Integrating socioeconomic and ecological data into restoration practice

Driven by the United Nations Decade on Restoration and international funding initiatives, such as the Mangrove Breakthrough, investment in mangrove restoration is expected to increase. Yet, mangrove restoration efforts frequently fail, usually because of ad hoc site-selection processes that do not consider mangrove ecology and the socioeconomic context. Using decision analysis, we developed an approach that accounts for socioeconomic and ecological data to identify sites with the highest likelihood of mangrove restoration success. We applied our approach in the Biosphere Reserve Marismas Nacionales Nayarit, Mexico, an area that recently received funding for implementing mangrove restoration actions. We identified 468 potential restoration sites, assessed their restorability potential based on socioeconomic and ecological metrics, and ranked sites for implementation with spatial optimization. The metrics we used included favorable conditions for propagules to establish and survive under sea-level rise, provision of ecosystem services, and community dynamics. Sites that were selected based on socioeconomic or ecological metrics alone had lower likelihood of mangrove restoration success than sites that were selected based on integrated socioeconomic and ecological metrics. For example, selecting sites based on only socioeconomic metrics captured 16% of the maximum attainable value of functioning mangroves able to provide propagules to potential restoration sites, whereas selecting sites based on ecological and socioeconomic metrics captured 46% of functioning mangroves. Our approach was developed as part of a collaboration between nongovernmental organizations, local government, and academics under rapid delivery time lines given preexisting mangrove restoration implementation commitments. The systematic decision process we used integrated socioeconomic and ecological considerations even under short delivery deadlines, and our approach can be adapted to help mangrove restoration site-selection decisions elsewhere.

Integrating larval connectivity into the marine conservation decision-making process across spatial scales

Larval dispersal connectivity is typically integrated into spatial conservation decisions at regional or national scales, but implementing agencies struggle with translating these methods to local scales. We used larval dispersal connectivity at regional (hundreds of kilometers) and local (tens of kilometers) scales to aid in design of networks of no-take reserves in Southeast Sulawesi, Indonesia. We used Marxan with Connectivity informed by biophysical larval dispersal models and remotely sensed coral reef habitat data to design marine reserve networks for 4 commercially important reef species across the region. We complemented regional spatial prioritization with decision trees that combined network-based connectivity metrics and habitat quality to design reserve boundaries locally. Decision trees were used in consensus-based workshops with stakeholders to qualitatively assess site desirability, and Marxan was used to identify areas for subsequent network expansion. Priority areas for protection and expected benefits differed among species, with little overlap in reserve network solutions. Because reef quality varied considerably across reefs, we suggest reef degradation must inform the interpretation of larval dispersal patterns and the conservation benefits achievable from protecting reefs. Our methods can be readily applied by conservation practitioners, in this region and elsewhere, to integrate connectivity data across multiple spatial scales.

How decisions about fitting species distribution models affect conservation outcomes

Species distribution models (SDMs) are increasingly used in conservation and land-use planning as inputs to describe biodiversity patterns. These models can be built in different ways, and decisions about data preparation, selection of predictor variables, model fitting, and evaluation all alter the resulting predictions. Commonly, the true distribution of species is unknown and independent data to verify which SDM variant to choose are lacking. Such model uncertainty is of concern to planners. We analyzed how 11 routine decisions about model complexity, predictors, bias treatment, and setting thresholds for predicted values altered conservation priority patterns across 25 species. Models were created with MaxEnt and run through Zonation to determine the priority rank of sites. Although all SDM variants performed well (area under the curve >0.7), they produced spatially different predictions for species and different conservation priority solutions. Priorities were most strongly altered by decisions to not address bias or to apply binary thresholds to predicted values; on average 40% and 35%, respectively, of all grid cells received an opposite priority ranking. Forcing high model complexity altered conservation solutions less than forcing simplicity (14% and 24% of cells with opposite rank values, respectively). Use of fewer species records to build models or choosing alternative bias treatments had intermediate effects (25% and 23%, respectively). Depending on modeling choices, priority areas overlapped as little as 10-20% with the baseline solution, affecting top and bottom priorities differently. Our results demonstrate the extent of model-based uncertainty and quantify the relative impacts of SDM building decisions. When it is uncertain what the best SDM approach and conservation plan is, solving uncertainty or considering alterative options is most important for those decisions that change plans the most.

Using individual-based movement information to identify spatial conservation priorities for mobile species

The optimal design of reserve networks and fisheries closures depends on species occurrence information and knowledge of how anthropogenic impacts interact with the species concerned. However, challenges in surveying mobile and cryptic species over adequate spatial and temporal scales can mask the importance of particular habitats, leading to uncertainty about which areas to protect to optimize conservation efforts. We investigated how telemetry-derived locations can help guide the scale and timing of fisheries closures with the aim of reducing threatened species bycatch. Forty juvenile speartooth sharks (Glyphis glyphis) were monitored over 22 months with implanted acoustic transmitters and an array of hydrophone receivers. Using the decision-support tool Marxan, we formulated a permanent fisheries closure that prioritized areas used more frequently by tagged sharks and considered areas perceived as having high value to fisheries. To explore how the size of the permanent closure compared with an alternative set of time-area closures (i.e., where different areas were closed to fishing at different times of year), we used a cluster analysis to group months that had similar arrangements of selected planning units (informed by shark movements during that month) into 2 time-area closures. Sharks were consistent in their timing and direction of migratory movements, but the number of tagged sharks made a big difference in the placement of the permanent closure; 30 individuals were needed to capture behavioral heterogeneity. The dry-season (May-January) and wet-season (February-April) time-area closures opened 20% and 25% more planning units to fishing, respectively, compared with the permanent closure with boundaries fixed in space and time. Our results show that telemetry has the potential to inform and improve spatial management of mobile species and that the temporal component of tracking data can be incorporated into prioritizations to reduce possible impacts of spatial closures on established fisheries.

Understanding the effects of different social data on selecting priority conservation areas

Conservation success is contingent on assessing social and environmental factors so that cost-effective implementation of strategies and actions can be placed in a broad social-ecological context. Until now, the focus has been on how to include spatially explicit social data in conservation planning, whereas the value of different kinds of social data has received limited attention. In a regional systematic conservation planning case study in Australia, we examined the spatial concurrence of a range of spatially explicit social values and land-use preferences collected using a public participation geographic information system and biological data. We used Zonation to integrate the social data with the biological data in a series of spatial-prioritization scenarios to determine the effect of the different types of social data on spatial prioritization compared with biological data alone. The type of social data (i.e., conservation opportunities or constraints) significantly affected spatial prioritization outcomes. The integration of social values and land-use preferences under different scenarios was highly variable and generated spatial prioritizations 1.2-51% different from those based on biological data alone. The inclusion of conservation-compatible values and preferences added relatively few new areas to conservation priorities, whereas including noncompatible economic values and development preferences as costs significantly changed conservation priority areas (48.2% and 47.4%, respectively). Based on our results, a multifaceted conservation prioritization approach that combines spatially explicit social data with biological data can help conservation planners identify the type of social data to collect for more effective and feasible conservation actions.

Use of demand for and spatial flow of ecosystem services to identify priority areas

Policies and research increasingly focus on the protection of ecosystem services (ESs) through priority-area conservation. Priority areas for ESs should be identified based on ES capacity and ES demand and account for the connections between areas of ES capacity and demand (flow) resulting in areas of unique demand-supply connections (flow zones). We tested ways to account for ES demand and flow zones to identify priority areas in the European Union. We mapped the capacity and demand of a global (carbon sequestration), a regional (flood regulation), and 3 local ESs (air quality, pollination, and urban leisure). We used Zonation software to identify priority areas for ESs based on 6 tests: with and without accounting for ES demand and 4 tests that accounted for the effect of ES flow zone. There was only 37.1% overlap between the 25% of priority areas that encompassed the most ESs with and without accounting for ES demand. The level of ESs maintained in the priority areas increased from 23.2% to 57.9% after accounting for ES demand, especially for ESs with a small flow zone. Accounting for flow zone had a small effect on the location of priority areas and level of ESs maintained but resulted in fewer flow zones without ES maintained relative to ignoring flow zones. Accounting for demand and flow zones enhanced representation and distribution of ESs with local to regional flow zones without large trade-offs relative to the global ES. We found that ignoring ES demand led to the identification of priority areas in remote regions where benefits from ES capacity to society were small. Incorporating ESs in conservation planning should therefore always account for ES demand to identify an effective priority network for ESs.

Multispecies genetic objectives in spatial conservation planning

Growing threats to biodiversity and global alteration of habitats and species distributions make it increasingly necessary to consider evolutionary patterns in conservation decision making. Yet, there is no clear-cut guidance on how genetic features can be incorporated into conservation-planning processes, despite multiple molecular markers and several genetic metrics for each marker type to choose from. Genetic patterns differ between species, but the potential tradeoffs among genetic objectives for multiple species in conservation planning are currently understudied. We compared spatial conservation prioritizations derived from 2 metrics of genetic diversity (nucleotide and haplotype diversity) and 2 metrics of genetic isolation (private haplotypes and local genetic differentiation) in mitochondrial DNA of 5 marine species. We compared outcomes of conservation plans based only on habitat representation with plans based on genetic data and habitat representation. Fewer priority areas were selected for conservation plans based solely on habitat representation than on plans that included habitat and genetic data. All 4 genetic metrics selected approximately similar conservation-priority areas, which is likely a result of prioritizing genetic patterns across a genetically diverse array of species. Largely, our results suggest that multispecies genetic conservation objectives are vital to creating protected-area networks that appropriately preserve community-level evolutionary patterns.

Effects of preference heterogeneity among landowners on spatial conservation prioritization

The participation of private landowners in conservation is crucial to efficient biodiversity conservation. This is especially the case in settings where the share of private ownership is large and the economic costs associated with land acquisition are high. We used probit regression analysis and historical participation data to examine the likelihood of participation of Danish forest owners in a voluntary conservation program. We used the results to spatially predict the likelihood of participation of all forest owners in Denmark. We merged spatial data on the presence of forest, cadastral information on participation contracts, and individual-level socioeconomic information about the forest owners and their households. We included predicted participation in a probability model for species survival. Uninformed and informed (included land owner characteristics) models were then incorporated into a spatial prioritization for conservation of unmanaged forests. The choice models are based on sociodemographic data on the entire population of Danish forest owners and historical data on their participation in conservation schemes. Inclusion in the model of information on private landowners' willingness to supply land for conservation yielded at intermediate budget levels up to 30% more expected species coverage than the uninformed prioritization scheme. Our landowner-choice model provides an example of moving toward more implementable conservation planning.

Evaluating complementary networks of restoration plantings for landscape-scale occurrence of temporally dynamic species

Multibillion dollar investments in land restoration make it critical that conservation goals are achieved cost-effectively. Approaches developed for systematic conservation planning offer opportunities to evaluate landscape-scale, temporally dynamic biodiversity outcomes from restoration and improve on traditional approaches that focus on the most species-rich plantings. We investigated whether it is possible to apply a complementarity-based approach to evaluate the extent to which an existing network of restoration plantings meets representation targets. Using a case study of woodland birds of conservation concern in southeastern Australia, we compared complementarity-based selections of plantings based on temporally dynamic species occurrences with selections based on static species occurrences and selections based on ranking plantings by species richness. The dynamic complementarity approach, which incorporated species occurrences over 5 years, resulted in higher species occurrences and proportion of targets met compared with the static complementarity approach, in which species occurrences were taken at a single point in time. For equivalent cost, the dynamic complementarity approach also always resulted in higher average minimum percent occurrence of species maintained through time and a higher proportion of the bird community meeting representation targets compared with the species-richness approach. Plantings selected under the complementarity approaches represented the full range of planting attributes, whereas those selected under the species-richness approach were larger in size. Our results suggest that future restoration policy should not attempt to achieve all conservation goals within individual plantings, but should instead capitalize on restoration opportunities as they arise to achieve collective value of multiple plantings across the landscape. Networks of restoration plantings with complementary attributes of age, size, vegetation structure, and landscape context lead to considerably better outcomes than conventional restoration objectives of site-scale species richness and are crucial for allocating restoration investment wisely to reach desired conservation goals.

Integrating biological and social values when prioritizing places for biodiversity conservation

The consideration of information on social values in conjunction with biological data is critical for achieving both socially acceptable and scientifically defensible conservation planning outcomes. However, the influence of social values on spatial conservation priorities has received limited attention and is poorly understood. We present an approach that incorporates quantitative data on social values for conservation and social preferences for development into spatial conservation planning. We undertook a public participation GIS survey to spatially represent social values and development preferences and used species distribution models for 7 threatened fauna species to represent biological values. These spatially explicit data were simultaneously included in the conservation planning software Zonation to examine how conservation priorities changed with the inclusion of social data. Integrating spatially explicit information about social values and development preferences with biological data produced prioritizations that differed spatially from the solution based on only biological data. However, the integrated solutions protected a similar proportion of the species' distributions, indicating that Zonation effectively combined the biological and social data to produce socially feasible conservation solutions of approximately equivalent biological value. We were able to identify areas of the landscape where synergies and conflicts between different value sets are likely to occur. Identification of these synergies and conflicts will allow decision makers to target communication strategies to specific areas and ensure effective community engagement and positive conservation outcomes.