Modelling Species Distributions Research Articles

Modelling species distributions limited by geographical barriers: A case study with African and American primates

AbstractAimThe boundaries of species distributions are often shaped by natural barriers, such as mountains and rivers, but species distribution models usually fail to include these constraints. We tested several approaches that include barriers as explanatory variables in species distribution models.LocationAfrica and South America.Time periodCurrent.Major taxa studiedPrimates.MethodsWe modelled the ranges of pairs of species separated by a river, taking into account three explanatory components: the environment (ecosystems, topohydrography, climate and human pressure), the spatial structure shaped by history and population dynamics (using a trend‐surface approach) and rivers as naturals barriers to dispersal (using a binary cis–trans variable that describes both sides of the river). To assess how the addition of a spatial structure and the barrier could improve distribution models, we used a nested approach by comparing models based on: (a) the environment; (b) the environment and the spatial structure; and (c) the environment, the spatial structure and the river. These models were constructed using favourability functions.ResultsThere was a decreased occurrence of high‐favourability values on the opposite side of the rivers in models that included the spatial structure of distributions compared with models based on the environment alone. This decrease was more marked when the description of the spatial structure was made more flexible. However, model performance was significantly improved by the inclusion of cis–trans variables that identified areas on the opposite side as totally unfavourable.Main conclusionsThe performance of distribution models can be improved by the use of approaches that describe barriers. Although adding the location of geographical units in relationship to a river appears to be the most accurate way to define the presence of a barrier, defining this variable may be challenging. A suitable alternative is to analyse the spatial structure of distributions using a flexible approach.

Climatic Drivers of Plant Species Distributions Across Spatial Grains in Southern Africa Tropical Forests

Understanding the importance of climate in determining species distribution and how it might change as a function of spatial grain size is a vital issue for species distribution modelling (SDM), yet it is often not accounted for in models and has not been extensively addressed in under sampled areas in tropical forests. Using extensive field sampled vegetation plots data on species occurrences and current climate conditions we modelled 150 plant species in the Okavango River Basin, to map their current projected suitable climate space at 2km2, 5km2, 10km2, 20km2 and 50km2 pixel resolution. Relationships between the variable importance scores and variable identity and their interaction with predictor spatial grain were investigated using Generalised Linear Models and post-hoc analysis. We found variation in the relative influence of temperature and precipitation variables across the spatial grains. The importance of the determinants of species distribution may change between species but such changes are less determined by the predictor’s spatial grain. Potential evapotranspiration consistently exhibited the greatest influence in determining species and richness distribution across spatial grains. We found that the spatial grain of predictors had no effect on the model predictive power and that varying predictor spatial grains had only negligible effects on the model performance measured by AUC and Kappa statistics. The spatial grains of climatic predictors used showed no effect on species richness pattern either. Our results indicate that in areas with relatively low topographic variation, modelling at coarse spatial grain for conservation purposes can be acceptable. Moreover, we show that in tropical areas that have comparatively homogeneous climatic conditions along large spatial extents the variable importance is not influenced by predictor spatial grain. For projections of contemporary species suitable climate space in relatively flat and topographically homogeneous areas which often have a climatically homogenous landscape, more attention must be given to the identity of the selected predictor variables for modelling species distributions than to their spatial grain size. We suggest that in species distribution modelling for conservation planning, assessment of the input datasets spatial grain should be informed and guided by knowledge of the landscape level topographic conditions, as protocol.

Seasonal climatic niches diverge in migratory birds

Migratory birds occupy different geographical areas during breeding and non‐breeding periods, and thus different factors may determine their range limits depending on each season. One such factor is the spatial climatic component of the niche, which is widely used to model species distributions, yet the temporal component is often neglected and is generally assumed to be constant. We tested the hypothesis that the climatic niche is conserved between breeding and non‐breeding areas in 355 bird species migrating through Eurasian–African flyways. For this, we performed niche overlap analyses and compared niche differences between sister or phylogenetically closely related species, as well as linking the differences to migratory distances. For more than 80% of the species, there was no or very little overlap between their breeding and non‐breeding climatic niches. For most closely related species, the degree of overlap of their breeding climatic niches was larger than the overlap observed within each species, but not for their wintering climatic niches, suggesting a phylogenetic conservation of breeding climatic niches. Finally, there was a clear negative relationship between migratory distances and climatic niche overlap within each species. Our results confirmed that the climatic niche of most Eurasian–African migratory species differs between both breeding and non‐breeding ranges, suggesting distinctive seasonal climatic requirements. Given these results and the geographically uneven effects of climate change, the impact of global change is likely to have different effects in each seasonal range. Hence, both breeding and non‐breeding climatic data need to be considered when using species distribution models.

An evaluation of stringent filtering to improve species distribution models from citizen science data

AbstractAimCitizen science data are increasingly used for modelling species distributions because they offer broad spatiotemporal coverage of local observations. However, such data are often collected without experimental design or set survey methods, raising the risk that bias and noise will compromise modelled predictions. We tested the ability of species distribution models (SDMs) built from these low‐structure citizen science data to match the quality of SDMs from systematically collected data and tested whether stringent data filtering improved predictions.LocationNortheastern USA.MethodsWe evaluated models built from a rapidly growing dataset of avian occurrences reported by birders—eBird—against models built from four independent, systematically collected datasets. We developed SDMs for 96 species using both data sources and compared their predictive abilities. We also tested whether culling eBird data by applying stringent data filters on survey effort or observer expertise improved predictions.ResultsWe found that SDMs built from low‐structure citizen science data matched or exceeded performance of SDMs from systematically collected datasets for 12%–31% of species ( = 22%), depending on the dataset. At least one culling option produced equivalent or better performance for 40%–70% of species ( = 49%). Data culling by restricting survey effort improved predictions more than restricting by observer expertise. The optimal effort restriction differed by dataset, and for three of the datasets was further informed by species traits.Main conclusionsSpecies distribution models developed using low‐structure citizen science data sometimes performed as well as those from systematic data. Culling generally improved models, but results were heterogeneous, prohibiting clear recommendations for how to cull. Our results indicate that the growing availability of citizen science data holds potential for creating high‐quality spatial predictions, but that time should be invested in determining how best to cull datasets and that one‐size‐fits‐all solutions beyond basic outlier filtering may be hard to find.

The MIAmaxent R package: Variable transformation and model selection for species distribution models.

The widely used “Maxent” software for modeling species distributions from presence‐only data (Phillips et al., Ecological Modelling, 190, 2006, 231) tends to produce models with high‐predictive performance but low‐ecological interpretability, and implications of Maxent's statistical approach to variable transformation, model fitting, and model selection remain underappreciated. In particular, Maxent's approach to model selection through lasso regularization has been shown to give less parsimonious distribution models—that is, models which are more complex but not necessarily predictively better—than subset selection. In this paper, we introduce the MIAmaxent R package, which provides a statistical approach to modeling species distributions similar to Maxent's, but with subset selection instead of lasso regularization. The simpler models typically produced by subset selection are ecologically more interpretable, and making distribution models more grounded in ecological theory is a fundamental motivation for using MIAmaxent. To that end, the package executes variable transformation based on expected occurrence–environment relationships and contains tools for exploring data and interrogating models in light of knowledge of the modeled system. Additionally, MIAmaxent implements two different kinds of model fitting: maximum entropy fitting for presence‐only data and logistic regression (GLM) for presence–absence data. Unlike Maxent, MIAmaxent decouples variable transformation, model fitting, and model selection, which facilitates methodological comparisons and gives the modeler greater flexibility when choosing a statistical approach to a given distribution modeling problem.

How to make more from exposure data? An integrated machine learning pipeline to predict pathogen exposure.

Predicting infectious disease dynamics is a central challenge in disease ecology. Models that can assess which individuals are most at risk of being exposed to a pathogen not only provide valuable insights into disease transmission and dynamics but can also guide management interventions. Constructing such models for wild animal populations, however, is particularly challenging; often only serological data are available on a subset of individuals and nonlinear relationships between variables are common. Here we provide a guide to the latest advances in statistical machine learning to construct pathogen-risk models that automatically incorporate complex nonlinear relationships with minimal statistical assumptions from ecological data with missing data. Our approach compares multiple machine learning algorithms in a unified environment to find the model with the best predictive performance and uses game theory to better interpret results. We apply this framework on two major pathogens that infect African lions: canine distemper virus (CDV) and feline parvovirus. Our modelling approach provided enhanced predictive performance compared to more traditional approaches, as well as new insights into disease risks in a wild population. We were able to efficiently capture and visualize strong nonlinear patterns, as well as model complex interactions between variables in shaping exposure risk from CDV and feline parvovirus. For example, we found that lions were more likely to be exposed to CDV at a young age but only in low rainfall years. When combined with our data calibration approach, our framework helped us to answer questions about risk of pathogen exposure that are difficult to address with previous methods. Our framework not only has the potential to aid in predicting disease risk in animal populations, but also can be used to build robust predictive models suitable for other ecological applications such as modelling species distribution or diversity patterns.

Vegetation mapping to support greater sage‐grouse habitat monitoring and management: multi‐ or univariate approach?

AbstractConservation planning for wildlife species requires mapping and assessment of habitat suitability across broad areas, often relying on a diverse suite, or stack, of geospatial data presenting multidimensional controls on a species. Stacks of univariate, independently developed vegetation layers may not represent relationships between each variable that can be characterized by multivariate modeling techniques, leading to inaccurate inferences on the distribution of suitable habitat. In this paper, we examine the role of variable combining in mapping multiple dimensions of greater sage‐grouse (Centrocercus urophasianus, GRSG) habitat as a basis for GRSG conservation in the great basin ecoregion within southeastern Oregon. We compare two modeling approaches: a univariate random forest regression model (RF regression) and a multivariate random forest nearest neighbor (RFNN) imputation model , across an array of variables. These include five GRSG habitat descriptor variables: percent cover of trees, juniper, sagebrush, and GRSG food forbs, and the proportion of grasses that are exotic annuals. We also model species distributions of 51 common species in the sage steppe and combine these predictions to estimate alpha diversity. Our results show that RF regression and RFNN can yield univariate predictions with similar performance, but RF regression predictions tend to contain slightly more bias at broader spatial scales. Stacking univariate predictions from RF regression yields covariance errors that manifest as logical errors (juniper cover > tree cover), biases in estimates of GRSG habitat area, and biases in estimates of alpha diversity. Combining variables from the RFNN model does not introduce covariance errors. We conclude that multivariate modeling approaches are better suited to map multidimensional habitat niches at broader spatial scales, and also better suited to provide information for defining multivariable adaptive management triggers at the population level or above.

How to predict biodiversity in space? An evaluation of modelling approaches in marine ecosystems

AbstractAimBiodiversity prediction becomes increasingly important in the face of global diversity loss, whereas substantial challenges still exist in both conceptual and technical aspects. There exist many predictive models, and an integrative evaluation can help understand their performance in handling the multifacets of biodiversity. This study aims to evaluate the performance of these modelling approaches to predict both α‐ and β‐diversity in diverse ecological contexts.LocationNorth Yellow Sea, China.MethodsThe biodiversity models follow three strategies, “assemble first, predict later”, “predict first, assemble later” and “assemble and predict together”. Hill diversity profile, Fisher's log‐series parameter and the distance decay of similarity are used to measure α‐ and β‐diversity. The evaluation study is conducted based on seasonal bottom trawl surveys from October 2016 to August 2017 in North Yellow Sea, China, allocated to coastal and offshore areas. We evaluate the predictive power of the models using cross‐validation.ResultsFollowing the “assemble first, predict later” approach, macroecological model (MEM) provided the most accurate prediction overall, whereas stacked species distribution model (SSDM) and joint species distribution model (JSDM), following the second and third modelling approaches, tended to overestimate α‐diversity and underestimate β‐diversity. The performances of SSDM and JSDM could be improved by moderately down‐weighting rare species. The relative performances of the three modelling approaches were consistent among seasons and spatial regions.Main conclusionsThe superior performances of MEM in a range of temporal and spatial contexts favour the “assemble first, predict later” approach and imply a tight community assembly in the studied area. The overall predictive powers of varying models suggest that the spatial pattern of marine biodiversity could be fairly well predicted with commonly accessible hydrologic data in a mesoscales. The approach of multi‐model evaluations is applicable to a variety of ecosystems for biodiversity prediction.

Modelling species distributions to predict areas at risk of invasion by the exotic aquatic New Zealand mudsnail Potamopyrgus antipodarum (Gray 1843)

Abstract The use of bioclimatic models to predict areas subject to invasion by exotic species has been important in the development of preventive conservation measures. The native New Zealand mudsnail Potamopyrgus antipodarum is an invasive mollusc, now with a worldwide distribution, that causes economic and ecological problems in invaded areas and has recently been designated as one of the 100 worst invasive species in Europe. In this study we sought to determine the potential distribution of and areas susceptible to invasion by P. antipodarum in South America and worldwide, in present and future scenarios (2070), using MaxEnt models. The models were developed using georeferenced occurrence data and bioclimate and hydroclimate variables to predict scenarios of the geographical distribution of P. antipodarum. Possible routes of invasion into South America were predicted. Present‐day scenario of the worldwide distribution (all known occurrences) of P. antipodarum, based on the bioclimatic and hydroclimatic models, are highly accurate, with area under curve higher than of 0.87. The models indicated that environmentally suitable areas exist in South America and in other regions of the world where the species has not yet been recorded. The predicted susceptibility includes approximately 80 protected areas in the South American continent. Models projected in an optimistic and pessimistic future scenario, based on the worldwide distribution, show increased environmental suitability of regions outside the current species distribution, including Brazil (south‐west), Uruguay (near Mirim Lagoon), Nigeria, Ethiopia (lakes Tana and Chomen), Tanzania, Cameroon (bordering lakes Bamendjing, Bankim, and Mbakaou), Equatorial Guinea, Congo, Rwanda, Burundi (Lake Tanganyika environs), Uganda, Tanzania (Naivasha), and South Africa (surrounding Gordon's Bay). The main vector for dispersal to South America would be ballast water coming from waterways and ports located in the predicted areas. Fish farming is also a possible route, since the mudsnail is resistant to the digestive tract of rainbow trout (Oncorhynchus mykiss). Current records and predicted susceptible areas coincide with port areas and sites where this fish is farmed. Geographical distribution modelling proved to be helpful in identifying environmentally suitable areas for the invasion of P. antipodarum. The model also suggests a future expansion of the species distribution, possibly affected by climate change.

Resolving misaligned spatial data with integrated species distribution models.

Advances in species distribution modeling continue to be driven by a need to predict species responses to environmental change coupled with increasing data availability. Recent work has focused on development of methods that integrate multiple streams of data to model species distributions. Combining sources of information increases spatial coverage and can improve accuracy in estimates of species distributions. However, when fusing multiple streams of data, the temporal and spatial resolutions of data sources may be mismatched. This occurs when data sources have fluctuating geographic coverage, varying spatial scales and resolutions, and differing sources of bias and sparsity. It is well documented in the spatial statistics literature that ignoring the misalignment of different data sources will result in bias in both the point estimates and uncertainty. This will ultimately lead to inaccurate predictions of species distributions. Here, we examine the issue of misaligned data as it relates specifically to integrated species distribution models. We then provide a general solution that builds off work in the statistical literature for the change‐of‐support problem. Specifically, we leverage spatial correlation and repeat observations at multiple scales to make statistically valid predictions at the ecologically relevant scale of inference. An added feature of the approach is that addressing differences in spatial resolution between data sets can allow for the evaluation and calibration of lesser‐quality sources in many instances. Using both simulations and data examples, we highlight the utility of this modeling approach and the consequences of not reconciling misaligned spatial data. We conclude with a brief discussion of the upcoming challenges and obstacles for species distribution modeling via data fusion.

A practical guide for combining data to model species distributions.

Understanding and accurately modeling species distributions lies at the heart of many problems in ecology, evolution, and conservation. Multiple sources of data are increasingly available for modeling species distributions, such as data from citizen science programs, atlases, museums, and planned surveys. Yet reliably combining data sources can be challenging because data sources can vary considerably in their design, gradients covered, and potential sampling biases. We review, synthesize, and illustrate recent developments in combining multiple sources of data for species distribution modeling. We identify five ways in which multiple sources of data are typically combined for modeling species distributions. These approaches vary in their ability to accommodate sampling design, bias, and uncertainty when quantifying environmental relationships in species distribution models. Many of the challenges for combining data are solved through the prudent use of integrated species distribution models: models that simultaneously combine different data sources on species locations to quantify environmental relationships for explaining species distribution. We illustrate these approaches using planned survey data on 24 species of birds coupled with opportunistically collected eBird data in the southeastern United States. This example illustrates some of the benefits of data integration, such as increased precision in environmental relationships, greater predictive accuracy, and accounting for sample bias. Yet it also illustrates challenges of combining data sources with vastly different sampling methodologies and amounts of data. We provide one solution to this challenge through the use of weighted joint likelihoods. Weighted joint likelihoods provide a means to emphasize data sources based on different criteria (e.g., sample size), and we find that weighting improves predictions for all species considered. We conclude by providing practical guidance on combining multiple sources of data for modeling species distributions.

Spatial priorities for agricultural development in the Brazilian Cerrado: may economy and conservation coexist?

The ever-increasing requirement of land for food production causes habitat loss and biodiversity decline. Human activities like agriculture are responsible for increases in global temperature, which may preclude species’ survival if they cannot adapt to new climatic conditions or track suitable ones. Although negative impacts of climate change may act in synergy with agriculture when dispersion routes are blocked by croplands, agriculture is important to local economies. Therefore, the demand for land conversion causes conflict among stakeholders and decision makers. But can we benefit both economy and environment? Here we propose an approach to help find a balance between agriculture expansion and biodiversity conservation. We used suitable areas for agriculture to identify priority places to implement monocultures. We modeled species distributions to avoid sites with high conservation value and used species dispersal ability to minimize the distance between present-day and future suitable areas for species persistence. We used a decision-support tool to find a balance between economic development and species conservation, and we conclude that land use conversion is a threat for species persistence given that negative impacts caused by crops could be exacerbated by climate change. Unguided agriculture expansion into future species distribution areas is possible due to severe decreases in the areas for species to persist in the future. Facing this scenario, applying ecological knowledge to guide agriculture expansion is urgent if we want to spare species future distribution area in the Cerrado.

Integrating experimental and distribution data to predict future species patterns

Predictive species distribution models are mostly based on statistical dependence between environmental and distributional data and therefore may fail to account for physiological limits and biological interactions that are fundamental when modelling species distributions under future climate conditions. Here, we developed a state-of-the-art method integrating biological theory with survey and experimental data in a way that allows us to explicitly model both physical tolerance limits of species and inherent natural variability in regional conditions and thereby improve the reliability of species distribution predictions under future climate conditions. By using a macroalga-herbivore association (Fucus vesiculosus - Idotea balthica) as a case study, we illustrated how salinity reduction and temperature increase under future climate conditions may significantly reduce the occurrence and biomass of these important coastal species. Moreover, we showed that the reduction of herbivore occurrence is linked to reduction of their host macroalgae. Spatial predictive modelling and experimental biology have been traditionally seen as separate fields but stronger interlinkages between these disciplines can improve species distribution projections under climate change. Experiments enable qualitative prior knowledge to be defined and identify cause-effect relationships, and thereby better foresee alterations in ecosystem structure and functioning under future climate conditions that are not necessarily seen in projections based on non-causal statistical relationships alone.

A review of evidence about use and performance of species distribution modelling ensembles like BIOMOD

AbstractAimThe idea of combining predictions from different models into an ensemble has gained considerable popularity in species distribution modelling, partly due to free and comprehensive software such as the R package BIOMOD. However, despite proliferation of ensemble models, we lack oversight of how and where they are used for modelling distributions, and how well they perform. Here, we present such an overview.LocationGlobal.MethodsSince BIOMOD is freely available and widely used by ensemble species distribution modellers, we focused on articles that apply BIOMOD, filtering the initial 852 papers identified in our structured literature search to a relevant final subset of 224 eligible peer‐reviewed journal articles.ResultsBIOMOD‐based ensembles are used across many taxa and locations, with terrestrial plants being the most represented group of species (n = 72) and Europe being the most represented continent (n = 106). These studies often focus on forecasting distributions in the future (n = 109), and commonly use presence‐only species data (n = 139) and climatic environmental predictors (n = 219). An average of six models are used in ensembles, and approximately half of ensembles weight contributions of models by their cross‐validation performance. However, discussion about choices made in the modelling process and unambiguous information on the performance of ensemble models versus individual models are limited. The use of independent data to validate model performance is particularly uncommon.Main conclusionsWe document the breadth of ensemble applications, but could not draw strong quantitative conclusions about the predictive performance of ensemble models, due to lack of unambiguous information reported. Understanding how and where ensembles are best used when modelling species distributions is important for enabling best choices for different applications. To enable this objective to be achieved, we provide recommendations for thorough reporting practices in a BIOMOD‐based ensemble workflow.

Impact of Climate Change on Species Distribution and Carbon Storage of Agroforestry Trees on Isolated East African Mountains

Changes in climate will affect conditions for species growth and distribution, particularly along elevation gradients, where environmental conditions change abruptly. Agroforestry tree (AGT) species on the densely inhabited slopes of Mount Kilimanjaro and Taita Hills will change their elevation distribution, and associated carbon storage. This study assesses the potential impacts of climate change by modelling species distribution using maximum entropy. We focus on important agroforestry tree species (Albiziagummifera, Mangiferaindica and Perseaamericana) and projected climate variables under IPCC-AR5 RCP 4.5 and 8.5 for the mid-century (2055) and late century (2085). Results show differential response: downward migration for M. indica on the slopes of Mount Kilimanjaro is contrasted with Avocado that will shift upslope on the Taita Hills under RCP 8.5. Perseaamericana will lose suitable habitat on Kilimanjaro whereas M. indica will expand habitat suitability. Potential increase in suitable areas for agroforestry species in Taita Hills will occur except for Albizia and Mango which will potentially decrease in suitable areas under RCP 4.5 for period 2055. Shift in minimum elevation range will affect species suitable areas ultimately influencing AGC on the slopes of Mount Kilimanjaro and Taita Hills. The AGC for agroforestry species will decrease on the slopes of Mount Kilimanjaro but AGC for Mango will increase under RCP 8.5 for period 2055 and 2085. In Taita Hills, AGC will remain relatively stable for A. gummifera and P. americana under RCP 8.5 for period 2055 and 2085 but decrease in AGC will occur for M. indica under projected climate change. Climate change will affect AGT species and the amount of carbon stored differently between the sites. Such insight can inform AGT species choice, and conservation and support development by improving carbon sequestration on sites and reliable food production.

东北地区5个物种潜在栖息地变化与优化保护规划

PDF HTML阅读 XML下载 导出引用 引用提醒 东北地区5个物种潜在栖息地变化与优化保护规划 DOI: 10.5846/stxb201804130847 作者: 作者单位: 北京林业大学,北京林业大学,北京林业大学,北京林业大学,北京林业大学,北京林业大学 作者简介: 通讯作者: 中图分类号: 基金项目: 国家科技支撑计划项目（2013BAC09B02） Potential distribution and conservation priority areas of five species in Northeast China Author: Affiliation: Beijing Forestry University,Beijing Forestry University,Beijing Forestry University,Beijing Forestry University,Beijing Forestry University,Beijing Forestry University Fund Project: National Key Technology Research and Development Program of the Ministry of Science and Technology of China 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:气候变化广泛影响着物种多样性及其分布变迁。优化模型模拟结果，获取气候变化影响下的优先保护区域将为制定应对气候变化的物种保护政策或行动提供理论依据，提升保护绩效。选取东北地区五种代表性动物，包括黑熊（Ursus thibetanus）、驼鹿（Alces alces）、水獭（Lutra lutra）、紫貂（Martes zibellina）及黑嘴松鸡（Tetrao parvirostris）；结合最大熵模型（Maxent）模拟在不同RCP情景下未来3个年代（2030s，2050s，2070s）的物种潜在栖息地。根据九个常用气候模式的评价结果，获取东北地区合适的气候模式，了解气候变化对物种潜在栖息地的影响，同时开展物种保护规划，识别保护空缺，为应对气候变化、保持生物多样性提供支持。结果显示，在气候变化背景下物种潜在栖息地面积整体呈现下降趋势，但不同气候模式之间存在差异；评价结果推荐CCSM4、NorESM1-M、HadGEM2-AO及GFDL-CM3气候模式，推荐在东北地区使用以上气候模式进行物种未来潜在分布的研究。5个物种潜在栖息地平均面积变化率分别为-62.16%，-73.93%，-78.46%（2030s，2050s，2070s）。综合5个重点保护物种的保护优先区，大兴安岭的呼中、汗马与额尔古纳国家级自然保护区，延边地区的天佛指山、老爷岭东北虎、珲春东北虎与汪清原麝国家级自然保护区，长白山国家级自然保护区是气候变化下物种保护的热点区域。 Abstract:Global climate change has already altered species distribution and diversity. It is significant to study the priority protection area of species to develop dynamic strategies for biodiversity conservation under climate change scenarios. In the current study, we estimated the potential distribution of five species (Asian black bear (Ursus thibetanus), moose (Alces alces), otter (Lutra lutra), sable (Martes zibellina), and black-billed capercaillie (Tetrao parvirostris)) in Northeast China over time using Maxent. We used nine general circulation models (GCMs), and four representative concentration pathways (RCPs) to derive future climate projections over three time periods (2030, 2050, 2070), and then modeled species distributions using these predicted environmental measurements for each time period. Zonation was combined with the results from Maxent to identify priority areas, which were further used to optimize the current nature reserve systems. According to the evaluation results of nine GCMs, appropriate climate models in Northeast China were obtained. Based on the GAP analysis for these conservation priority areas, proposals for priority conservation plans were made. The results showed that climate change in the study area would cause a considerable decline in the total distribution areas of the five species. However, different projections by GCMs may cause uncertainty of the predicted distributions. By comparing the species potential distributions, these four GCMs (CCSM4, NorESM1-M, HadGEM2-AO, and GFDL-CM3) performed well in Northeast China. The mean percentage of species potential range loss increased, as 62.16% range loss by 2030; -73.93% by 2050; and -78.46% by 2070. Conservation priority areas were mainly distributed in a few national nature reserves in the Changbai, Lesser Khingan, Greater Hinggan, and Wanda mountains. 参考文献 相似文献 引证文献

Landscape Modeling of the Potential Natural Vegetation of Santa Catalina Island, California

The vegetation of Santa Catalina Island has been significantly transformed through a history of introduction of exotic plant species and disturbance by large introduced herbivores. Many of these disturbances have been reduced in recent decades, using measures such as carefully controlling the number of bison and removing cattle, sheep, feral pigs, and goats. The success of subsequent vegetation restoration actions depends on the choice of the right plant community for a location, which may not be obvious for an island with extensive areas dominated by exotic species. Environmental niche modeling is an approach to re-create the spatial distribution of habitat types for such a purpose. Such models, however, often require both presence and absence data to be meaningful, while in this scenario absence is misleading because it may reflect a long history of disturbance. Maximum entropy modeling is a technique to model species distributions with presence-only data that has been shown to produce accurate results. We used this modeling tool to model the environmental niche for distinct vegetation types, conceptualized as potential natural vegetation, on Catalina Island as a means to predict locations where restoration actions would be most successful and to predict potential natural vegetation prior to anthropogenic disturbance. Using an existing vegetation map, we extracted random points from within the polygons defining each native vegetation type. We then modeled the habitat suitability for each habitat using high-resolution environmental data that included elevation, aspect, hillshade, northeastness, slope, solar radiation, and topographic wetness index. The resulting models were combined to produce a map of potential natural vegetation. A 1977 map of potential natural vegetation included 4 vegetation types (woodland, chaparral, scrub, and grassland) to which we compared our results. Our new model of potential natural vegetation has high spatial complexity and high resolution. It also shows naturalistic responses to topography that are consistent with the broad patterns mapped in 1977 while providing fine-scale resolution to inform restoration efforts.

Constraints on raptor distribution at the southwestern boundary of the Palaearctic: implications for conservation

Populations at the far edges of their ranges tend to be scarce, and they are frequently of conservation concern. This paper examines the distribution of three raptors (Circaetus gallicus, Hieraaetus pennatus and Milvus migrans) at the southwestern boundary of their breeding range. We modelled species distribution to obtain habitat suitability indexes that were validated with extensive fieldwork in Morocco and Spain. Our results support a strong effect of habitat suitability and a bottleneck effect of the Strait of Gibraltar on raptor distribution. However, after controlling for these effects, the three species were scarcer in Morocco than in Spain. We did not find differences between the two countries in the number of power lines that are dangerous to raptors or in general impacts of agricultural intensification on bird populations (we assessed more farmland birds in Morocco). However, many more people (i.e., shepherds) were detected in Morocco, whose negative effect on raptors could explain the depletion of their populations. These transboundary differences are used to discuss the fate of these peripheral populations in a context of climate change and rural abandonment. They also highlight the need for strengthening raptor conservation around the Strait of Gibraltar, where a permanent flow of raptors converges throughout the year.

Modelling potential distribution of a pine bark beetle in Mexican temperate forests using forecast data and spatial analysis tools

Accurate and reliable predictions of pest species distributions in forest ecosystems are urgently needed by forest managers to develop management plans and monitor new areas of potential establishment. Presence-only species distribution models are commonly used in these evaluations. The maximum entropy algorithm (MaxEnt) has gained popularity for modelling species distribution. Here, MaxEnt was used to model the spatial distribution of the Mexican pine bark beetle (Dendroctonus mexicanus) in a daily fashion by using forecast data from the Weather Research and Forecasting model. This study aimed to exploit freely available geographic and environmental data and software and thus provide a pathway to overcome the lack of costly data and technical guidance that are a challenge to implementing national monitoring and management strategies in developing countries. Our results showed overall agreement values between 60 and 87%. The results of this research can be used for D. mexicanus monitoring and management and may aid as a model to monitor similar species.

Negative biotic interactions drive predictions of distributions for species from a grassland community.

Understanding the factors that determine species' geographical distributions is important for addressing a wide range of biological questions, including where species will be able to maintain populations following environmental change. New methods for modelling species distributions include the effects of biotic interactions alongside more commonly used abiotic variables such as temperature and precipitation; however, it is not clear which types of interspecific relationship contribute to shaping species distributions and should therefore be prioritized in models. Even if some interactions are known to be influential at local spatial scales, there is no guarantee they will have similar impacts at macroecological scales. Here we apply a novel method based on information theory to determine which types of interspecific relationship drive species distributions. Our results show that negative biotic interactions such as competition have the greatest effect on model predictions for species from a California grassland community. This knowledge will help focus data collection and improve model predictions for identifying at-risk species. Furthermore, our methodological approach is applicable to any kind of species distribution model that can be specified with and without interspecific relationships.