Development Of Species Distribution Models Research Articles

You Shall Not Pass: The Pacific Oxygen Minimum Zone Creates a Boundary to Shortfin Mako Shark Distribution in the Eastern North Pacific Ocean

ABSTRACTAimShoaling of large oxygen minimum zones (OMZs) that form along eastern margins of the world's oceans can reduce habitat availability for some pelagic fishes. Our aim was to test the hypothesis that habitat compression caused by shoaling of the Pacific OMZ in tropical regions creates a boundary to the southern distribution of shortfin mako sharks (Isurus oxyrinchus) in the Eastern North Pacific Ocean.LocationEastern North Pacific and Western North Atlantic oceans.MethodsWe compared environmental conditions between areas used by satellite‐tagged mako sharks in the Eastern North Pacific, encompassing the world's largest OMZ, to those used in the Western North Atlantic where no OMZ is present. In the Pacific we quantified the effects of temperature and dissolved oxygen (DO) on depth use and tested if sharks spent less time in areas with strong habitat compression over the OMZ than expected by chance.ResultsThe southern distribution of sharks in the Pacific corresponded with the apex of OMZ shoaling in the North Equatorial Current. Sharks in the Atlantic occupied areas with warm surface temperatures (≥ 26°C) more often than the Pacific, and waters with these temperatures in the Atlantic had greater DO at depth. Sharks in the Pacific reduced time near the surface in warm temperatures and consistently avoided depths with low DO and spent less time in areas with strong habitat compression than expected by chance.Main ConclusionsThe combination of warm surface temperatures and shoaling of the OMZ creates a soft boundary to mako shark movements in the Eastern North Pacific Ocean. The expected expansion of OMZs due to climate change could have considerable impact on future distribution of mako sharks and other pelagic fish. As such, development of species distribution models to predict the effects of climate change on pelagic fish distributions should incorporate oxygen availability.

A joint distribution framework to improve presence‐only species distribution models by exploiting opportunistic surveys

AbstractAimThe availability of data related to species occurrences has favoured the development of species distribution models using only observations of presence. These data are intrinsically biased by the sampling effort. Presence‐only (PO) species distribution models (SDM) typically account for this effect by introducing additional data considered to be related with the sampling. This approach, however, does not allow the characterisation of the sampling effort and hinders the interpretation of the model. Here, we propose a Bayesian framework for PO SDMs that can explicitly model the sampling effect.LocationMexico.TaxonPines, flycatchers (family Tyranidae), birds and plants.MethodsThe framework defines a bivariate process separable into ecological and sampling effort processes. PO data are conceived of incomplete observations where some presences have been filtered out. A choosing principle is used to separate out presences, missing data and absences relative to the species of interest and the sampling observations. The framework provides three modelling alternatives to account for a spatial autocorrelation structure: independent latent variables (model I); common latent spatial random effect (model II) and correlated latent spatial random effects (model III). The framework was compared against the Maximum Entropy (MaxEnt) algorithm in two case studies: one for the prediction of pines and another for the prediction of flycatchers.ResultsIn both case studies, at least one of the proposed models achieved higher predictive accuracy than MaxEnt. The model III fit best when the sampling effort was informative, while model II was more suitable in cases with a high proportion of non‐sampled sites.Main ConclusionsOur approach provides a flexible framework for PO SDMs aided by a sampling effort process informed by the accumulated observations of independent and heterogeneous surveys. For the two case studies, the framework provided a model with a higher predictive accuracy than an optimised version of MaxEnt.

The value of remotely sensed vs. field-surveyed habitat structure for predicting bird abundance: a case study in traditional orchards

Understanding environmental effects on the distribution and abundance of species is central to ecology, biogeography and evolutionary biology. This led to the development of species distribution models (SDMs) that relate spatial variation in occurrence and abundance to environmental variables. So far, SDMs rarely considered habitat structure, as a major determinant of bird distributions. While remote sensing increasingly provides high-resolution measures of habitat structure, certain structural variables affecting bird abundance still need to be measured with field surveys. In this study, we compare the value of remotely sensed vs. field-surveyed habitat structure for predicting bird abundance. Specifically, we analysed abundance data for nine bird species of traditional orchards in South-Western Germany. ‘Remote sensing SDMs’ related abundance to structural variables obtained by aerial photogrammetry of individual orchard trees. Alternative ‘field survey SDMs’ related bird abundance to detailed field surveys of the species composition and pruning state of orchard trees. Additionally, both remote sensing and field survey SDMs included climate and land use variables. Accounting for detailed habitat structure improved abundance predictions for seven of nine study species compared to models only incorporating climate and land use. The impact on model performance differed between remotely sensed and field-surveyed variables: the former improved abundance models for most (n = 7) bird species, whereas the latter had more variable impact, decreasing model performance for five species. The remotely sensed variable with strongest effects was overall tree density, which positively affected abundance of seven species. In contrast, multiple field-surveyed variables had similar effect strength, with the overall strongest effect found for pear tree density, to which seven bird species showed a unimodal response. These analyses have conservation implications since they predict expected responses of bird species to ongoing changes in orchard structure. Moreover, they identify structural variables that will be most promising to measure via remote sensing data in the future.

The Relict Ecosystem of Maytenus senegalensis subsp. europaea in an Agricultural Landscape: Past, Present and Future Scenarios

Maytenus senegalensis subsp. europaea is a shrub belonging to the Celastraceae family, whose only European populations are distributed discontinuously along the south-eastern coast of the Iberian Peninsula, forming plant communities with great ecological value, unique in Europe. As it is an endangered species that makes up plant communities with great palaeoecological significance, the development of species distribution models is of major interest under different climatic scenarios, past, present and future, based on the fact that the climate could play a relevant role in the distribution of this species, as well as in the conformation of the communities in which it is integrated. Palaeoecological models were generated for the Maximum Interglacial, Last Maximum Glacial and Middle Holocene periods. The results obtained showed that the widest distribution of this species, and the maximum suitability of its habitat, occurred during the Last Glacial Maximum, when the temperatures of the peninsular southeast were not as contrasting as those of the rest of the European continent and were favored by higher rainfall. Under these conditions, large territories could act as shelters during the glacial period, a hypothesis reflected in the model’s results for this period, which exhibit a further expansion of M. europaea’s ecological niche. The future projection of models in around 2070, for four Representative Concentration Pathways according to the fifth report of the Intergovernmental Panel on Climate Change, showed that the most favorable areas for this species would be Campo de Dalías (southern portion of Almería province) as it presents the bioclimatic characteristics of greater adjustment to M. europaea’s ecological niche model. Currently, some of the largest specimens of the species survive in the agricultural landscapes in the southern Spain. These areas are almost totally destroyed and heavily altered by intensive agriculture greenhouses, also causing a severe fragmentation of the habitat, which implies a prospective extinction scenario in the near future.

BioModelos: A collaborative online system to map species distributions.

Information on species distribution is recognized as a crucial input for biodiversity conservation and management. To that end, considerable resources have been dedicated towards increasing the quantity and availability of species occurrence data, boosting their use in species distribution modeling and online platforms for their dissemination. Currently, those platforms face the challenge of bringing biology into modeling by making informed decisions that result in meaningful models, based on limited occurrence and ecological data. Here we describe BioModelos, a modeling approach supported by an online system and a core team of modelers, whereby a network of experts contributes to the development of species distribution models by assessing the quality of occurrence data, identifying potentially limiting environmental variables, establishing species’ accessible areas and validating modeling predictions qualitatively. Models developed through BioModelos become freely and publicly available once validated by experts, furthering their use in conservation applications. Our approach has been implemented in Colombia since 2013 and it currently consist of a network of nearly 500 experts that collaboratively contribute to enhance the knowledge on the distribution of a growing number of species and it has aided the development of several decision support products such as national risk assessments and biodiversity compensation manuals. BioModelos is an example of operationalization of an essential biodiversity variable at a national level through the implementation of a research infrastructure that enhances the value of open access species data.

Incorporating plant fossil data into species distribution models is not straightforward: Pitfalls and possible solutions

The increasing development of species distribution models (SDMs) using palaeodata has created new prospects to address questions of evolution, ecology and biogeography from wider perspectives. Palaeobotanical data provide information on the past distribution of taxa at a given time and place and its incorporation on modelling has contributed to advancing the SDM field. This has allowed, for example, to calibrate models under past climate conditions or to validate projected models calibrated on current species distributions. However, these data also bear certain shortcomings when used in SDMs that may hinder the resulting ecological outcomes and eventually lead to misleading conclusions. Palaeodata may not be equivalent to present data, but instead frequently exhibit limitations and biases regarding species representation, taxonomy and chronological control, and their inclusion in SDMs should be carefully assessed. The limitations of palaeobotanical data applied to SDM studies are infrequently discussed and often neglected in the modelling literature; thus, we argue for the more careful selection and control of these data. We encourage authors to use palaeobotanical data in their SDMs studies and for doing so, we propose some recommendations to improve the robustness, reliability and significance of palaeo-SDM analyses.

Lowland tapir distribution and habitat loss in South America.

The development of species distribution models (SDMs) can help conservation efforts by generating potential distributions and identifying areas of high environmental suitability for protection. Our study presents a distribution and habitat map for lowland tapir in South America. We also describe the potential habitat suitability of various geographical regions and habitat loss, inside and outside of protected areas network. Two different SDM approaches, MAXENT and ENFA, produced relative different Habitat Suitability Maps for the lowland tapir. While MAXENT was efficient at identifying areas as suitable or unsuitable, it was less efficient (when compared to the results by ENFA) at identifying the gradient of habitat suitability. MAXENT is a more multifaceted technique that establishes more complex relationships between dependent and independent variables. Our results demonstrate that for at least one species, the lowland tapir, the use of a simple consensual approach (average of ENFA and MAXENT models outputs) better reflected its current distribution patterns. The Brazilian ecoregions have the highest habitat loss for the tapir. Cerrado and Atlantic Forest account for nearly half (48.19%) of the total area lost. The Amazon region contains the largest area under protection, and the most extensive remaining habitat for the tapir, but also showed high levels of habitat loss outside protected areas, which increases the importance of support for proper management.

The Relative Impact of Climate Change on the Extinction Risk of Tree Species in the Montane Tropical Andes.

There are widespread concerns that anthropogenic climate change will become a major cause of global biodiversity loss. However, the potential impact of climate change on the extinction risk of species remains poorly understood, particularly in comparison to other current threats. The objective of this research was to examine the relative impact of climate change on extinction risk of upper montane tree species in the tropical Andes, an area of high biodiversity value that is particularly vulnerable to climate change impacts. The extinction risk of 129 tree species endemic to the region was evaluated according to the IUCN Red List criteria, both with and without the potential impacts of climate change. Evaluations were supported by development of species distribution models, using three methods (generalized additive models, recursive partitioning, and support vector machines), all of which produced similarly high AUC values when averaged across all species evaluated (0.82, 0.86, and 0.88, respectively). Inclusion of climate change increased the risk of extinction of 18–20% of the tree species evaluated, depending on the climate scenario. The relative impact of climate change was further illustrated by calculating the Red List Index, an indicator that shows changes in the overall extinction risk of sets of species over time. A 15% decline in the Red List Index was obtained when climate change was included in this evaluation. While these results suggest that climate change represents a significant threat to tree species in the tropical Andes, they contradict previous suggestions that climate change will become the most important cause of biodiversity loss in coming decades. Conservation strategies should therefore focus on addressing the multiple threatening processes currently affecting biodiversity, rather than focusing primarily on potential climate change impacts.

What are the roles of species distribution models in conservation planning?

The development of species distribution models (SDMs) has benefited biodiversity conservation through their linkage of science to policy and decision processes. These models have evolved to provide scenarios of future landscapes based on known and projected environmental parameters. Whereas there are many caveats to their use, the persuasive power of the models for conveying the consequences of environmental change to the non-science community is immense. Scientists are obliged to convey the uncertainty of the futures depicted in their models, but also to involve the stakeholders who will shape those future conditions. Stakeholders can identify the natural resources they want to sustain, voice their priorities in environmental policy, and articulate the range of solutions they are willing to accept. The creation of alternative futures is an academic exercise if not linked to real viable decisions concerning important resources. SDMs only reach their full potential when they bring together scientists, public stakeholders and policy makers, and are used as an adaptive management tool to understand complex landscapes that are undergoing short- and long-term change.

物种分布模型理论研究进展

PDF HTML阅读 XML下载 导出引用 引用提醒 物种分布模型理论研究进展 DOI: 10.5846/stxb201212031735 作者: 作者单位: 西北农林科技大学黄土高原土壤侵蚀与旱地农业国家重点实验室;中国科学院植物研究所植被与环境变化国家重点实验室;中国科学院水利部水土保持研究所,中国科学院植物研究所植被与环境变化国家重点实验室,中国科学院植物研究所植被与环境变化国家重点实验室;中国林业科学研究院荒漠化研究所,中国科学院植物研究所植被与环境变化国家重点实验室;北京电子科技职业学院生物工程学院,中国科学院植物研究所植被与环境变化国家重点实验室,中国科学院植物研究所植被与环境变化国家重点实验室 作者简介: 通讯作者: 中图分类号: 基金项目: 环境保护部环保公益性行业科研专项(201209028); 国家自然科学基金(40741003); 西北农林科技大学12年博士科研启动基金(Z111021309) Advances in theoretical issues of species distribution models Author: Affiliation: State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Northwest A,,,,, Fund Project: 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:利用物种分布模型估计物种的真实和潜在分布区,已成为区域生态学与生物地理学中非常活跃的研究领域。然而,到目前为止,这项技术的理论基础仍然存在不足之处,一些关键的生态过程未能被有效纳入到物种分布模型的理论框架中,从而为解释物种分布模型预测的结果带来了诸多困惑。鉴于此, 总结了物种分布模型的理论基础;系统探讨了物种分布模型与物种分布区的关系;特别指出了物种分布模型研究中存在的理论问题;重点阐述了物种分布模型未来的发展方向。研究认为,物种分布模型与生态位理论、源-库理论、种群动态理论、集合种群理论、进化理论等具有重要的联系;正确理解物种分布模型的预测结果与物种分布区的关系,有赖于对影响物种分布的3个主要因素(环境条件、物种相互作用与物种迁移能力)做出定量的分离;目前物种分布模型主要存在的问题是未能将物种的相互作用和物种的迁移能力有效纳入到模型的构建过程中;未来物种分布模型的发展应该加强模型背后理论框架的研究,并进一步加强整合物种相互作用过程、种群动态过程、迁移过程和物种进化过程等内容。研究还认为,从更高的理论层次模拟功能群和群落结构将是未来物种分布模型的重要发展方向。 Abstract:With the development of spatial techniques in geographic information systems (GIS), new methods have allowed for robust and detailed preparation of digital models of the earth's surface elevation, interpolation of climate parameters, and remote sensing of surface conditions in terrestrial environments. These methods in turn have led to greatly enhanced species distribution models (also called species niche models) by providing estimates of environmental conditions and predictions of potential and actual species distribution areas across entire landscapes. Species distribution models have become the subject of active field of research in large-scale ecology and biogeography, and have been used to solve many ecological issues in recent decades. Models are used for biodiversity assessment; biological reserve design; habitat management and restoration; population viability analysis; environmental risk assessment; invasive species management; community and ecosystem modeling; and predicting the effects of global environmental change on species and ecosystems. Species distribution models using species occurrence records (presence only or presence/absence data) associated with environmental variables seek to determine the fundamental niche or realized niche of a particular species, and then to project this niche onto the landscape of interest to reflect the potential distribution area of the species. Results could be interpreted as the probability of occurrence of the species, species habitat suitability or species relative richness. However, there is still insufficient knowledge of the theoretical basis of species distribution models, as some of the key ecological processes have not been incorporated into the framework of these models. This generates substantial confusion when the predicted results of species distribution models are explained. For more efficient use and further development of species distribution models, this study provides: 1) a full overview of the history and recent theoretical advances in the field of species distribution models; 2) a systematic discussion of the relationship between species distribution model and species distribution area; 3) a highlight of the critical limitations inherent in species distribution models; and 4) a focus on challenges of species distribution models for future research. Results from this study suggest that the theoretical basis of species distribution models is strongly related to niche theory, source-sink theory, population dynamics theory, metapopulation theory, and evolutionary theory. Proper understanding of the relationship between the predicted and actual species distribution area depends on separation of three factors (environmental condition, species interactions and species migration ability) affecting the distribution area of species. The main problems of current species distribution models are that they fail to efficiently integrate species interaction and species migration ability into the model building process, which creates gaps between the predicted and actual species distribution area under normal circumstances. Future development of species distribution models should focus on strengthening their inherent theoretical framework, and must integrate species interactions process, population dynamics process, migration process and evolutionary process into models. This study also suggests that simulating functional groups and community structure from higher theoretical levels is important for the development of species distribution models. We believe that through the efforts of scientists, future species distribution models can overcome the above-mentioned drawbacks and can dynamically model the multi-species potential distribution area, thus providing a more in-depth theoretical study of community ecology and biogeography. 参考文献 相似文献 引证文献

Sensitivity analysis of CLIMEX parameters in modelling potential distribution of Lantana camara L.

A process-based niche model of L. camara L. (lantana), a highly invasive shrub species, was developed to estimate its potential distribution using CLIMEX. Model development was carried out using its native and invasive distribution and validation was carried out with the extensive Australian distribution. A good fit was observed, with 86.7% of herbarium specimens collected in Australia occurring within the suitable and highly suitable categories. A sensitivity analysis was conducted to identify the model parameters that had the most influence on lantana distribution. The changes in suitability were assessed by mapping the regions where the distribution changed with each parameter alteration. This allowed an assessment of where, within Australia, the modification of each parameter was having the most impact, particularly in terms of the suitable and highly suitable locations. The sensitivity of various parameters was also evaluated by calculating the changes in area within the suitable and highly suitable categories. The limiting low temperature (DV0), limiting high temperature (DV3) and limiting low soil moisture (SM0) showed highest sensitivity to change. The other model parameters were relatively insensitive to change. Highly sensitive parameters require extensive research and data collection to be fitted accurately in species distribution models. The results from this study can inform more cost effective development of species distribution models for lantana. Such models form an integral part of the management of invasive species and the results can be used to streamline data collection requirements for potential distribution modelling.

Integrating local breeding pond, landcover, and climate factors in predicting amphibian distributions

Species distributions are influenced by many processes operating over varying spatial scales. The development of species distribution models (SDMs), also known as ecological niche models, has afforded the opportunity to predict the distributions of diverse taxa across broad geographic areas and identify variables that are potentially important in regulating these distributions. However, the integration of site-specific habitat data with broad scale climate and landcover data has received limited attention in an SDM framework. We investigate whether SDMs developed with breeding pond, landcover, and climate variables can accurately predict the distributions of nine pond-breeding amphibians in eastern Missouri, USA. Additionally we investigate the relative influences of each environmental variable on the distribution predictions for each study species, and whether the most influential variables are shared among multiple taxa. Boosted regression tree (BRT) SDMs were developed for each species with 38 abiotic and biotic environmental variables, including data from the breeding ponds, surrounding landcover, and climate. To test the models, field surveys were performed in 2007 and 2008 at 103 ponds for nine amphibian species. BRT models developed with breeding pond, landcover, and climate data accurately predicted the occurrences of six of nine species across the study area. Furthermore, the presence of each species was best predicted by a unique combination of environmental variables. Results also suggest that landcover and climate factors may be more influential for species near the edge of their geographic ranges, while local breeding pond factors may be more important for species nearer to the center of their ranges.