirect drivers of biodiversity loss (Rosa change and biodiversity degradation have been considered et al. 2020). Sarkar et al. (2020) reviewed the drivers of the in individual fields such as the Intergovernmental Panel on changes in tropical wetlands in India and Brazil. They found Climate Change (IPCC) and Intergovernmental Science- common drivers of agricultural intensification, infrastructure Policy Platform on Biodiversity and Ecosystem Services development, and lack of concrete policies, while some driv- (IPBES) (Pörtner et al. 2021). However, in recent years, ers differed between countries. Moreover, 20 indicators of nature-based solutions have become a central issue in cli- wetland values were classified into three value perspectives mate change mitigation and adaptation (Chausson et al. of NFF; there were both common indicators among value 2020; Seddon et al. 2020, 2021). In 2021, the IPCC and perspectives and specific indicators for each value perspec- IPBES jointly reported that climate change and biodiver- tive (Sarkar et al. 2020). Siqueira-Gay et al. (2020) described sity problems are inextricably connected (Pörtner et al. the drivers of current deforestation in the Amazon using 2021). Degradation of ecological systems is a wicked the Driver, Pressure, State, Impact, and Response (DPSIR) problem; thus, identification of critical levers and lever - framework, and qualitatively envisioned the changes in age points to trigger the transformation of the entire socio- nature’s contributions to people (NCP) under alternative ecological system is an urgent and important task (Abson future scenarios. Resende et al. (2020) also described the et al. 2017; Chan et al. 2020; Meadows 1999). drivers of changes in water quality and quantity using the A place-based scenario analysis that considers inter- DPSIR framework, and qualitatively evaluated the future actions between site-specific socio-ecological systems is direction of a business as usual (BaU) and four alternative helpful in designing local transformations (TNFD 2022; scenarios. Lembi et al. (2020) graphically visualized three Wu 2013, 2021). Therefore, a large number of previous alternative future socio-ecological systems in urban areas in studies have conducted scenario analyses of landscape and Brazil by applying the NFF concept. All these studies quali- ecological management at local scales worldwide (Ren tatively draw the interlinkage between indirect drivers, such et al. 2019; Shifley et al. 2017; Verburg et al. 2004). How - as population and socio-economic conditions, direct drivers, ever, the comparison and integration of knowledge from such as land use and land cover (LULC) change, natural each scenario analysis were difficult because the scenarios resource management, climate change, and natural ecosys- were developed for each local-specific context (Rosa et al. tems. Narratives of SSP scenarios in climate change research 2017). Shared socio-economic pathways (SSPs) are fre- have been translated into quantitative socio-economic status quently used in biodiversity studies. However, a global using gridded population models and LULC change models assessment report demonstrated that even SSP1-RCP2.6, (Chen et al. 2020; Shi et al. 2021). Thus, establishing a sce- which is the most sustainable scenario, cannot halt biodi- nario simulation methodology that quantitatively translates versity degradation (IPBES 2019). NFF narratives using models that simulate the impacts of a Thus, to explore nature-positive futures (CBD 2021), wide variety of drivers is a key task. the IPBES task force on scenarios and models developed There are two expected tasks for NFF modeling at the a framework for designing multiscale and integrative local scale. One is to simulate all the key indirect and nature–people scenarios: the Nature Futures Framework: direct drivers to represent the holistic transformation of a flexible tool to support the development of scenarios socio-ecological systems, considering the diversity of and models of desirable futures for people, nature, and value, learning, and ecosystem management activities Mother Earth (henceforth the NFF) (Schoolenberg et al. between stakeholder agents (Kim et al. 2021; Pereira 2020; Okayasu et al. 2019a, b; Pereira et al. 2020; Rosa et al. 2020). This task requires the integration of land- et al. 2017). This is a framework for scenario development scape change models and socio-economic dynamics mod- that enables comparisons between case studies of desir- els and the implementation of stakeholder agents into the able futures, while also considering the local context in integrated model (Chopin et al. 2019). The other task is collaboration with stakeholders. The NFF is characterized to inform interlinkages between the three different value 1 3 Sustainability Science perspectives of the NFF to strengthen and catalyze col- the interlinkages between the three value perspectives of laboration between stakeholders (Pereira et al. 2020). the NFF. This information is useful for considering key leverage Specification 3. Screening key direct drivers to achieve points and levers in a landscape (Pasalodos-Tato et al. targets: This protocol identifies the conditions of direct driv - 2013; Pereira et al. 2020; Scheller 2020). Thus, this study ers to achieve all pathways identified in Specification 2. focused on the second task, in which the existing land- This study conducted an NFF modeling case study in scape change models can contribute to developing a pro- Japan to assess the utility of the protocol. In this paper, we tocol for modeling the NFF in a landscape. The following discuss whether our protocol can inform collaborations section describes the specific requirements of the devel- among stakeholders with diverse values; we also discuss oped protocol. research needs to expand the applicability of NFF in a cer- Specification 1. Exploring nature-positive futures: tain landscape context. This protocol explores solution sets that satisfy plausible, nature-positive, and NFF visions using landscape change models, which can simulate various human interventions. Materials and methods Specification 2. Seeking alternative pathways for targets satisfying visions of plural values: This protocol visual- This study modeled the NFF-based scenarios by applying izes multiple alternative pathways to reach an agreed-upon the concept of multi-objective optimization and a land- target in the NFF state space. The protocol also visualizes scape change model to explore desirable futures, seek tar- gets in the NFF state space, and screen response options Fig. 1 Overall protocol developed by this study. The right bottom figure was modified from the description of scenarios by Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) (https:// ipbes. net/ scena rios) 1 3 Sustainability Science for reaching these targets in the Bekambeushi River Site description watershed in northeastern Japan (Fig. 1). First, a land- scape change model simulated changes in NFF indicators The Bekambeushi River watershed in northeastern Japan toward 2100 under plausible landscape management strat- was selected as the case study area (Fig. 2A). The total egies (hereafter, plausible strategies) (Step 1). Second, area of the watershed is 700 km , with a small difference strategies that satisfied the nature-positive constraints in elevation from 0 to 141 m (GSI 2019). The current (hereafter, nature-positive strategies) were extracted (Step monthly mean air temperature ranges from − 8 to 20 °C, 2). Third, Pareto optimal strategies and dominated strate- and the annual precipitation is 1200 mm (Esgf-CoG 2017). gies among NN, NS, and NC indicators were identified Forest and pasturelands cover 70% and 20% of the water- to embed them in the NFF state space (Kim et al. 2021) shed, respectively (Fig. 2B; Biodiversity Center of Japan (Step 3–1). Then, our protocol identified how values of 2017). The Bekambeushi River wetland, located in the each nature-positive strategy transitioned in the NFF state middle of the watershed, was listed in the Ramsar Con- space toward 2100 (Step 3–2). Finally, the key response vention in 1993 (Akkeshi Town 2019a). In the national options to reach a certain condition in the NFF state space forest in the northern areas, the dominant species for tree were identified (Step 4). All analyses and visualizations plantation is Larix kaempferi (Lamb.) Carrière. In the for this process were conducted using R v4.1.2 (R Core private forest in the southern areas, the dominant species Team 2021) and Julia v1.6 (Bezanson et al. 2017). are Sakhalin fir (Abies sachalinensis (F. Schmid)] Mast.) for tree plantation and a natural mixed forest of Sakha- lin fir and Japanese oak (Quercus crispula Blume). The Fig. 2 Description of the study area. a Location of the Bekambeushi River watershed (red dot), b vegetation distribution, and c zoning of the study area 1 3 Sustainability Science Bekambeushi River flows from the north into Lake Akke - Step 1. Developing an NFF indicator data frame shi, a 32 km brackish water lake, connecting Akkeshi using a model Bay and the Pacific Ocean through a narrow ~ 400 m-wide channel. Upstream land use changes in the watershed Model description affect the water quality and productivity of the lake and bay through nutrient concentration changes in the river This study simulated LULC change and vegetation succes- water (Nakaoka et al. 2018). sion using the LANDIS-II model (Scheller et al. 2007) under The main industries in the watershed are dairy farming, different forest and pastureland management scenarios at a tourism, forestry in the national and private forests, and 100 m resolution under climate change. LANDIS-II is one fisheries and aquaculture in the bay, lake, and offshore area of the leading forest landscape models (e.g., Petter et al. (Akkeshi Town 2018). The watershed stakeholders include 2020; Scheller et al. 2011; Shifley et al. 2017 ; Thompson local ecosystem managers, such as foresters and farmers, et al. 2016) that has already been localized in this area, as and those who use local ecosystems, such as citizens, local described previously. A significant difference from the other governments, and tourists (Tajima et al. 2021). One of the land use change models, which simulate discrete state space stakeholders’ concerns is the impact of LULC changes in the transitions (e.g., Estman 2022), is that LANDIS-II simu- watershed on river water quantity and quality, which may lates the impact of anthropogenic interventions on vegeta- affect fundamental industries such as fisheries and aquacul- tion dynamics in each grid cell. LANDIS-II is a raster-based ture. Moreover, the total residential population in the water- spatially explicit model that represents an entire landscape shed was 8604 in 2010 and is projected to decrease to 4980 as a collection of grid cells and computes the vegetation by 2050 (NIPSSR 2018). This depopulation has affected dynamics of each plant cohort in each grid cell. The model local ecosystem management. The areas of clear-cutting and calculates spatially explicit plant cohort recruitment due to thinning of larch and Sakhalin fir for timber and pulp pro- seed dispersal from surrounding grids, natural disturbances, duction have decreased in recent years (Hokkaido Prefecture forest management, growth, competition, mortality, above- 2019). Local administrative documents reflect the concern ground biomass, and ecosystem carbon and nitrogen cycles that the abandonment of pastureland will increase because of influenced by climate change. The model requires initial for - the declining population (Akkeshi Town 2019b). However, est vegetation and climate conditions, life history, functional other administrative documents plan to maintain or expand data by tree species, and landscape management options. dairy farming (Akkeshi Town 2014). As these contrasting The model outputs time-series changes in biomass by tree plans suggest, it is uncertain whether the future direction of species, carbon sequestration rate, and timber and pasture land use management will be toward land sharing or land production. This study used LANDIS-II NECN Succession sparing (Immovilli and Kok 2020). extension v6.4 (Scheller et al. 2011) (hereafter, NECN v6.4), The study area has been subjected to multiple scenario which can represent responses to climate change according analyses. Tajima et al. (2021) conducted a questionnaire sur- to the traits of each plant species. The calculation process vey of residents and found that the objects supporting local is detailed both in Scheller et al. (2011) and the GitHub identity differed depending on the stakeholders. Objects repository of the simulation model (https:// landis- ii- found related to marine ecosystems, such as fisheries, oysters, ation.gi thub. io/ Extens ion- NECN- Succes sion/). A previous kelp, clams, and Pacific saury, produced capitals, such as study has described the parameterization, calibration, and the Akkeshi Bridge and roadside stations, and landscapes validation of the model (Haga et al. 2019, 2020). The list of and livelihoods, such as dairy farming, were found to sup- simulated species is shown in Table S1 in the Supplementary port local identities (Tajima et al. 2021). On the other hand, Materials. modeling studies have shown that changes in local land use Table 1 describes the major direct drivers and response and forest management intensities as populations decline options, i.e., human interventions, which could be simulated would alter ecosystem services, such as vegetation distri- using the LANDIS-II model. The LANDIS-II Biomass Har- bution, carbon fixation, wood supply, and pasture supply vest extension v4.1 calculates harvesting methods and tim- using the LANDIS-II model (Haga et al. 2019). Moreover, ing for plant cohorts at the stand level and harvesting plans another study has suggested that the introduction of renew- based on zoning at the landscape level. Clearing, thinning, able energy as a climate change mitigation measure would and selective cutting can be simulated by setting the type and decrease the habitat of the mountain hawk eagle and Blak- amount of trees to be harvested and the timing of harvesting iston's fish owls within the area (Haga et al. 2020). Based on in each forest stand. Even in pasturelands, model users can these studies, this study translated the stakeholder’s prefer- control the timing of vegetation succession owing to seed ences (Tajima et al. 2021) into an NFF perspective and used dispersal from the surrounding area. the LANDIS-II model to visualize the interlinkages, such as We used the RCP2.6, which is expected to minimize the trade-offs or synergies, among different values. impacts of climate change on biodiversity, because the NFF 1 3 Sustainability Science Table 1 Direct drivers and Ecosystems Direct drivers Player Response options response options, which LANDIS-II can simulate. The Forest Landscape level: Forest manager Promotion of sustainable response options refer to the Zoning Local government forest management PANCES Project (PANCES Stand level: Forest owner Formulation of forest Project 2022) Clear-cut Power generation company zoning Selective cutting Promotion of plantation Thinning forest management Planting Guidance from a single- Site preparation storied forest to a multi- Rotation period storied forest Guidance to a natural forest Introduction of forest environment tax to man- age abandoned forests Support for new employ- ment in forestry and training of leaders Promotion of utilization of woody biomass Pastureland Continuous management Farmer Promotion of direct pay- Pastureland abandonment Local government ment system to agricul- Solar PV installation on Land owner tural workers abandoned pastureland Power generation company Consolidate and increase Plantation of trees on aban- the scale of agricultural doned pastureland land Promotion of mechaniza- tion in the agricultural sector Diversion of abandoned cultivated land (natural regeneration or tree plantation) Promotion of solar PV installation assumes desirable visions of future human–nature relation- and regional forest plans (Akkeshi Town 2017; Hokkaido ships (Pereira et al. 2020). The same bias-corrected RCP2.6 Prefecture 2017a, 2017b, 2017c; MAFF 2017). The current future climate data of the CMIP5 MRI-CGCM3 model were conservation practices on the current protected forests for used, as in previous studies (Haga et al. 2019, 2020). Under disaster prevention and mitigation and wildlife conserva- these climate conditions, the average temperature in the tion will be continued until 2100. Other forests intended for region would increase by 1.4 °C by 2100 (Haga et al. 2019, timber production were designated as management areas. 2020). The remaining environmental conditions were the The pasturelands were zoned into two areas based on geo- same as those described by Haga et al. (2020). The simula- graphical, topographical, and social factors: the conserva- tion period was 85 years, from 2015 to 2100, with time steps tion areas to be guided to the natural forest after abandon- for material cycling set to one month and seed dispersal and ment, and management areas to support instrumental or ecosystem management set to one year. relational values. First, we identified pasturelands that were topographically and socially difficult to manage, likely to Designing plausible direct human interventions to nature be abandoned, and used these to simulate the expansion of abandoned land (Haga et al. 2020). For pasturelands aban- First, in all plausible strategies, conservation areas where doned in management areas, a grid search was conducted human intervention should be avoided and management to determine whether solar PV should be introduced after areas where ecosystem management is conducted were abandonment or whether biomass energy should be used by determined according to the current policy of forest and converting pastures into forests after abandonment. Solar pastureland management in the area (Fig. 2C). Forests were PV was introduced from abandoned pasturelands located far first zoned into conservation areas and management areas from the forest edge. See Haga et al. (2020) for a detailed by forest stands, referring to the forest register database algorithm for abandoned pastureland expansion. 1 3 Sustainability Science Second, combinations of plausible forest and pasture- they were considered to have the potential to become wet- land management in the management areas, i.e., plausible lands (Kaneko et al. 2008; Morimoto et al. 2017). strategies (Fig. 3 and Supplementary Materials S2), were designed. Individual forest and pastureland management Indicator selection by translating visions and values of NFF options are plausible and difficult to determine superior or to the local context inferior relationships from the viewpoint of plural values of nature. Therefore, we conducted a grid search over the We assumed that whether the study area is nature posi- 110 combinations of plausible strategies comprising two tive can be evaluated by the condition of the fundamental contrasting groups: 21 land-sparing-oriented strategies, landscape structure and ecosystem functioning. Thus, we which decrease timber production and maintain or rewild selected five common indicators to quantitatively evaluate pastureland as the population decreases (Fig. 3 orange four values (Table 2). The visions and values of each vertex and Table S2-1), and 89 land-sharing-oriented strategies, of the NFF were translated into the local context and mapped which continue landscape management despite depopu- to variables that could be output by the LANDIS-II model lation to maintain a supply of NCPs from the landscape (Table 2 and Supplementary Materials S3). In this study, (Fig. 3 green, Tables S2-2, and S2-3). The forest manage- we set up common visions and values to evaluate nature- ment in this study refers to standard practices as follows positive criteria, which is the basic idea of NFF, and spe- (Akkeshi Town 2017; Hokkaido Prefecture 2017c). The cific visions and values to enable the quantitative evaluation older forest stand that reached the standard harvest period of the value each vertex aims for. First, we assumed that was harvested first. We limited the maximum clear-cut the nature-positive state recovered fundamental landscape area of each operation to 20 ha for biodiversity conserva- structures and ecosystem functions by the 2030s and 2050s tion, referring to the standard practice. To conserve and compared to the current level (Locke et al. 2021). For this expand riparian forests, abandoned pastureland within purpose, we calculated the Dissimilarity-based Satoyama 300 m of the river was rewilded by natural regeneration. In Index (DSI) (Yoshioka et al. 2017) and proportion of non- addition, pasturelands that were once wetlands in the past artificial landscape area to evaluate landscape structure, net were not used as a source of renewable energy because ecosystem productivity (NEP) as regulating NCP, vegetation biomass, and its diversity (Table 2). Fig. 3 Combinations of forest and pastureland management strategies 1 3 Sustainability Science Table 2 Indicators for evaluating the common value and three NFF values. The Supplementary Materials describe the calculation of each indica- tor Category Vision Value Indicator which can be calcu- Stakeholder (Tajima et al. lated from LANDIS-II output 2021) Common Fundamental landscape char- Landscape structure Dissimilarity-based Satoy- Forest, land, and marine acteristics and ecosystem ama Index (DSI) (Yoshioka function will be maintained et al. 2017) Landscape structure Area of natural landscape Forest, land, and marine (ha) Regulating NCP Net Ecosystem productivity Forest, land, and marine –1 (NEP) (g-C y ) Biomass of plant species Total aboveground biomass Forest, land, and marine of trees in the watershed (g-dry weight) Diversity of plant species Simpson’s diversity index of Forest, land, and marine aboveground biomass of trees in the watershed Nature for Nature Preserve nature's diversity Habitat of fauna Habitat suitability index of Forest and functions Blakiston’s fish owl (Yoshii et al. 2018) Habitat suitability index of the mountain hawk eagle (Itoh et al. 2012) Amount of flora Aboveground biomass of Forest native tree species (g-dry weight) (Editorial Commit- tee of History of Akkeshi Town 2012) Nature for Society Maintain or maximize instru- Yield of agriculture and Pasture yield (g-dry weight Forest, land, and outside –1 mental values of nature forestry y ) the area Timber yield (g-dry weight Forest, land, and outside –1 y ) the area Energy production Energy production of woody Forest, land, marine, and –1 biomass (J y ) outside the area Energy production of solar –1 PV (J y ) Nature as Culture/ Maintain the livelihoods and Supporting local identities Proportion of the natural Forest and land One with Nature the natural landscape which and learning and inspiration landscape in the viewshed support local identity and from residential areas and culture roads Eco-tourism (physical and Proportion of the natural Land psychological experiences) landscape in the viewshed from tourism resources: canoe route, local cultural heritage, and natural park Connectivity of dairy farming Biomass of riparian forests Land and marine and aquaculture (regulation (g-dry weight), which regu- of freshwater and coastal lates water quality water quality) Scenario-specific indicators were introduced by con- NN was evaluated using the habitat suitability index (HSI) sidering each of the three value perspectives of the NFF. for rare representative species in the area and native plant The NN primarily aims to preserve biodiversity and eco- species biomass (Table 2). For the fauna, Blakiston's fish system functions (Pereira et al. 2020). In this study, this owl (Ketupa blakistoni blakistoni) (Yoshii et al. 2018), narrative was translated as encouraging a rewilding by which uses riparian forests, and the mountain hawk eagle withdrawing from human intervention in forest and pas- (Spizaetus nipalensis orientalis), which uses forest edges tureland as the population declined. Thus, the value of (Itoh et al. 2012), were selected. 1 3 Sustainability Science NS and NC primarily aim to maintain the provision of 2020 (CBD 2021). Therefore, we herein define “nature-pos- NCPs: instrumental values in NS and relational values in itive strategies” as strategies that indicate higher values of NC (Immovilli and Kok 2020; Pereira et al. 2020). Thus, common indicators in both the 2030s and 2050s than the the NS vision in this area was to maximize the supply of mean common indicator value from 2015 to 2029. grass, timber, and renewable energy by managing landscapes through mechanization, efficiency, and land consolidation, Step 3. Embedding nature‑positive strategies to NFF even under population decline. The provision of instru- state space by identifying the Pareto front mental value in NS was evaluated by timber and pasture production and the potential of solar power generation on All vertices of the NFF state space are plausible and desir- abandoned land and renewable energy from woody biomass able futures, although the values differ among them (Kim of woody plants (Table 2). et al. 2021; Pereira et al. 2020). Therefore, combinations However, NC emphasizes the relational values of a region of vertices that can achieve different values simultaneously, (Immovilli and Kok 2020; Pereira et al. 2020). For this rea- which show trade-off relationships, or strike a balance son, NC’s vision is to maintain the local identity supported between different values, can exist. This information about by local livelihoods. A previous study revealed that local the interlinkages between different values of NFF state space identities in this area are provided by fisheries and unique will visualize local problems in landscape management and natural terrestrial landscapes that support tourism and dairy suggest possible collaboration between stakeholders. There- farming (Tajima et al. 2021). Thus, the proportion of the fore, this study visualizes the interlinkages between the val- natural landscape in the viewshed from residential areas ues of each vertex of the NFF state space (Kim et al. 2021) and roads and from tourism resources, such as canoe routes, using the concept of multi-objective optimization for the local cultural heritage sites, and natural parks, and the bio- 2030s, 2050s, and 2090s. mass of riparian forests, which regulate water quality, were All scenario narratives drawn in the NFF are desirable used as indicators. futures (Pereira et al. 2020; IPBES 2022), i.e., there are no Finally, the 15 indicators in Table 2 were summarized superior or inferior relationships between scenarios, but each into four categories to develop a time-series NFF indica- scenario is better than the others in at least one value of tor data frame (four indicators × 110 strategies × 85 years) nature. Therefore, exploring management strategies result- (Fig. 1). First, each individual indicator was min–max nor- ing in such scenarios is equal to exploring Pareto optimal malized timewise. Then, integrated indicator time series by solutions (Benson 2009) in the three-dimensional NFF state categories were calculated as the arithmetic mean of the space (Kim et al. 2021; IPBES 2022). An example is shown min–max normalized indicators for each year and category in Fig. 4. In the NN and NS plane, plots on the colored curve as follows: in Fig. 4A are Pareto optimal strategies: there are no other strategies that increase NN or NS value without decreasing w × Y c,i the other value. The colored curve is called the Pareto front, i=1 c,i,y Yinteg = , c,y ∑ a set of Pareto optimal strategies, which shows trade-offs c,i i=1 between the two values. The other colored plots and black plots in Fig. 4A are dominated strategies: there is at least one where Yinteg are the integrated indicators of category c c,y strategy in which both NN and NS values are better. Repeat- in year y , N is the number of individual indicators within ing this procedure for the NN and NC plane and NC and NS category c , and Y is the min–max normalized individual c,i,y plane as shown in Fig. 4B, C, all nature-positive strategies indicator i within category c in year y . w is the weight of c,i can be classified into two groups: (1) Pareto optimal strate- indicator i within category c . It should be noted that the gies in which there are no other strategies that increase an higher indicator values show better conditions. We assume NFF value without decreasing another value or (2) domi- that each indicator has the same weight ( w = 1) to demon- c,i nated strategies in which there is at least one strategy with strate how our protocol performs. Sensitivity analysis of w c,i higher three NFF values than themselves (Fig. 4D left). are shown in the supplementary materials S7. Next, our protocol classified the Pareto optimal strategies into seven NFF categories to show which NFF perspectives Step 2. Screening nature‑positive strategies using will be achieved (Fig. 4D). The Pareto optimal strategies the simulated common integrated indicator with top 10% values of each integrated indicator (Fig. 4A–C) were classified to be the categories which can achieve the Step 2 screened nature-positive strategies from 110 manage- vertices of NFF triangles: NN category, NS category, and ment strategies using the common integrated indicator. The NC category. The remaining Pareto optimal strategies were post-2020 target aims to stop the net decline in biodiversity classified into the categories that balance multiple value per - loss by 2030 and sufficiently recover by 2050 compared with spectives: NN–NS Pareto category, NS–NC Pareto category, 1 3 Sustainability Science Fig. 4 How to embed the nature-positive strategies (hereinafter, Pareto optimal strategies; small black plots are dominated strategies. nature-positive strategies) into the Nature Futures Framework (NFF) The Pareto optimal strategies are then classified into the NFF catego- state space. All nature-positive strategies are plotted in A NN and NS, ries shown in D B NN and NC, and C NC and NS planes. Large colored plots are the NN–NC Pareto category, and NN–NS–NC Pareto category. over the 2050s and 2090s, depending on ecosystem manage- As a result, the Pareto optimal strategies can be embedded in ment strategies (Table 3). The mean and median values for a three-dimensional state space of NFF integrated indicators each category increased moderately through the 2050s and (hereafter, NFF state space (Kim et al. 2021)). Step 4. Identifying pathways to achieve NFF Table 3 Summary of four integrated indicators by category and time horizon for all 110 strategies category Category Year Mean ± SD Min Median Max Step 3 labels the non-nature-positive strategies, dominated Common Current 0.49 ± 0.05 0.39 0.49 0.68 strategies, and seven NFF categories for each management 2030s 0.50 ± 0.06 0.32 0.50 0.69 strategy defined in Step 1. Therefore, decision tree analysis 2050s 0.48 ± 0.09 0.20 0.48 0.73 was used to visualize the key response options to reach each 2090s 0.59 ± 0.14 0.24 0.61 0.85 NFF category avoiding non-nature-positive conditions. From NN Current 0.31 ± 0.04 0.24 0.30 0.55 the results, the critical indirect drivers, leverage points, and 2030s 0.36 ± 0.05 0.22 0.37 0.61 levers behind them were also discussed. 2050s 0.35 ± 0.08 0.12 0.35 0.62 2090s 0.51 ± 0.13 0.22 0.50 0.83 NS Current 0.32 ± 0.09 0.04 0.32 0.57 Results 2030s 0.32 ± 0.12 0.00 0.31 0.67 2050s 0.38 ± 0.15 0.06 0.35 0.69 Step 1. Summary of the NFF indicators time‑series 2090s 0.42 ± 0.18 0.12 0.40 0.75 data frame NC Current 0.69 ± 0.03 0.57 0.69 0.81 2030s 0.71 ± 0.06 0.49 0.72 0.87 The summary of the four integrated indicators of 110 strate- 2050s 0.66 ± 0.13 0.21 0.70 0.89 gies organized according to time horizon shows that the indi- 2090s 0.7 ± 0.2 0.2 0.8 1.0 cator values diverged into decreasing or increasing trends 1 3 Sustainability Science further through the 2090s. The common, NN, and NC indi- to the 2030s was small, and the mean of the indicator val- cators showed that the maximum value increased through ues decreased from the 2030s to the 2070s (Fig. 5). For the 2090s, whereas the minimum value decreased, indicating individual indicators, DSI was lower than in the nature- that the ecological condition diverged due to human inter- positive strategies, the natural landscape was monotoni- vention. However, the results of NS showed that the mini- cally decreasing, and the other indicators were lower than mum, mean, median, and maximum values monotonically those of the nature-positive strategies (Fig. S4). increased over the 2090s, suggesting an increasing trend in every strategy. Step 3‑1. Identifying Pareto optimal strategies Step 2. Screening nature‑positive strategies by common indicators The nature-positive strategies in the three-dimensional NFF state space show synergies and trade-off relation- By screening 110 strategies in which the common integrated ships among the three value perspectives (Figs. 6, 8). indicator increased from the current to the 2030s and 2050s, The dominated strategies (Fig. 6 small colored plots and 51 nature-positive strategies were identified, which were Fig. 7 small black plots) are nature-positive strategies, but positive in all three time horizons (blue lines) (Fig. 5 and are inferior to the Pareto optimal strategies (Fig. 6 large S4). Our sensitivity analysis also identified 50 ± 10 nature- colored plots and Fig. 7 large colored plots). For all indica- positive strategies (Supplementary Material S7). Nature- tor combinations, the Pareto optimal strategy and the set positive strategies in blue lines show a recovery from the of dominated strategies separated by the year approached current state to the 2030s, followed by an increase through 2100, and a trade-off relationship between the indicators 2090. The DSI, proportion of natural landscape, and total emerged (Fig. 7). The correlation coefficients between (1) tree biomass were high in these nature-positive strategies NN and NS indicators and (2) NS and NC indicators were (Fig. S4). consistently negative and the Pareto front was confirmed, However, as indicated by the gray dashed line, the suggesting a trade-off between these indicators (Fig. 7, majority were labeled as non-nature-positive strategies 1st column). On the other hand, the NN and NC indica- (Figs. 5 and S4): non-positive in the 2030s (N = 2), non- tors were positively correlated for all time horizons, but a positive in the 2050s (N = 10), non-positive in the 2030s Pareto front emerged in the 2090s, suggesting a trade-off and 2050s (N = 23), and non-positive in the three time (Fig. 7, 3rd column). horizons (N = 23). In the non-nature-positive strategies, 6–18% of the nature-positive strategies were Pareto the recovery in the mean indicator values from the current optimal strategies by three time horizons and were classi- fied into six NFF categories (Table 4). Five of the catego- ries were predetermined, as shown in Fig. 4B: the vertex categories of NN, NS, and NC, the NN–NS Pareto cat- egory, and the NN–NC Pareto category (Table 4), whereas the NS–NC Pareto category and NN–NS–NC Pareto cat- egory were not identified. In addition, an unexpected cat- egory, the NN and NC bundle category, was identified that maximized NN and NC indicators simultaneously (Table 4). This category appeared because the NN and NC indicators showed a synergy relationship in the 2030s and 2050s. In the 2090s, the trade-off between NN and NC indicators emerged (Fig. 7, 3rd column); then this NN and NC bundle category was changed into the NC category (Fig. 8). The number of Pareto optimal strategies varied from 20 to 7 over time (Table 4). The sensitivity analy- sis also showed that only 30–60% of the nature-positive strategies were classified as Pareto optimal strategies on average (Supplementary Material S7). The majority were Fig. 5 Common integrated indicator by 110 management strategies. classified as the NN–NS Pareto category. Only one or two Black horizontal line shows the mean value from 2016 to 2029 of strategies were classified as NN, NS, and NC categories business as usual (BaU). Blue lines show nature-positive strategies for each time horizon, suggesting that the feasible solution whose mean values for the 2030s and 2050s are higher than the black space to reach each NFF vertex is very small. horizontal line (N = 51); gray dashed lines show non-nature-positive strategies (N = 59) 1 3 Sustainability Science Fig. 6 Visualization of Nature for Nature (NN), Nature for Society (NS), and Nature as Culture/One with Nature (NC) integrated indicators in the 2030s, 2050s, and 2090s in three-dimensional space. The light blue, red, and blue plots show 51 nature-positive strate- gies in the 2030s, 2050s, and 2090s. Large and small plots denote the Pareto optimal and dominated strategies, respec- tively. All indicators are the minimum at the right bottom origin O (0, 0, 0) and the maxi- mum at the left top (1, 1, 1). The small gray plots show the projection of the 51 strategies on each plane surface of NN, NS, and NC Step 3‑2. Embedding Pareto optimal strategies to the NFF a different nature future (Fig. 9). In all strategies, the NN- integrated indicator increased through the 2030s, plateaued state space through the 2050s, and then showed a monotonically increasing trend (Fig. 6). The HSI of Blakiston’s fish owl Approximately 50% of the nature-positive strategies transi- tioned to other NFF categories or dominated strategies over increased monotonically through 2100, because there was no development or deforestation near the river (Fig. S5-1). The time (Fig. 8A). The NN category in the 2030s transitioned to the dominated strategy after the 2050s. Strategies classi- HSI of the mountain hawk eagle decreased in the long term as forest edges decreased due to the conversion of abandoned fied as NN and NC bundle category in the 2030s and 2050s, which achieved NN and NC simultaneously, were classified pasturelands to forest lands (Fig. S5-1). The total biomass of native trees decreased after 2040, but increased thereafter as NC category in the 2090s. In the NN–NS Pareto category (N = 16), most strategies were inferior after the 2050s. The (Fig. S5-1). The NS-integrated indicator showed that the supply of timber and pasture grasses varied widely from year remainder of the NN–NS Pareto category transitioned to the NN or NN–NC Pareto categories in the 2090s. Later, 12% to year, but in some strategies increased, others remained flat, and in others decreased until 2100 (Fig. 6). The NC- of dominated strategies in the 2030s and 2050s also moved to Pareto optimal strategies. integrated indicator showed an increase in 2030, leveling off in 2040, and a monotonically increasing trend thereafter The NFF classification results for the 2090s were embed- ded in the three-dimensional NFF state space, as shown in (Fig. 6). The total biomass of riparian forests decreased after 2040, but increased thereafter (Fig. S5-1). The proportion Fig. 8B. The Euclidean distances between the vertices of (1) NN and NC, (2) NN and NS, and (3) NS and NC were (1) of natural landscapes decreased with the introduction of renewable energy (Fig. S5-1). Because the NS indicator has 0.28, (2) 0.58, and (3) 0.81, respectively, unlike the equilat- eral triangle shape assumed in Fig. 4A. This indicates that a trade-off relationship with both the NN and NC indicators (Fig. 8), NS indicators were low in strategies where NC was the visions of NN and NC are in close space in the NFF state space, while NS is isolated, reflecting how similar the maximized at a certain time horizon; conversely, NC indica- tors were low in strategies where NN and NS indicators were visions are. high (Fig. 9, left and middle panels). Only three strategies were Pareto optimal strategies in all Step 3‑3. What will alternative nature futures be like? three time horizons (Fig. 9 left panels). The majority of the NFF category transition pattern was that the NN–NS Pareto Twenty-four strategies were Pareto optimal strategies in at least one or more time horizons, with each arriving at category in the 2030s shifted to the dominated strategy in 1 3 Sustainability Science Fig. 7 Visualization of NN-, NS-, and NC-integrated indica- tors in the 2030s, 2050s, and 2090s in two-dimensional space. Large colored and small black plots denote the Pareto optimal and dominated strate- gies in each two-dimensional area, respectively. The colors show the NFF category (Fig. 4b). Black solid lines show the Pareto front. Numbers in each panel show the Pearson correlation coefficient both the 2050s and 2090s (N = 11) (Fig. D1 right panels). Thus, short-, mid-, and long-term milestones for ecosystem In strategies classified as the NS category in the 2030s and management are important for reducing uncertainty. the NN–NS Pareto category in later years, the NC indicator decreased through the 2050s, indicating a significant trade- Step 4. Key direct driver identification off. On the other hand, each indicator was maintained at a moderate level in the NN–NS Pareto category in all time Although the rules for classifying NFF categories differed by horizons. Although this strategy was not a Pareto optimal decade, the forest harvesting method and percentage of solar strategy in terms of the NC indicator, it was almost a bal- PV on abandoned pastureland were critical explanatory vari- anced solution between the three value perspectives. The ables (Fig. 10 and Supplementary Material S6). Here, we show most frequent strategies were NN–NS Pareto in the 2030s, the decision tree for the 2090s, as discussed in Fig. 8B as an and the dominated strategies in the 2050s and 2090s (N = 11) example (Fig. 8). First, the final cutting method of the forest (Fig. 9 right panels). These results suggest that ecosystem stands was divided into two groups: one containing nature- management goals in the short term are not sufficient to positive strategies and the other containing non-nature-posi- manage changes in mid- and long-term ecosystem values. tive strategies (Node 1). The clear-cutting generally resulted 1 3 Sustainability Science Table 4 Summary of the NFF NFF category Number of strategies and mean value of NN-, NS-, and NC-integrated category by time horizon indicators 2030s 2050s 2090s NN N = 1 N = 0 N = 1 (0.42, 0.17, 0.77) (NA) (0.75, 0.35, 0.90) NS N = 2 N = 1 N = 1 (0.36, 0.41, 0.67) (0.35, 0.59, 0.59) (0.51, 0.72, 0.67) NC N = 0 N = 0 N = 1 (NA) (NA) (0.70, 0.13, 0.99) NN and NC bundle N = 1 N = 1 N = 0 (0.59, 0.06, 0.85) (0.61, 0.08, 0.89) (NA) NN–NS Pareto N = 16 N = 5 N = 5 (0.39, 0.29, 0.73) (0.38, 0.40, 0.70) (0.62, 0.50, 0.77) NN–NC Pareto N = 0 N = 0 N = 1 (NA) (NA) (0.71, 0.26, 0.91) Total N = 20 N = 7 N = 9 (0.40, 0.29, 0.74) (0.41, 0.38, 0.71) (0.64, 0.44, 0.81) The numbers indicate the number of strategies belonging to each NFF category. The numbers in parenthe- ses indicate the mean value of integrated indicators within each category for NN, NS, and NC, respectively Fig. 8 a Transition of NFF category of 51 nature-positive strategies from the 2030s to the 2090s. b Embedded NFF classification results of the 2090s in the NFF state space. Colors show the NFF categories in non-nature-positive strategies, whereas the strategies with category (N = 2) (Node 10 left). The rotation period of forestry, gradually withdrawn clear-cutting in forests but maintained as well as the method of reforestation after final cutting and the pastureland were selected as NCs (Node 25). Next, the use expansion speed of abandoned pastureland, all contributed to of woody biomass without introducing solar PV on aban- the divergence of these individual strategies. doned pastureland resulted in the NN–NC Pareto (N = 1), NN (N = 1), and dominated strategy (N = 35) categories (Node 2 left). The strategy with solar PV installed on all abandoned Discussion pasturelands was classie fi d as non-nature-positive strategies (Node 10 right). On the other hand, a mix of solar PV and By simulating a combination of plausible management strat- woody biomass use resulted in 10 nature-positive strategies, egies, this study was able to indicate the range of nature- some of which were NS solutions (N = 1) or the NN–NS Pareto positive futures (Specification 1), while simultaneously 1 3 Sustainability Science Fig. 9 Strategies where one or more of the nature-positive strategies were classified as Pareto optimal solutions in at least one or more time horizons (N = 24). The line colors indicate the transition pattern of NFF categories in the 2030s, 2050s, and 2090s. The left panel represents strategies with Pareto optimal strategies in all years (N = 3), the right panel represents strategies classified as the NN–NS Pareto category in the 2030s, which shifted to the dominated strategy in both the 2050s and 2090s (N = 11), and the middle panel represents the other strategies (N = 10) identifying the desired targets in terms of NFF, pathways An ecosystem management option did not always guaran- to those targets (Specification 2), and key response options tee maximizing or maintaining the same value in all time (Specification 3). This study defined NFF categories at each horizons. vertex and edge of the NFF triangle as Pareto optimal strate- Several landscape models have applied a multi-objective gies between NFF indicators. This definition can contribute optimization framework to visualize Pareto fronts between to the quantification of diverse desirable alternatives in NFF. agricultural production and environmental indicators, as well Moreover, this study found that NFF categories transition as between forest carbon fixation, biomass, and yield (Cole - over time, demonstrating that the values in each management man et al. 2017; Marques et al. 2020). This study shows that strategy were unstable in the future. Some of the nature- NFF can be applied to existing landscape change modeling positive futures were classified as the vertex or edge of the to analyze the interlinkages between different visions con- NFF triangle (Figs. 7, 9), but the number of these strategies strained by biodiversity conservation goals. Our protocol is was only 25 out of 110 (Fig. 9). The sensitivity analysis also scalable because it is not dependent on a specific model or showed the strategies that satisfy the NFF visions are limited landscape. Furthermore, to propose various candidates for regardless of the weight w (Supplementary Material S7). nature future scenarios, it is encouraged to exhaustively test c,i 1 3 Sustainability Science Fig. 10 Decision tree of the 2090s the combinations of ecosystem management. In this study, selective cutting (Akkeshi Town 2017; Hokkaido Prefec- we performed a grid search over 110 pre-defined strategies: ture 2017c). The results of this study suggest that the use of all strategies assumed stakeholders will not change strategy forest management practices to balance timber production toward 2100 to reduce iterations because of the limitations and ecosystem conservation will contribute to the nature- of our computational resources. However, metaheuristic positive future of plantation forests. Among the response optimization, such as evolutionary algorithms, would also options identified in the PANCES Project (see Table 1), the be useful to explore optimal strategies from a huge number following should be considered: promotion of sustainable of combinations (Groot and Rossing 2011). forest management, formulation of forest zoning, promotion In the following, we discuss: (1) implications for local of plantation forest management, guidance from a single- policy design and (2) future scenarios and modeling of the storied forest to a multi-storied forest or natural forest, intro- NFF to support a transformative change in socio-ecological duction of forest environmental tax to manage abandoned systems in local communities. forests, support for new employment in forestry, and training of leaders (PANCES Project 2022). Implications for local agenda setting and policy In terms of pastureland management, the features of the design Pareto optimal strategies were: (1) pastureland is main- tained, or if pastureland is abandoned, it is (2) converted to Can our protocol help identify alternative policy options? forest by natural regeneration, or (3) a mix of solar PV and natural regeneration is installed. Depopulation and outflow The final cutting in forests and utilization of abandoned from rural areas to urban areas (NIPSSR 2018) and the intro- pastureland contributed to the classification of NFF catego- duction of renewable energy to the landscape for decarboni- ries (Figs. 10 and S6), suggesting that nature future sce- zation (METI 2021) are current megatrends in Japan. Thus, narios are oriented by a combination of response options. the priority areas for agricultural landscape management and In Japan, coniferous plantation forests expanded post-WWII renewable energy should be identified, reflecting the local (Tanimoto 2006), but the conversion to broadleaf forests is characteristics and stakeholders' interests. Response options considered throughout the country for biodiversity conser- for agriculture, such as the promotion of a direct payment vation (Forest Agency 2021). The forest plan for this area system, consolidating and increasing the scale of agricul- also describes the conversion to broadleaf forests through tural land, and promoting mechanization in the agricultural 1 3 Sustainability Science sector, should be implemented to maintain the agricultural through sharing central value perspectives among stake- landscape while at the same time carefully designing the holders. conversion of abandoned pastureland (Table 1). Thus, our 2. The supply of energy, which alters the landscape struc- process successfully identie fi d policy implications to achieve ture and influences the NN, NC, and NS indicators, desirable futures that avoid non-nature-positive states by should be community driven to internalize externalities treating the direct drivers among multiple sectors in a land- and telecoupling. Moreover, to minimize the ecologi- scape change model. cal impacts, energy demand within the region should be reduced in line with future population decline and decar- Can our approach draw insights to transform indirect bonization measures. drivers? Our results also demonstrate that balancing intrinsic and Indirect drivers in multiple sectors, such as forestry, dairy instrumental values in the short term does not always guar- farming, the energy industry, and fisheries, influence the antee long-term balanced futures. For example, the major- direct drivers that lead to the divergence of nature futures. ity of strategies classified as the NN–NS Pareto category in Therefore, cross-sectoral cooperation among stakeholders the 2030s resulted in the dominated strategy category after with different value perspectives is a key lever in this study the 2050s. Furthermore, rural areas in Japan are expecting area (IPBES 2019). The vertices of the NN and NC were an increase in migration and related populations, who con- similar in the NFF state space (Table 4 and Fig. 8B) without tinuously have relationships with a specific region against a clear trade-off relationship. The reason why NN and NC depopulation (Hori et al. 2021; Naitou et al. 2019). Thus, indicators did not show the trade-off, i.e., Pareto front, was the structure and relationships of stakeholders in the future the similarity of indicators. In this region, the previous ques- assumed in the NFF might differ from the current situation. tionnaire survey to stakeholders revealed that local identi- For envisioning long-term nature future scenarios, the inclu- ties (Akkeshi-ness) were supported by fisheries, agriculture, sion of future generations' voices is also essential to consider and tourism resources closely related to natural landscapes changes in stakeholders (Rana et al. 2020). evaluated by NN indicators (Tajima et al. 2021). Two habitat suitability indices of NN and two viewshed indicators of Implications for future scenarios and models NC decreased with increasing installation of solar PV (Fig. of the NFF S5). Moreover, the aboveground biomass of native tree spe- cies and that of riparian forests showed similar dynamics Localizing NFF narratives consistent with local policies (Fig. S5). Thus, both NN and NC indicators increased at the same time and did not show the Pareto front (Fig. 7). The When developing NFF visions, values, and indicators at result suggested that it is possible to design win–win coop- local scales, such as those shown in Table 2, mapping to eration among the forest, land, and marine stakeholders in various existing local strategies is necessary. The 2020s is a the region who have different visions: (1) preserve nature’s decade of action; for example, in Japan, local municipalities diversity and functions and (2) maintain the livelihoods and are developing plans to achieve the SDGs, climate change the natural landscape which support local identity and cul- mitigation and adaptation, and biodiversity conservation ture (Table 2). (CAO 2022; MOE 2021, 2022). Since these existing envi- In contrast, the NS indicator showed a clear trade-off rela- ronmental and sustainability policies might overlap with tionship with the NC and NN indicators. Thus, if the stake- the visions and values described in the NFF, a procedure holders of an ecosystem service are outside the region, there for translating them into the NFF context will help a local may be hidden conflicts with the stakeholders in the water - administrator operationalize the NFF. shed. In particular, unplanned renewable energy installations Variety in the definition of a desirable future should also cause conflicts between energy production, local communi- be considered in future research. This study used the narra- ties, and local SDG (Akita et al. 2020; Schumacher 2017; tive of nature positive as a constraint to select positive strate- Schwanitz et al. 2017). Therefore, cross-sectoral cooperation gies, but the degree of recovery is also important. Moreover, among stakeholders inside and outside the region is another there are other candidates for the constraints, such as area- important lever. For example, these levers should work on based conservation measures, decarbonization targets, and the following leverage points (IPBES 2019): other socio-economic targets, such as food self-sufficiency, labor, and financial requirements. Considering local nature 1. Unleash values by discussing local identities and futures with global-, regional-, or national-scale megatrends embracing diverse visions of a good quality of life as boundary conditions is important. The sub-national- scale SSP scenario narratives and their spatially explicit 1 3 Sustainability Science socio-economic dataset can be used as the constraints of impact spatially explicit forest management (Sotnik et al. NFF modeling (Chen et al. 2020; NIES 2021). 2021). Including citizen agents of the target landscape and The indicators used to evaluate NFF visions and values stakeholder agents outside the region will allow for an evalu- are multidimensional (Sarkar et al. 2020; Siqueira-Gay et al. ation of the influence of indirect factors. 2020); thus, it is necessary to develop a local reference indi- On the other hand, connecting with the models of indirect cator set to analyze these trade-offs. Our 15 NFF indicators drivers helps quantify the pathway to transform entire socio- were developed from administrative planning documents and ecological systems. For example, the discussion of socio- previous studies that can be evaluated by LULC, biomass, economic changes in climate change research is supported and other outputs of landscape change models. The chal- by integrated assessment models that connect global climate lenge is to evaluate relational values ascribed to nature, such models with socio-economic models. A local-scale study as a way of life and sense of place (Saito et al. 2022). In successfully coupled a macro socio-economic model and addition, the state variables of the social system that influ- an optimization model of renewable energy installation and ence stakeholders' values and behaviors, such as localized visualized the future (Hori et al. 2020). Off-line coupling of SDG indicators, are another challenge. the local landscape change models and socio-economic fac- Moreover, this study calculated the integrated indicator tors through the social demand of nature's value and social value as the arithmetic means of the min–max scaled indi- resource constraints will enable a more comprehensive cators for the Common, NN, NS, and NC categories with implication for transforming socio-ecological systems. equal w . The sensitivity analysis showed that the number c,i of nature-positive strategies and Pareto optimal strategies are both sensitive for the w values with complex interactions c,i (Supplementary Material S7). The use of analytic hierar- Conclusion chy process (AHP) for integrating indicators is one option to reflect different current stakeholders' preferences (e.g., The NFF is a tool that can evaluate nature from different per - Abelson et al. 2021). However, in mid- to long-term simula- spectives: intrinsic, instrumental, and relational values. This tion, (1) the choice of indicators and (2) setting a plausible study developed a protocol for applying NFF to scenarios stakeholders’ preference, i.e., w , for each decade (2030s, and models at a landscape scale. We used the nature-positive c,i 2050s, and 2090s) are important issues for future research concept as a constraint to filter plausible and feasible solu- because the stakeholders themselves are also dynamic. tions, evaluated Pareto optimality in the three value aspects Another option is to identify Pareto optimal solutions among of the NFF, and identified pathways to reach the vertices and all individual indicators to visualize trade-off and bundle edges of the triangle. Ecosystem management strategies that relationships (e.g., Groot et al. 2010; Hu et al. 2015). reached the vertices and edges of the NFF triangles existed, but the number of these strategies was small. Selective cut- Coupling landscape models and indirect driver models ting of forestry was the important key response option to achieve nature-positive futures. The response options to A major limitation of this study was that only direct driv- achieve Pareto optimal strategies differed between NFF ers were modeled. Thus, policy implications for indirect visions. Not only forestry and pastureland management, but drivers, leverage points, and levers are limited to those also renewable energy installation altered the consequences. closely related to ecosystem management activities. In In addition, only a few strategies were able to consistently climate change research, a broad range of indirect drivers maximize or maintain the same value perspectives, suggest- are discussed, including lifestyle changes and food system ing the importance of setting visions for landscape man- transformation. For example, the identification of hotspots agement that can be sustained medium to long term. These with a high impact on C O emissions enabled the design results also imply the potential for stakeholder collaboration of levers and leverage points for lifestyle changes using a within the region. Further local practices in scenarios and participatory approach at the city scale (IGES 2019; Koide modeling that explicitly incorporate changing stakeholder et al. 2021a, b). values and indirect drivers are needed to envision nature- Some landscape modeling studies have incorporated positive and holistic transformative changes. agents of ecosystem managers to simulate feedback between Supplementary Information The online version contains supplemen- socio-ecological systems (Kim et al. 2021). These studies tary material available at https://doi. or g/10. 1007/ s11625- 023- 01301-8 . have primarily focused on agent-based landscape change modeling (Gibon et al. 2010; Sotnik 2018; Sotnik et al. Acknowledgements The authors thank Dr. Takahiro Inoue for helping with the species parameter and environment data survey. This research 2021), which focuses on specific stakeholders such as for - contributes to Japan Long Term Ecological Research (JaLTER) and the estry and agricultural workers. For example, an extension of International Long Term Ecological Research (ILTER) network. The LANDIS-II can simulate the social learning of agents and 1 3 Sustainability Science authors would like to thank Editage (www. edita ge. com) for English Akkeshi town (2018) Statistics of Akkeshi town 2017 (in Japanese). language editing.https:// www. akkes hi- town. jp/ gyosei/ tokei/ tokei sho/ Akkeshi town (2019a) Bekambeushi watershed. https://www .akk eshi- Author contributions Conceptualization: CH, MM, TM, SH, and OS; town. jp/ kanko/ kanko 10/ bekan nbeus hi/ methodology: CH, MM, WH, TM, MN, JM, HS, SH; formal analysis Akkeshi town (2019b) Self-reliance promotion project in Akkeshi and investigation: CH, MM, TM; writing—original draft preparation: town. (in Japanese). https:// www. akkes hi- town. jp/ file/ conte nts/ CH, MM, writing—review and editing: all authors; resources: JM, HS; 292/ 5223/ h29- R02ka sokei kaku. pdf funding acquisition: CH, JM, HS, OS; supervision: TM, MN, JM, HS, Benson HP (2009) Multi-objective optimization: pareto optimal solu- SH, OS, SO, HJK, GP. tions, properties multi-objective optimization: pareto optimal solutions, properties. In: Floudas CA, Pardalos PM (eds) Ency- Funding Open access funding provided by Osaka University. This clopedia of optimization. Springer US, Boston, pp 2478–2481 research was funded by the Environment Research and Technol- Bezanson J, Edelman A, Karpinski S, Shah VB (2017) Julia: a fresh ogy Development Fund (JPMEERF16S11500 and 1FS-2201) of the approach to numerical computing. SIAM Rev 59:65–98. https:// Environmental Restoration and Conservation Agency of Japan; JSPS doi. org/ 10. 1137/ 14100 0671 KAKENHI Grant numbers 17H01516 and 18J20266; JST e-ASIA Biodiversity Center of Japan (2017) GIS data of 1:25,000 scale vegeta- JRP Grant number JPMJSC20E6; JST Belmont Forum Grant number tion map. http:// gis. biodic. go. jp/ webgis/ JPMJBF2102; and Grant Program for Doctoral Course Students from CAO (2022) Regional Revitalization SDGs/“Environmental Future Sompo Environment Foundation. City” Concept-Regional Revitalization Promotion Secretariat. https:// www. chisou. go. jp/ tiiki/ kankyo/ index. html. Accessed 24 Data availability Input data and analysis scripts have deposited in our Jun 2022 GitHub repository: https://git hub.com/ hag achi/ Pr oject- bek ambe- NFF . 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The images or other third party material in this article are 10. 1016/j. biocon. 2019. 03. 046 included in the article's Creative Commons licence, unless indicated Coleman K, Muhammed SE, Milne AE et al (2017) The landscape otherwise in a credit line to the material. If material is not included in model: a model for exploring trade-offs between agricultural pro- the article's Creative Commons licence and your intended use is not duction and the environment. Sci Total Environ 609:1483–1499. permitted by statutory regulation or exceeds the permitted use, you will https:// doi. org/ 10. 1016/j. scito tenv. 2017. 07. 193 need to obtain permission directly from the copyright holder. To view a Editorial Committee of history of akkeshi town (2012) New history of copy of this licence, visit http://cr eativ ecommons. or g/licen ses/ b y/4.0/ . Akkeshi town. Gyousei, Akkeshi town ESGF-CoG: CMIP-5 (2017). https://esgf- node. llnl. go v/sear ch/cmip5/ . Accessed 08 Feb 2018. 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Sustainability Science – Springer Journals

Published: Mar 10, 2023

Keywords: Scenario analysis; Forestry; Agriculture; Forest landscape model; Nature positive

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Modeling desirable futures at local scale by combining the nature futures framework and multi-objective optimization

Modeling desirable futures at local scale by combining the nature futures framework and multi-objective optimization

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Modeling desirable futures at local scale by combining the nature futures framework and multi-objective optimization

Modeling desirable futures at local scale by combining the nature futures framework and multi-objective optimization

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