Tool For Conservation Planning Research Articles

Satellite Tracking Can Inform Population-Level Dispersal to Foraging Grounds of Post-nesting Kemp’s Ridley Sea Turtles

Understanding how populations are distributed spatially can be an effective tool for conservation and management planning. This is especially true for threatened species of mobile marine megafauna, which can use vast expanses of the ocean as foraging and reproductive habitat. Additionally, the broad distributions of these species expose them to a wide variety of threats across their range. Marine turtles are one of these mobile marine megafauna taxa, with individuals making extensive migrations between nesting beaches and foraging grounds. However, it is not well understood how many marine turtle populations distribute themselves spatially at the population or species level. In this study, 178 post-nesting female Kemp’s ridley sea turtles were concurrently satellite tracked from their primary and major secondary nesting beaches between 2010 and 2017 to determine population- and species-level distribution of females to foraging grounds. Filtered tracks and switching state-space models were used to determine the proportion of the adult female population dispersing to, and foraging in, the northern and southern Gulf of Mexico. Fidelity to Gulf regions and foraging areas were also assessed. The majority of females dispersed to northern Gulf of Mexico foraging grounds indicating 82% of the adult female population may use the region to forage. Additionally, individuals displayed fidelity to regions, key foraging areas and migratory corridors over time. These results suggest that the northern Gulf of Mexico may provide the most important foraging areas for reproductively active females of the species and threats in the region may have a disproportionately higher impact on the adult female population, a population critical to the perpetuation and recovery of the species. Results highlight that continued bi-national recovery efforts are essential for recovery of Kemp’s ridleys, as most females nest on Mexican beaches and forage in USA waters. The methodologies used could be applied to conservation and management efforts of other imperiled mobile marine megafauna.

Emergent conservation outcomes of shared risk perception in human-wildlife systems.

Human perception of risks related to economic damages caused by nearby wildlife can be transmitted through social networks. Understanding how sharing risk information within a human community alters the spatial dynamics of human-wildlife interactions has important implications for the design and implementation of effective conservation actions. We developed an agent-based model that simulates farmer livelihood decisions and activities in an agricultural landscape shared with a population of a generic wildlife species (wildlife-human interactions in shared landscapes [WHISL]). In the model, based on risk perception and economic information, farmers decide how much labor to allocate to farming and whether and where to exclude wildlife from their farms (e.g., through fencing, trenches, or vegetation thinning). In scenarios where the risk perception of farmers was strongly influenced by other farmers, exclusion of wildlife was widespread, resulting in decreased quality of wildlife habitat and frequency of wildlife damages across the landscape. When economic losses from encounters with wildlife were high, perception of risk increased and led to highly synchronous behaviors by farmers in space and time. Interactions between wildlife and farmers sometimes led to a spillover effect of wildlife damage displaced from socially and spatially connected communities to less connected neighboring farms. The WHISL model is a useful conservation-planning tool because it provides a test bed for theories and predictions about human-wildlife dynamics across a range of different agricultural landscapes.

Priority areas for conservation of and research focused on terrestrial vertebrates.

Effective conservation policies require comprehensive knowledge of biodiversity. However, knowledge shortfalls still remain, hindering possibilities to improve decision making and built such policies. During the last 2 decades, conservationists have made great efforts to allocate resources as efficiently as possible but have rarely considered the idea that if research investments are also strategically allocated, it would likely fill knowledge gaps while simultaneously improving conservation actions. Therefore, prioritizing areas where both conservation and research actions could be conducted becomes a critical endeavor that can further maximize return on investment. We used Zonation, a conservation planning tool and geographical distributions of amphibians, birds, mammals, and reptiles to suggest and compare priority areas for conservation and research of terrestrial vertebrates worldwide. We also evaluated the degree of human disturbance in both types of priority areas by describing the value of the human footprint index within such areas. The spatial concordance between priority conservation and research areas was low: 0.36% of the world's land area. In these areas, we found it would be possible to protect almost half of the currently threatened species and to gather information on nearly 42% of data-deficient (DD) species. We also found that 6199 protected areas worldwide are located in such places, although only 35% of them have strict conservation purposes. Areas of consensus between conservation and research areas represent an opportunity for simultaneously conserving and acquiring knowledge of threatened and DD species of vertebrates. Although the picture is not the most encouraging, joint conservation and research efforts are possible and should be fostered to save vertebrate species from our own ignorance and extinction.

A practical method for evaluating spatial biodiversity offset scenarios based on spatial conservation prioritization outputs

Abstract Biodiversity offsetting is a tool to balance ecological damage caused by human activity with new benefits created elsewhere. Offsetting is implemented by protecting, restoring or managing sufficiently large areas of habitat. While there are concerns about the true feasibility of offsetting, they are becoming a common policy tool world‐wide. Operationally uncomplicated, quantitative approaches to spatial analysis of offsets are rare and their use is often restricted by the availability of suitable spatial data. We describe a practical method for offsets that builds upon two layers of relatively easily sourced spatial data, a balanced spatial priority ranking and a weighted range size rarity map. Together with (a) spatial information about impact and offset areas, and (b) extra parameters for the effectiveness of avoided loss and the amount of leakage expected, we can evaluate whether the proposed offset exchange represents a credible no net loss or net positive impact with an upward trade. The priority ranking and range size rarity maps can be produced in various ways, most notably using existing conservation planning tools. Here we used the standard outputs of the Zonation spatial prioritization software. We illustrate the method and associated visualization in the context of offsetting of boreal forests in Finland, where forests experience high and increasing pressures from forestry and bioenergy sectors. The example is timely as there is political demand for the uptake of biodiversity offset policies in Finland, and boreal forests are the most common biotope. The methods described here are applicable to biomes around the world. The described tools are made available as r scripts that utilize standard Zonation outputs, thus providing direct linkage to any past or future Zonation applications. As a limitation, the present methods only apply to avoided loss offsets.

Прогнозирование распространения и численности редких видов по параметрам ландшафта (на примере авифауны в Липецкой области)

Цель работы заключалась в прогнозировании численности редких видов на основе ландшафтных факторов их распространения. Методы. Прогнозное моделирование выполнено на примере всех гнездящихся видов авифауны в Липецкой области, внесенных в Красную книгу региона. Установлено 60 таких видов птиц, координаты 1165 их гнездовых участков мы внесли в ГИС и проанализировали по регулярной сети квадратов 10 x10 км в рамках исследуемой территории. Для каждого из 220 полученных квадратов вычисляли количество гнездовых участков редких видов, а также 13 характеристик ландшафта – на основе карт и данных дистанционного зондирования. Полученный массив переменных анализировали с помощью метода главных компонент (ГК) и географически взвешенной Пуассоновской регрессии (ГВПР). Результаты. ГК объединили исследуемые переменные в пять факторов, отражающие 84 % дисперсии, из которых выделены наиболее статистически значимые для предпринятого моделирования предикторы: средняя высота местности, количество водоемов, лесистость и доля площади населенных пунктов на квадрат. Построенная на их основе ГВПР, отразила 68 % варьирования данных и позволила спрогнозировать численность редких видов птиц для слабо обследованных районов. Сравнение прогнозной и учтенной численности по квадратам позволило установить территории высокой природоохранной ценности и перспективные территории для дополнительных орнитологических обследований. Выводы: мы установили факторы размещения редких видов птиц и выполнили пространственный прогноз их численности для Липецкой области. Результаты исследования необходимы для мониторинга биоразнообразия и природоохранного планирования в регионе.

Habitat mapping of Cabo Girão Marine Park (Madeira island): a tool for conservation and management

At the time Cabo Girao natural marine park was proposed (2016) and later when it was established, (2017), its effective conservation and marine spatial planning goals were hampered by the scarce knowledge on the sea floor habitats and biotic patterns inside the area. In the present work, a combination of different datasets and underwater surveys was used to produce the first habitat map of the Natural Marine Park of Cabo Girao, which is also the first habitat map for Madeira island. Five major habitats were identified, including two previously unknown for Madeira — Avrainvillea canariensis meadows and maerl beds. 132 marine taxa (macroflora, macrofauna and fishes) were identified, including 18 commercially important species and three vulnerable fish species. The results show that the methods used are useful to produce reliable information with limited resources. The information obtained is a tool for conservation and marine spatial planning, which allows for improved policy-making and better management. This study provides a baseline for the benthic habitats of this MPA against which future marine biodiversity changes can be recognised.

Diversity loss and changes in saproxylic beetle assemblages following a high-severity fire in Araucaria–Nothofagus forests

Forest fires have increased in frequency worldwide due to global warming, drought, and land-use change. These fire-regime changes have altered the dynamics of deadwood accumulation in forests, which can affect biological communities dependent on this resource. We analyzed the effect of high-severity fire events on saproxylic beetle assemblages, which specialize in using deadwood that accumulates after disturbances. We compared assemblages in sites severely burned in 2002 to those in unburned sites in Chilean Araucaria–Nothofagus forests. Insects were collected using window-interception traps from spring 2017 to summer 2018. Rarefaction–interpolation curves revealed a significant decrease in diversity as a result of fire. Permutational multivariate analysis of variance showed a significant effect of fire on insect community structure. In addition, we observed that species turnover contributing most, and nestedness to a lesser extent, to differences in s-diversity between burned and unburned sites. Species replacement was associated with an increase in abundance of xylophagous (deadwood feeders) and a decrease in abundance of mycophagous (fungivorous) insects with fire. Therefore, our results suggest that fire causes a reduction in diversity while benefiting the abundance of xylophagous beetles in Araucaria–Nothofagus forests. We recommend the use of these insects as an evaluation tool in conservation planning, management practices, and ecological restoration efforts in burned forests in southern Chile.

Predictive modeling of suitable habitat for deep-sea corals offshore the Northeast United States

Deep-sea corals (DSCs) are important living marine resources, forming both oases of biodiversity and three-dimensional habitat structure for fishes and invertebrates. However, because of logistical difficulties and expense of deep-sea exploration, much less is known about the distribution of DSCs than is known for their shallow-water counterparts. Predictive modeling, therefore, is essential for estimating the extent of DSC habitat in areas that are unexplored in order to support conservation efforts, to provide information for effective management of offshore activities affecting the seafloor, and for future exploration and research. In support of research and management efforts in the U.S. Northeast (Cape Hatteras, NC north to the Canadian border), we developed a comprehensive set of habitat suitability models covering this entire geographic region for nine taxonomic groups of DSCs (Alcyonacea, gorgonian corals, non-gorgonian corals, Scleractinia, Caryophylliidae, Flabellidae, Pennatulacea, Sessiliflorae, and Subselliflorae). Maximum entropy (MaxEnt) models were fit to DSC presence records and spatially-explicit environmental predictors depicting depth and seafloor topography, surficial sediment characteristics, and oceanography. A stepwise model selection procedure was then implemented to identify the set of predictor variables that maximized predictive performance for each taxonomic group. To allow for comparisons across taxonomic groups, the standard MaxEnt logistic predictions were converted into calibrated classes of habitat suitability. Overall, model performance was high for all taxonomic groups. Model fit was best for Caryophylliidae, Sessiliflorae, and Flabellidae, whereas model stability was greatest for the three taxonomic groups of Alcyonacea. Model results reported here corroborate known distributions of corals in the region. For example, large structure-forming taxa are predicted to occur mainly in canyon environments, particularly in areas of steep slope (>30°); sea pens in softer sediments of the continental shelf and slope. Additionally, the models successfully predicted DSC locations during field testing. Despite the limitations of presence-only data, several novel extensions to the traditional MaxEnt analysis workflow improved model selection, accuracy assessment, and comparability of results across taxonomic groups. This approach, when integrated with management processes, could be a powerful tool for science-based conservation, management, and spatial planning for these marine resources.

Comparing Agricultural Conservation Planning Framework (ACPF) practice placements for runoff mitigation and controlled drainage among 32 watersheds representing Iowa landscapes

Precision conservation planning tools can use high-resolution data to identify conservation practice-placement options for watershed improvement plans. Use of these tools across multiple watersheds could help to identify regional conservation strategies. This study evaluated practice-placement options determined using the Agricultural Conservation Planning Framework (ACPF) ArcGIS tools for controlled drainage (CD), contour buffer strips (CBS), water and sediment control basins (WASCOBs), and grassed waterways (GWWs) across 32 headwater hydrological unit code (HUC)12 watersheds in Iowa. The watersheds represented three Major Land Resource Areas (MLRAs) and four Agro-Hydrologic Landscape (AHL) classes, with four watersheds randomly chosen from each of eight combined MLRA-AHL landscape groupings. Placement options for the practices identified using the ACPF were quantified by watershed as densities (km km−2 of cropland) for GWWs, counts of proposed practice locations per square kilometer for CBS and WASCOBs, and as fractions of tile-drained land for CD. The influence of the landscape groupings on practice-placement densities among watersheds was tested using analysis of variance and contrast comparisons. Significant differences were found that led to nuanced interpretations. Differences attributed to slope steepness were captured by AHL classes, while differences attributed to slope shape and convergence were best captured by MLRA, which better segregated the watersheds based on landscape age and stream dissection. Grassed waterway placements showed minor differences among MLRAs but provided data to better inform the choices that ACPF users can make when running the GWW tool. The MLRA/AHL landscape classifications could be used together to develop effective regional conservation strategies using precision planning tools.

Quantifying the return on investment of social and ecological data for conservation planning

The interdisciplinary nature of conservation problems is increasingly being incorporated into research, raising fundamental questions about the relative importance of the different types of knowledge and data. Although there has been extensive research on the development of methods and tools for conservation planning, especially spatial planning, comparatively little is known about the relative importance of ecological versus non-ecological data for prioritization, or the likely return on investment of incorporating better data. We demonstrate a simple approach for (1) quantifying the sensitivity of spatial planning results to different ecological and non-ecological data layers, and (2) estimating the potential gains in efficiency from incorporating additional data. Our case study involves spatial planning for coastal squeeze, a process by which development blocks coastal ecosystems from moving landward in response to sea-level rise. We show that incorporating spatial data on landowners’ likelihood of selling had little effect on identifying relative priorities but drastically changed the outlook for whether conservation goals could be achieved. Better data on the costs of conservation actions had the greatest potential to improve the efficiency of spatial planning, in some cases generating more than an order of magnitude greater cost savings compared to ecological data. Our framework could be applied to other systems to guide the development of spatial planning and to identify general rules of thumb for the importance of alternative data sources for conservation problems in different socio-ecological contexts.

Spatially explicit models of seasonal habitat for greater sage-grouse at broad spatial scales: Informing areas for management in Nevada and northeastern California.

Defining boundaries of species' habitat across broad spatial scales is often necessary for management decisions, and yet challenging for species that demonstrate differential variation in seasonal habitat use. Spatially explicit indices that incorporate temporal shifts in selection can help overcome such challenges, especially for species of high conservation concern. Greater sage‐grouse Centrocercus urophasianus (hereafter, sage‐grouse), a sagebrush obligate species inhabiting the American West, represents an important case study because sage‐grouse exhibit seasonal habitat patterns, populations are declining in most portions of their range and are central to contemporary national land use policies. Here, we modeled spatiotemporal selection patterns for telemetered sage‐grouse across multiple study sites (1,084 sage‐grouse; 30,690 locations) in the Great Basin. We developed broad‐scale spatially explicit habitat indices that elucidated space use patterns (spring, summer/fall, and winter) and accounted for regional climatic variation using previously published hydrographic boundaries. We then evaluated differences in selection/avoidance of each habitat characteristic between seasons and hydrographic regions. Most notably, sage‐grouse consistently selected areas dominated by sagebrush with few or no conifers but varied in type of sagebrush selected by season and region. Spatiotemporal variation was most apparent based on availability of water resources and herbaceous cover, where sage‐grouse strongly selected upland natural springs in xeric regions but selected larger wet meadows in mesic regions. Additionally, during the breeding period in spring, herbaceous cover was selected strongly in the mesic regions. Lastly, we expanded upon an existing joint–index framework by combining seasonal habitat indices with a probabilistic index of sage‐grouse abundance and space use to produce habitat maps useful for sage‐grouse management. These products can serve as conservation planning tools that help predict expected benefits of restoration activities, while highlighting areas most critical to sustaining sage‐grouse populations. Our joint–index framework can be applied to other species that exhibit seasonal shifts in habitat requirements to help better guide conservation actions.

Landscape connectivity modeling from the perspective of animal dispersal

Dispersal plays a key role in linking populations, habitat (re)-colonization, and species range expansion. As fragmentation and habitat loss are ubiquitous threats and can disrupt dispersal, landscape connectivity modeling has become a valuable tool in conservation planning. We provide an overview of how current connectivity modeling has incorporated the different aspects of animal dispersal. We describe the most popular connectivity models and highlight their main assumptions related to dispersal, suggesting a series of questions that could clarify the advantages and disadvantages of using a particular approach. We review the structure of the connectivity models based on least-cost analysis, circuit theory, and the individual-based dispersal models. We use some studies as case examples to discuss how important elements of animal dispersal were considered through models to predict movement routes. Ongoing developments in connectivity modeling have made it possible to represent animal dispersal in a more realistic way by implementing key elements such as dispersal behaviors, mortality, and inter-individual variability. However, the potential to consider such elements and how this is done in connectivity modeling depends on the selected approach, since each model represents animal dispersal through a different perspective. We recommend that the choice of a connectivity model should be made after considering the study objectives, the species dispersal mechanism, and the prior knowledge available about it. By understanding and incorporating dispersal behavior into connectivity modeling, we can improve our capacity to generate useful information aimed to construct more effective conservation strategies.

HBIM (heritage Building Information Modell) of the Wang Stave Church in Karpacz – Case Study

ABSTRACT In recent years, Building Information Model (BIM) has become a leading topic for construction-related fields. This technology may be viewed as a process, a type of database, a software or even a 3D model, but in fact, it integrates all these roles and many more. Heritage (Historic) Building Information Model (HBIM) is a standard for heritage objects. It not only allows for the storing of spatial information and metadata but also provides the means of documenting changes that such structures undergo. The scope of application varies from simple documentation repository, through conservation planning tools, to construction and renovation simulation instruments. In this paper, we focus on the example of the so-called Wang Temple in Karpacz, Poland. This object was chosen due to its unique history. This project was done in cooperation with BIMPoint Company (Kraków). It was aimed at showing how effective HBIM can be in accurate spatial documentation of small-scale heritage sites.

Past and future climatic indicators for distribution patterns and conservation planning of temperate coniferous forests in southwestern China

Abstract The distribution of forests can be hindcast or forecast when robust data and validation proofs are available. Distribution consequences for temperate coniferous forests during the Quaternary and future climatic fluctuations are little understood in China. Temperate coniferous forests are subdivided into two types where Pinus, Keteleeria, Tsuga and Cupressus species characterize the warm forests while Abies, Picea, Larix and Juniperus species characterize the cold forests. Both forest types were investigated using 12,675 unique records in the MaxEnt model to infer distribution patterns during six time periods: the present time, mid-Holocene, Last Glacial Maximum, Last Interglacial and the near- and far-future (2050, 2070). The results showed that the mean temperature of the driest quarter, followed by the temperature annual range and the precipitation of the warmest quarter were the most important variables controlling forests distribution. Suitable areas capable of supporting the presence of warm coniferous forests were highly contracted during the Quaternary; meanwhile, the cold coniferous forests expanded westwards from the last glacial maximum through the mid-Holocene to the present. The predicted future distributions showed a significant range-shift of warm forests northeastwards in response to climate change while cold coniferous forests are expected to migrate towards the Tibetan Plateau. Under increased global warming, the bioclimatic variables of the past and future periods were used as ecological indicators for the identification of areas showing “climatic stability”, and predicted to be refugia for coniferous forests and as good tools for conservation planning in Sichuan and the Hengduan-Tibetan migration corridor.

Combining multi-state species distribution models, mortality estimates, and landscape connectivity to model potential species distribution for endangered species in human dominated landscapes

Species ranges are changing in response to human-related disturbances and often management and conservation decisions must be based on incomplete information. In this context, species distribution models (SDMs) represent the most widely used tool, but they often lack any reference to demographic performance of the population under study, spatial structure of the habitat patches, or connectivity at the landscape level.Combining a multi-state SDM with a landscape pattern analysis and a mortality model, we developed a spatially-explicit, integrated model to assist and inform conservation planning for the Apennine brown bear in central Italy. We identified 15 critical habitat areas, potentially hosting 76 adult female bears. Many of these areas are, however, characterized by high levels of human-related mortality, making them attractive sink-like areas. Structural connectivity was higher in the northern part of the study area while only limited connectivity characterizes the core area, where most of the bears currently live.Our integrated model indicates that the conservation of this relict and isolated bear population is a realistic conservation goal, as we estimated that 192–270 bears could live across the Apennines. Our modelling framework enhances the biological realism of traditional SDMs and provides a conservation planning tool that integrates habitat suitability, mortality risk (as a component of the total demographic performance) and structural connectivity among habitat patches at the landscape scale. It is particularly suited for endangered species living in a human-modified landscapes where establishing a realistic and spatially explicit conservation goal would facilitate pro-active management.

Scale effects on the performance of niche-based models of freshwater fish distributions

Niche-based species distribution models (SDMs) have become an essential tool in conservation and restoration planning. Given the current threats to freshwater biodiversity, it is of fundamental importance to address scale effects on the performance of niche-based SDMs of freshwater species’ distributions. The scale effects are addressed here in the context of hierarchical catchment ordering, considered as counterpart to coarsening grain-size by increasing grid-cell size. We combine fish occurrence data from the Danube River Basin, the hierarchical catchment ordering and multiple environmental factors representing topographic, climatic and anthropogenic effects to model fish occurrence probability across multiple scales. We focus on 1st to 5th order catchments. The spatial scale (hierarchical catchment order) only marginally influences the mean performance of SDMs, however the uncertainty of the estimates increases with scale. Key predictors and their relative importance are scale and species dependent. Our findings have useful implications for choosing proper species dependent spatial scales for river rehabilitation measures, and for conservation planning in areas where fine grain species data are unavailable.

Ecological niche modeling as a cumulative environmental impact assessment tool for biodiversity assessment and conservation planning: A case study of critically endangered plant Lagerstroemia minuticarpa in the Indian Eastern Himalaya

Cumulative environmental impact assessment (CEIA) at river basin level for hydroelectric projects is an evolving concept and has proved to be a useful tool to assess the cumulative impact of developmental projects on the natural ecosystems. However, the generality of CEIA studies is often contested because of methodological limitations, especially in the domain of biodiversity conservation and conservation planning. Ecological niche modeling (ENM) can be a useful tool in CEIA studies for conservation planning of threatened plants in hydroelectric project (HEP) areas. We elucidate this hypothesis taking the example of Lagerstroemia minuticarpa Debberm. ex P.C. Kanjilal, a critically endangered tree species in the Indian Eastern Himalaya. Standard ecological methods were employed to document occurrence records, estimate population size, and characterize habitats. ENM was used to estimate the species potential environmental niche and distribution areas. The possible impacts of HEPs on the potential habitats were predicted by overlaying the HEPs on the potential area map as well as using the conceptual network diagram. The study revealed that the species occupies an environmental niche characterized by humid to per-humid conditions, and is distributed mostly in the Lohit and Teesta basins. Potential areas of the species with high environmental suitability coincide with 19 HEPs, which point to a potential threat to the survival of the species. Network diagram indicated that project activities might deteriorate the habitats thereby affecting the population and regeneration of the species. Our study provides a framework for developing appropriate measures for species conservation and reintroduction at basin level using ENM.

A checklist of Israeli land vertebrates

Faunal lists are important tools in ecology, biogeography, and conservation planning. Such lists can identify gaps in our knowledge of the distribution and taxonomy of regional faunas, and highlight issues needing further study. We present an up to date list of all land vertebrates occurring in Israel. We identify 786 species, of which 551 are birds, 130 are mammals, 97 are reptiles and eight are amphibians. Of these 369 species breed in Israel (including reintroductions), 199 (mostly birds) are regular visitors and 182 are accidental. Fourteen other species are invasive, and 22 species are extinct. We identify issues with the taxonomy and status of several species, and note recent developments in our understanding the Israeli land vertebrate fauna.

Balancing agricultural production, groundwater management, and biodiversity goals: A multi-benefit optimization model of agriculture in Kern County, California

As surface water becomes scarcer and precipitation more variable in a warmer climate, reliance on groundwater reserves for agricultural use is expected to increase. Where efforts to achieve sustainable groundwater management require reductions in aggregate extraction, some of these reductions will entail a decline in agricultural production. However, the degree to which water conservation and other environmental priorities conflict with agricultural production depends, in part, on the spatial configuration of land-use decisions. We explore how to spatially configure groundwater recharge projects to achieve groundwater recharge targets and preserve ecologically important habitat at the least cost. We conduct a field-level analysis for all fields in Kern County, California, the largest crop-producing county in California (and the United States). Results demonstrate that fields selected for cost-effective land retirement can achieve groundwater recharge targets and simultaneously contribute to biodiversity goals via endangered species habitat conservation. However, increasing the amount of habitat conserved raises costs significantly. We discuss conservation actions, economic incentives, and policies that can be leveraged to meet regional biodiversity goals while preserving the economic vitality of agricultural communities. More broadly, this work also provides a proof-of-concept for applying the conservation planning tool, Marxan, to promote efficient allocation of land uses in the face of increasing water resource scarcity and biodiversity threats.

Spatially explicit species distribution models: A missed opportunity in conservation planning?

AbstractAimSystematic conservation planning is vital for allocating protected areas given the spatial distribution of conservation features, such as species. Due to incomplete species inventories, species distribution models (SDMs) are often used for predicting species’ habitat suitability and species’ probability of occurrence. Currently, SDMs mostly ignore spatial dependencies in species and predictor data. Here, we provide a comparative evaluation of how accounting for spatial dependencies, that is, autocorrelation, affects the delineation of optimized protected areas.LocationSoutheast Australia, Southeast U.S. Continental Shelf, Danube River Basin.MethodsWe employ Bayesian spatially explicit and non‐spatial SDMs for terrestrial, marine and freshwater species, using realm‐specific planning unit shapes (grid, hexagon and subcatchment, respectively). We then apply the softwaregurobito optimize conservation plans based on species targets derived from spatial and non‐spatial SDMs (10%–50% each to analyse sensitivity), and compare the delineation of the plans.ResultsAcross realms and irrespective of the planning unit shape, spatially explicit SDMs (a) produce on average more accurate predictions in terms of AUC, TSS, sensitivity and specificity, along with a higher species detection probability. All spatial optimizations meet the species conservation targets. Spatial conservation plans that use predictions from spatially explicit SDMs (b) are spatially substantially different compared to those that use non‐spatial SDM predictions, but (c) encompass a similar amount of planning units. The overlap in the selection of planning units is smallest for conservation plans based on the lowest targets and vice versa.Main conclusionsSpecies distribution models are core tools in conservation planning. Not surprisingly, accounting for the spatial characteristics in SDMs has drastic impacts on the delineation of optimized conservation plans. We therefore encourage practitioners to consider spatial dependencies in conservation features to improve the spatial representation of future protected areas.