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Protected areas (PAs) are vital for conserving biodiversity, but many PA networks consist of fragmented habitat patches that poorly represent species and ecosystems. One possible solution is to create conservation landscapes that surround and link these PAs. This often involves working with a range of landowners and agencies to develop large-scale conservation initiatives (LSCIs). These initiatives are being championed by both government and civil society, but we lack data on whether such landscape-level approaches overcome the limitations of more traditional PA networks. Here we expand on a previous gap analysis of England to explore to what extent LSCIs improve the representation of different ecoregions, land-cover types and elevation zones compared to the current PA system. Our results show the traditional PA system covers 6.37% of England, an addition of only 0.07% since 2001, and that it is an ecologically unrepresentative network that mostly protects agriculturally unproductive land. Including LSCIs in the analysis increases the land for conservation more than tenfold and reduces these representation biases. However, only 24% of land within LSCIs is currently under conservation management, mostly funded through agri-environment schemes, and limited monitoring data mean that their contribution to conservation objectives is unclear. There is also a considerable spatial overlap between LSCIs, which are managed by different organisations with different conservation objectives. Our analysis is the first to show how Other Effective Area-Based Conservation Measures (OECMs) can increase the representativeness of conservation area networks, and highlights opportunities for increased collaboration between conservation organisations and engagement with landowners.

Many ecosystem services (ES) models exist to support sustainable development decisions. However, most ES studies use only a single modelling framework and, because of a lack of validation data, rarely assess model accuracy for the study area. In line with other research themeswhich have high model uncertainty, such as climate change, ensembles of ES models may better serve decision-makers by providing more robust and accurate estimates, as well as provide indications of uncertainty when validation data are not available. To illustrate the benefits of an ensemble approach, we highlight the variation between alternative models, demonstrating that there are large geographic regionswhere decisions based on individual models are not robust.We test if ensembles are more accurate by comparing the ensemble accuracy of multiple models for six ES against validation data across sub-Saharan Africa with the accuracy of individual models. We find that ensembles are better predictors of ES, being 5.0–6.1%more accurate than individualmodels.We also find that the uncertainty (i.e. variation among constituent models) of the model ensemble is negatively correlated with accuracy and so can be used as a proxy for accuracy when validation is not possible (e.g. in data-deficient areas or when developing scenarios). Since ensembles are more robust, accurate and convey uncertainty, we recommend that ensemble modelling should be more widely implemented within ES science to better support policy choices and implementation. ; The UK Ecosystem Services for Poverty Alleviation program (ESPA; www.espa.ac.uk) and 'EnsemblES - Using ensemble techniques to capture the accuracy and sensitivity of ecosystem service models and the Spanish Government through María de Maeztu excellence accreditation 2018-2021. ; http://www.elsevier.com/locate/scitotenv ; am2021 ; Zoology and Entomology

Biosphere reserves (BR) balance biodiversity protection and sustainable use through different management restrictions in three zones: core areas, buffer zones, and transition areas. Information about the links between zoning and ecosystem services (ES) is lacking, particularly in terms of the relative roles of natural contributions (ecosystem properties and functions) and anthropogenic contributions (human inputs such as technology and infrastructure) in coproducing ES. This study aimed to: (1) analyse how coproduction of four ES (crop production, grazing, timber production, recreation) differs across the three zones of BRs; and (2) understand which predictors (zoning, natural and anthropogenic contributions, other environmental characteristics) best explain ES provision within BRs. To do this, we collected spatial data on 137 terrestrial BRs in the European Union and on 16 indicators of ES coproduction. We used non-parametric pairwise Wilcoxon rank sum tests to calculate differences in indicators between zones. We used model selection and multiple linear regression to identify predictors of ES provision patterns. Anthropogenic contributions showed most differences between zones, with contributions generally increasing from buffer zones to transition areas. Natural contributions did not, on average, differ between zones, however, for recreation and crop production they decreased from buffer zones to transition areas. ES provision differed between zones only for crop production and grazing, which increased from buffer zones to transition areas. Regression analysis showed that natural contributions are the best predictors of ES provision for all four services. Our results indicate that zoning of BRs has implications for ES coproduction.

The Intergovernmental Platform for Biodiversity and Ecosystem Services (IPBES), established in 2012 to counter the biodiversity crisis, requires the best scientific input available to function as a successful science-policy interface that addresses the knowledge needs of governments for safeguarding nature and its services. For the macroecological research community, IPBES presents a great opportunity to contribute knowledge, data and methods, and to help identify and address knowledge gaps and methodological impediments. Here, we outline our perspectives on how macroecology may contribute to IPBES. We focus on three essential topics for the IPBES process, where contributions by macroecologists will be invaluable: biodiversity data, biodiversity modelling, and modelling of ecosystem services. For each topic, we discuss the potential for contributions from the macroecological community, as well as limitations, challenges, and knowledge gaps. Overall, engagement of the macroecological community with IPBES should lead to mutual benefits. Macroecologists may profit as their contributions to IPBES may strengthen and inspire them as a community to design and conduct research that provides society-relevant results. Furthermore, macroecological contributions will help IPBES become a successful instrument of knowledge exchange and uncover the linkages between biodiversity and human well-being.

Biosphere reserves (BR) balance biodiversity protection and sustainable use through different management restrictions in three zones: core areas, buffer zones, and transition areas. Information about the links between zoning and ecosystem services (ES) is lacking, particularly in terms of the relative roles of natural contributions (ecosystem properties and functions) and anthropogenic contributions (human inputs such as technology and infrastructure) in coproducing ES. This study aimed to: (1) analyse how coproduction of four ES (crop production, grazing, timber production, recreation) differs across the three zones of BRs; and (2) understand which predictors (zoning, natural and anthropogenic contributions, other environmental characteristics) best explain ES provision within BRs. To do this, we collected spatial data on 137 terrestrial BRs in the European Union and on 16 indicators of ES coproduction. We used non-parametric pairwise Wilcoxon rank sum tests to calculate differences in indicators between zones. We used model selection and multiple linear regression to identify predictors of ES provision patterns. Anthropogenic contributions showed most differences between zones, with contributions generally increasing from buffer zones to transition areas. Natural contributions did not, on average, differ between zones, however, for recreation and crop production they decreased from buffer zones to transition areas. ES provision differed between zones only for crop production and grazing, which increased from buffer zones to transition areas. Regression analysis showed that natural contributions are the best predictors of ES provision for all four services. Our results indicate that zoning of BRs has implications for ES coproduction.

Protected areas (PAs) are vital for conserving biodiversity, but many PA networks consist of fragmented habitat patches that poorly represent species and ecosystems. One possible solution is to create conservation landscapes that surround and link these PAs. This often involves working with a range of landowners and agencies to develop large-scale conservation initiatives (LSCIs). These initiatives are being championed by both government and civil society, but we lack data on whether such landscape-level approaches overcome the limitations of more traditional PA networks. Here we expand on a previous gap analysis of England to explore to what extent LSCIs improve the representation of different ecoregions, land-cover types and elevation zones compared to the current PA system. Our results show the traditional PA system covers 6.37% of England, an addition of only 0.07% since 2001, and that it is an ecologically unrepresentative network that mostly protects agriculturally unproductive land. Including LSCIs in the analysis increases the land for conservation more than tenfold and reduces these representation biases. However, only 24% of land within LSCIs is currently under conservation management, mostly funded through agri-environment schemes, and limited monitoring data mean that their contribution to conservation objectives is unclear. There is also a considerable spatial overlap between LSCIs, which are managed by different organisations with different conservation objectives. Our analysis is the first to show how Other Effective Area-Based Conservation Measures (OECMs) can increase the representativeness of conservation area networks, and highlights opportunities for increased collaboration between conservation organisations and engagement with landowners.

Faced with environmental degradation, governments worldwide are developing policies to safeguard ecosystem services (ES). Many ES models exist to support these policies, but they are generally poorly validated, especially at large scales, which undermines their credibility. To address this gap, we describe a study of multiple models of five ES, which we validate at an unprecedented scale against 1675 data points across sub-Saharan Africa. We find that potential ES (biophysical supply of carbon and water) are reasonably well predicted by the existing models. These potential ES models can also be used as inputs to new models for realised ES (use of charcoal, firewood, grazing resources and water), by adding information on human population density. We find that increasing model complexity can improve estimates of both potential and realised ES, suggesting that developing more detailed models of ES will be beneficial. Furthermore, in 85% of cases, human population density alone was as good or a better predictor of realised ES than ES models, suggesting that it is demand, rather than supply that is predominantly determining current patterns of ES use. Our study demonstrates the feasibility of ES model validation, even in data-deficient locations such as sub-Saharan Africa. Our work also shows the clear need for more work on the demand side of ES models, and the importance of model validation in providing a stronger base to support policies which seek to achieve sustainable development in support of human well-being.

Faced with environmental degradation, governments world-wide are developing policies to safeguard ecosystem services (ES). Many ES models exist to support these policies, but they are generally poorly validated, especially at large scales, which undermines their credibility. To address this gap, we describe a study of multiple models of five ES, which we validate at an unprecedented scale against 1,675 data points across sub-Saharan Africa. We find that potential ES (biophysical supply of carbon and water) are reasonably well predicted by existing models. These potential ES models can also be used as inputs to new models for realised ES (use of charcoal, firewood, grazing resources and water), by adding information on human population density. We find that increasing model complexity can improve estimates of both potential and realised ES, suggesting that developing more detailed models of ES will be beneficial. Furthermore, in 85% of cases, human population density alone was as good or a better predictor of realised ES than ES models, suggesting that it is demand, rather than supply that is predominantly determining current patterns of ES use. Our study demonstrates the feasibility of ES model validation, even in data-deficient locations such as sub-Saharan Africa. Our work also shows the clear need for more work on the supply-side of ES models, and the importance of model validation in providing a stronger base to support policies which seek to achieve sustainable development in support of human well-being.

Faced with environmental degradation, governments worldwide are developing policies to safeguard ecosystem services (ES). Many ES models exist to support these policies, but they are generally poorly validated, especially at large scales, which undermines their credibility. To address this gap, we describe a study of multiple models of five ES, which we validate at an unprecedented scale against 1675 data points across sub-Saharan Africa. We find that potential ES (biophysical supply of carbon and water) are reasonably well predicted by the existing models. These potential ES models can also be used as inputs to new models for realised ES (use of charcoal, firewood, grazing resources and water), by adding information on human population density. We find that increasing model complexity can improve estimates of both potential and realised ES, suggesting that developing more detailed models of ES will be beneficial. Furthermore, in 85% of cases, human population density alone was as good or a better predictor of realised ES than ES models, suggesting that it is demand, rather than supply that is predominantly determining current patterns of ES use. Our study demonstrates the feasibility of ES model validation, even in data-deficient locations such as sub-Saharan Africa. Our work also shows the clear need for more work on the demand side of ES models, and the importance of model validation in providing a stronger base to support policies which seek to achieve sustainable development in support of human well-being.

Faced with environmental degradation, governments worldwide are developing policies to safeguard ecosystem services (ES). Many ES models exist to support these policies, but they are generally poorly validated, especially at large scales, which undermines their credibility. To address this gap, we describe a study of multiple models of five ES, which we validate at an unprecedented scale against 1675 data points across sub-Saharan Africa. We find that potential ES (biophysical supply of carbon and water) are reasonably well predicted by the existing models. These potential ES models can also be used as inputs to new models for realised ES (use of charcoal, firewood, grazing resources and water), by adding information on human population density. We find that increasing model complexity can improve estimates of both potential and realised ES, suggesting that developing more detailed models of ES will be beneficial. Furthermore, in 85% of cases, human population density alone was as good or a better predictor of realised ES than ES models, suggesting that it is demand, rather than supply that is predominantly determining current patterns of ES use. Our study demonstrates the feasibility of ES model validation, even in data-deficient locations such as sub-Saharan Africa. Our work also shows the clear need for more work on the demand side of ES models, and the importance of model validation in providing a stronger base to support policies which seek to achieve sustainable development in support of human well-being.