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Investigating the influence of coastal development on marine environments is a priority to maintain healthy seas. Cetaceans are top predators, keystone and umbrella species and thus are good candidate models to evaluate the extent of anthropogenic impacts on coastal habitats. We employed a generalized linear model with spatial eigenvector mapping (SEV-GLM) to understand the influence of environmental and anthropogenic activities on migrant (humpback whale Megaptera novaeangliae) and non-migrant (Bryde’s whale Balaenoptera brydei and common bottlenose dolphin Tursiops truncatus) cetacean habitat use off Cabo Frio, Rio de Janeiro, Brazil. We hypothesized that both environmental and anthropogenic activities influence their habitat use. Data were collected during 118 boat trips between December 2010 and June 2014. The best SEV-GLM predicted humpback whales would increase linearly with distance to coast, with minimum sea surface temperature (SST) around 19.4–19.8°C and maximum SST around 25.5–26°C, with low variations in chlorophyll a (chl a) concentrations. The model also predicted that humpback whales would occur up to 10 km from diving areas, increasing linearly with distance to fishing grounds. The best non-migrant cetacean SEV-GLM predicted that they would occur more frequently around depths from 30–60 m, increasing with low SST and high chl a concentration. For the anthropogenic component, the model predicted that non-migrant cetaceans would occur up to 10 km from fishing grounds. Our study modeled the influence of anthropogenic activities on cetaceans, and indicates specific priority areas for cetacean conservation, contributing at a local and national scale.

Current methods in conservation planning for promoting the persistence of biodiversity typically focus on either representing species geographic distributions or maintaining connectivity between reserves, but rarely both, and take a focal species, rather than a multispecies, approach. Here, we link prioritization methods with population models to explore the impact of integrating both representation and connectivity into conservation planning for species persistence. Using data on 288 Mediterranean fish species with varying conservation requirements, we show that: (1) considering both representation and connectivity objectives provides the best strategy for enhanced biodiversity persistence and (2) connectivity objectives were fundamental to enhancing persistence of small‐ranged species, which are most in need of conservation, while the representation objective benefited only wide‐ranging species. Our approach provides a more comprehensive appraisal of planning applications than approaches focusing on either representation or connectivity, and will hopefully contribute to build more effective reserve networks for the persistence of biodiversity.

Most spatial conservation planning for wide-ranging or migratory species is constrained by poor knowledge of species’ spatiotemporal dynamics and is only based on static species’ ranges. However, species have substantial variation in abundance across their range and migratory species have important spatiotemporal population dynamics. With growing ecological data and advancing analytics, both of these can be estimated and incorporated into spatial conservation planning. However, there is limited information on the degree to which including this information affects conservation planning. We compared the performance of systematic conservation prioritizations for different scenarios based on varying the input species’ distributions by ecological metric (abundance distributions versus range maps) and temporal sampling resolution (weekly, monthly, or quarterly). We used the example of a community of 41 species of migratory shorebirds that breed in North America, and we used eBird data to produce weekly estimates of species’ abundances and ranges. Abundance distributions at a monthly or weekly resolution led to prioritizations that most efficiently protected species throughout the full annual cycle. Conversely, spatial prioritizations based on species’ ranges required more sites and left most species insufficiently protected for at least part of their annual cycle. Prioritizations with only quarterly species ranges were very inefficient as they needed to target 40% of species’ ranges to include 10% of populations. We highlight the high value of abundance information for spatial conservation planning, which leads to more efficient and effective spatial prioritization for conservation. Overall, we provide evidence that spatial conservation planning for wide-ranging migratory species is most robust and efficient when informed by species’ abundance information from the full annual cycle.

Climate change is already having profound effects on biodiversity, but climate change adaptation has yet to be fully incorporated into area-based management tools used to conserve biodiversity, such as protected areas. One main obstacle is the lack of consensus regarding how impacts of climate change can be included in spatial conservation plans. We propose a climate-smart framework that prioritizes the protection of climate refugia—areas of low climate exposure and high biodiversity retention—using climate metrics. We explorefour aspects of climate-smart conservation planning: (1) climate model ensembles; (2) multiple emission scenarios; (3) climate metrics; and (4) approaches to identifying climate refugia. We illustrate this framework in the Western Pacific Ocean, but it is equally applicable to terrestrial systems. We found that all aspects of climate-smart conservation planning considered affected the configuration of spatial plans. The choice of climate metrics and approaches to identifying refugia have large effects in the resulting climatesmart spatial plans, whereas the choice of climate models and emission scenarios have smaller effects. As the configuration of spatial plans depended on climate metrics used, a spatial plan based on a single measure of climate change (e.g., warming) will not necessarily be robust against other measures of climate change (e.g., ocean acidification). We therefore recommend using climate metrics most relevant for the biodiversity and region considered based on a single or multiple climate drivers. To include the uncertainty associated with different climate futures, we recommend using multiple climate models (i.e., an ensemble) and emission scenarios. Finally, we show that the approaches we used to identify climate refugia feature trade-offs between: (1) the degree to which they are climate-smart, and (2) their efficiency in meeting conservation targets. Hence, the choice of approach will depend on the relative value that stakeholders place on climate adaptation. By using this framework, protected areas can be designed with improved longevity and thus safeguard biodiversity against current and future climate change. We hope that the proposed climate-smart framework helps transition conservation planning toward climate-smart approaches.

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. Las políticas y las investigaciones cada vez más se enfocan en la protección de los servicios ambientales (SAs) por medio de la conservación de áreas prioritarias. Las áreas prioritarias para los SAs deberían ser identificadas con base en la capacidad de SAs y la demanda de SAs, y deberían representar las conexiones entre las áreas de capacidad de SAs y la demanda (flujo), resultando así en áreas de conexiones únicas de demanda y suministro (zonas de flujo). Probamos maneras para representar la demanda de SAs y las zonas de flujo para identificar las áreas prioritarias en la Unión Europea. Mapeamos la capacidad y la demanda de un SA global (secuestro de carbono), regional (regulación de inundación), y tres locales (calidad del aire, polinización, y tiempo libre urbano). Usamos el software Zonation para identificar las áreas prioritarias para los SAs con base en seis experimentos: con y sin representación de la demanda de los SAs, y cuatro experimentos que representaron el efecto de la zona de flujo de los SAs. Sólo hubo un traslape de 37.1 % entre el 25 % de las áreas prioritarias que englobaron la mayoría de los SAs con y sin representación de la demanda de SAs. El nivel de los SAs que se mantuvo en las áreas prioritarias incrementó de un 23.2 % a 57.9 % después de considerar la demanda de los SAs, especialmente para aquellos SAs con una zona de flujo reducida. Representar la zona de flujo tuvo un pequeño efecto sobre la ubicación de las áreas prioritarias y el nivel de SAs que se mantuvo, pero resultó en menos zonas de flujo sin SAs mantenidos en relación a ignorar las zonas de flujo. Representar la demanda y las zonas de flujo mejoró la representación y distribución de los SAs con zonas de flujo de regionales a locales sin compensaciones grandes en relación al SA global. Hallamos que ignorar la demanda de SAs llevó a la identificación de las áreas prioritarias en las regiones remotas en donde los beneficios de la capacidad de los SAs para la sociedad fueron pequeños. Incorporar los SAs a la planeación de la conservación por lo tanto debería siempre representar a la demanda de los SAs para identificar una red efectiva de prioridades para los SAs. reducida. Representar la zona de flujo tuvo un pequeño efecto sobre la ubicación de las áreas prioritarias y el nivel de SAs que se mantuvo, pero resultó en menos zonas de flujo sin SAs mantenidos en relación a ignorar las zonas de flujo. Representar la demanda y las zonas de flujo mejoró la representación y distribución de los SAs con zonas de flujo de regionales a locales sin compensaciones grandes en relación al SA global. Hallamos que ignorar la demanda de SAs llevó a la identificación de las áreas prioritarias en las regiones remotas en donde los beneficios de la capacidad de los SAs para la sociedad fueron pequeños. Incorporar los SAs a la planeación de la conservación por lo tanto debería siempre representar a la demanda de los SAs para identificar una red efectiva de prioridades para los SAs.

Reversing large-scale degradation and deforestation goes beyond what can be achieved by site-level ecological restoration. Forest and landscape restoration focuses on spatial scales beyond the \"site\" level, where multiple land uses and forms of land ownership coexist, and where management decisions are usually made by different sets of stakeholders. In this context, natural regeneration can be a cost-effective approach to expand buffer zones of protected areas or forest reserves, create new forest patches and riparian zones, and create biological corridors to link existing protected areas. Here, I describe different modalities of natural regeneration, describe their benefits and features, and present several case studies of large-scale natural regeneration. Regrowing forests are often ignored, and their ecological and economic value remains largely unrecognized. Effective incentives for landowners and local communities are needed to encourage and protect naturally regenerating forests on farms. Predicting and mapping areas with a high capacity for natural regeneration will lower the overall costs of implementing restoration at local, regional, and national levels and may permit larger areas to be restored. Regrowing tropical forests will play an increasingly important role in climate change mitigation and biodiversity conservation in our future uncertain world.

1. Spatial prioritization tools provide a means of finding efficient trade-offs between biodiversity protection and the delivery of ecosystem services. Although a large number of prioritization approaches have been proposed in the literature, most are specifically designed for terrestrial systems. When applied to river ecosystems, they often fail to adequately account for the essential role that landscape connectivity plays in maintaining both biodiversity and ecosystem services. This is particularly true of longitudinal connectivity, which in many river catchments is highly altered by the presence of dams, stream-road crossings, and other artificial structures. 2. We propose a novel framework for coordinating river conservation and connectivity restoration. As part of this, we formulate an optimization model for deciding which subcatchments to designate for ecosystem services and which to include in a river protected area (RPA) network, while also deciding which existing river barriers to remove in order to maximize longitudinal connectivity within the RPA network. In addition to constraints on the size and makeup of the RPA network, the model also considers the suitability of sites for conservation, based on a biological integrity index, and connectivity to multiple habitat types. We demonstrate the usefulness of our approach using a case study involving four managed river catchments located in Hungary. 3. Results show that large increases in connectivity-weighted habitat can be achieved through targeted selection of barrier removals and that the benefits of barrier removal are strongly depend on RPA network size. We find that (i) highly suboptimal solutions are produced if habitat conservation planning and connectivity restoration are done separately and (ii) RPA acquisition provides substantially greater marginal benefits than barrier removal given limited resources. 4. Synthesis and applications. Finding a balance between conservation and ecosystem services provision should give more consideration to connectivity restoration planning, especially in multi-use riverscapes. We present the first modelling framework to directly integrate and optimize river conservation and connectivity restoration planning. This framework can help conservation managers to account better for connectivity, resulting in more effective catchment scale maintenance of biological integrity and ecosystem services delivery.

AimGlobal changes are predicted to have severe consequences for biodiversity; 34 biodiversity hotspots have become international priorities for conservation, with important efforts allocated to their preservation, but the potential effects of global changes on hotspots have so far received relatively little attention. We investigate whether hotspots are quantitatively and qualitatively threatened to the same order of magnitude by the combined effects of global changes. LocationWorldwide, in 34 biodiversity hotspots. MethodsWe quantify (1) the exposure of hotspots to climate change, by estimating the novelty of future climates and the disappearance of extant climates using climate dissimilarity analyses, (2) each hotspot's vulnerability to land modification and degradation by quantifying changes in land-cover variables over the entire habitat, and (3) the future suitability of distribution ranges of 100 of the world's worst invasive alien species', by characterizing the combined effects of climate and land-use changes on the future distribution ranges of these species. ResultsOur findings show that hotspots may experience an average loss of 31% of their area under analogue climate, with some hotspots more affected than others (e.g. Polynesia-Micronesia). The greatest climate change was projected in low-latitude hotspots. The hotspots were on average suitable for 17% of the considered invasive species. Hotspots that are mainly islands or groups of islands were disproportionally suitable for a high number of invasive species both currently and in the future. We also showed that hotspots will increase their area of pasture in the future. Finally, combining the three threats, we identified the Atlantic forest, Cape Floristic Region and Polynesia-Micronesia as particularly vulnerable to global changes. Main conclusionsGiven our estimates of hotspot vulnerability to changes, close monitoring is now required to evaluate the biodiversity responses to future changes and to test our projections against observations.

Context Systematic Conservation Planning (SCP) seeks to optimize biodiversity outcomes under real-world constraints, including limited financial and social resources. Integrating cost data into spatial prioritization tools is essential for effective conservation, yet current applications lack consistent guidance on the selection and use of cost metrics. Objectives This study aims to develop a structured roadmap for cost integration in SCP and assess how alternative types of cost data are used to inform spatial prioritization. We focus on the classification of cost metrics and the ways they are implemented and aggregated in spatial prioritization tools. Methods We developed a structured roadmap for cost integration in spatial prioritization tools, based on a systematic analysis of 139 peer-reviewed studies. From these, we examined the alternative layers of information and aggregation methods used to achieve conservation targets. Results Costs were classified into two main categories: monetary (i.e. opportunity, acquisition, management, and transaction costs) and proxy metrics. Six proxy approaches were identified: area-based, distance-based, anthropogenic uses and activities, human pressures, ecological impacts, and other unclassified metrics. Aggregation methods range from single-layer to cumulative and multi-layer approaches. Cost inclusion often remains simplistic, overlooking synergies and uncertainties. Gaps include limited use of spatially explicit socio-economic and ecological data, aggregation methods, and metrics for emerging threats. Conclusions This roadmap offers evidence-based guidance for cost selection and implementation in spatial prioritization tools, and insights that support more socially equitable and operationally effective conservation planning. Addressing current gaps will be critical to designing socially and ecologically sound conservation strategies in the face of climate change.

For biodiversity protection to play a persuasive role in land-use planning, conservationists must be able to offer objective systems for ranking which natural areas to protect or convert. Representing biodiversity in spatially explicit indices is challenging because it entails numerous judgments regarding what variables to measure, how to measure them, and how to combine them. Surprisingly few studies have explored this variation. Here, we explore how this variation affects which areas are selected for agricultural conversion by a land-use prioritization model designed to reduce the biodiversity losses associated with agricultural expansion in Zambia. We first explore the similarity between model recommendations generated by three recently published composite indices and a commonly used rarity-weighted species richness metric. We then explore four underlying sources of ecological and methodological variation within these and other approaches, including different terrestrial vertebrate taxonomic groups, different species-richness metrics, different mathematical methods for combining layers, and different spatial resolutions of inputs. The results generated using different biodiversity approaches show very low spatial agreement regarding which areas to convert to agriculture. There is little overlap in areas identified for conversion using previously published indices (mean Jaccard similarity, Jw , between 0.3 and 3.7%), different taxonomic groups (5.0% < mean Jw < 13.5%), or different measures of species richness (15.6% < mean Jw < 33.7%). Even with shared conservation goals, different methods for combining layers and different input spatial resolutions still produce meaningful, though smaller, differences among areas selected for conversion (40.9% < mean Jw < 67.5%). The choice of taxonomic group had the largest effect on conservation priorities, followed by the choice of species richness metric, the choice of combination method, and finally the choice of spatial resolution. These disagreements highlight the challenge of objectively representing biodiversity in land-use planning tools, and present a credibility challenge for conservation scientists seeking to inform policy making. Our results suggest an urgent need for a more consistent and transparent framework for designing the biodiversity indices used in land-use planning, which we propose here.