openModeller: a generic approach to species’ potential distribution modelling (original) (raw)
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Applying various algorithms for species distribution modelling
Integrative Zoology, 2013
Species distribution models have been used extensively in many fields, including climate change biology, landscape ecology and conservation biology. In the past three decades, a number of new models have been proposed, yet researchers still find it difficult to select appropriate models for their data and objectives. In this review, we aim to provide insight into the prevailing species distribution models for newcomers in the field of modeling. We compared 11 popular models, including regression models (the generalized linear model, the generalized additive model, the multivariate adaptive regression splines model and hierarchical modeling), classification models (mixture discriminant analysis, the generalized boosting model, and classification and regression tree analysis) and complex models (artificial neural network, random forest, genetic algorithm for rule set production and maximum entropy approaches). Our objectives are: (i) to clarify the characteristics of the models and suitable situations for their use (in terms of data type and species–environment relationships); and (ii) to provide guidelines for model application, including 3 steps: model selection, model formulation and parameter estimation.
Not as good as they seem: the importance of concepts in species distribution modelling
Diversity and Distributions, 2008
Aim Nowadays, large amounts of species distribution data and software for implementing different species distribution modelling methods are freely available through the internet. As a result, methodological works that analyse the relative performance of modelling techniques, as well as those that study which species characteristics affect their performance, are necessary. We discuss three important topics that must be kept in mind when modelling species distributions, namely (i) the distinction between potential and realized distribution, (ii) the effect of the relative occurrence area of the species on the results of the evaluation of model performance, and (iii) the general inaccuracy of the predictions of the realized distribution provided by species distribution modelling methods.Location Unspecific.Methods Using some recent papers as a basis, we illustrate the three issues mentioned above and discuss the negative implications of neglecting them.Results Considering a potential-realized distribution gradient, different modelling methods may be arranged along this gradient according to their ability to model any concept. Complex techniques may be more suitable to model the realized distribution than simple ones, which may be more appropriate to estimate the potential distribution. Comparisons among techniques must consider this scenario. The relative occurrence area of the species conditions the results of the evaluation scores, implying that models of rare species will unavoidably yield higher discrimination values. Moreover, discrimination values that are usually reported in the literature may imply considerable over or underestimations of the distribution of the species.Main conclusions It is extremely important to establish a solid conceptual and methodological framework on which the emergent field of species distribution modelling can stand and develop.
Scientists and software - surveying the species distribution modelling community
Diversity and Distributions, 2015
Aim Software use is ubiquitous in the species distribution modelling (SDM) domain; nearly every scientist working on SDM either uses or develops specialist SDM software; however, little is formally known about the prevalence or preference of one software over another. We seek to provide, for the first time, a 'snapshot' of SDM users, the methods they use and the questions they answer. Location Global. Methods We conducted a survey of over 300 SDM scientists to capture a snapshot of the community and used an extensive literature search of SDM papers in order to investigate the characteristics of the SDM community and its interactions with software developers in terms of co-authoring research publications. Results Our results show that those members of the community who develop software and who are directly connected with developers are among the most highly connected and published authors in the field. We further show that the two most popular softwares for SDM lie at opposite ends of the 'use-complexity' continuum. Main conclusion Given the importance of SDM research in a changing environment, with its increasing use in the policy domain, it is vital to be aware of what software and methodologies are being implemented. Here, we present a snapshot of the SDM community, the software and the methods being used.
GeoSVM: an efficient and effective tool to predict species' potential distributions
Journal of Plant Ecology, 2008
Here, we also give the results of our evaluation of the performance of GeoSVM. We used data for 30 species of Rhododendron in China as a case study to compare GeoSVM and Genetic Algorithm for Rule-Set Prediction (GARP), one of the most popular models to predict species' potential distributions. We found that GeoSVM is more accurate and efficient than GARP. Furthermore, GeoSVM can handle more environmental information, which significantly improves the prediction accuracy.
sdm: a reproducible and extensible R platform for species distribution modelling
Ecography, 2016
The outputs of SDMs are sensitive to the specific rules used to parameterize them. When models are implemented in different platforms, rules used to fit them may not be comparable. For example, Domain (Carpenter et al. 1993), DesktopGARP (Stockwell and Peters 1999), and Maxent (Phillips et al. 2006) are typically implemented with different off-the-shelf software making cross-model comparisons challenging. Models are also generally implemented following different protocols for pre-processing of data and postprocessing of the results, even when they are implemented within the same computer platform. Given the difficulties in comparing the results of different models, conclusions from model comparison studies are difficult to generalise beyond the specific case studies (Segurado and Araújo 2004, Elith et al. 2006). An integrated framework enabling multiple SDMs to be fitted and compared simultaneously is required to move the field of species distribution modeling forward. Three off-theshelf software including openModeller (de Souza Muñoz et al. 2009), BIOENSEMBLES (Diniz-Filho et al. 2009), and ModeEco (Guo and Liu 2010) have been independently developed to provide such frameworks. They enable several modelling algorithms to be fitted simultaneously and they perform the most common tasks related to species
Five (or so) challenges for species distribution modelling
Journal of Biogeography, 2006
Species distribution modelling is central to both fundamental and applied research in biogeography. Despite widespread use of models, there are still important conceptual ambiguities as well as biotic and algorithmic uncertainties that need to be investigated in order to increase confidence in model results. We identify and discuss five areas of enquiry that are of high importance for species distribution modelling: (1) clarification of the niche concept; (2) improved designs for sampling data for building models; (3) improved parameterization;
Biodiversity Information Science and Standards, 2018
Anthropogenic-induced climate change has already altered the conditions to which species have adapted locally, and consequently, shifts of occurrence areas have been previously reported (Chen et al. 2011). Anticipating the results of climate change is urgent, and using these results efficiently to guide decision-making can help to build strategies to protect species from those changes. Therefore, our objective is to propose the use of climate change impact assessments, obtained through species distribution models (SDMs), to guide decision making. The emphasis will be on data that could help determine the potentially vulnerable species and the priority areas, which could act as climate refuges, as well as wildlife corridors. SDMs are based on species occurrence points, available mainly from biological collections and observations (Franklin 2010). When combined with geospatially explicit layers of abiotic or biotic data (e. g. temperature, precipitation, land use), which defines the ecological requirements of species under study, it can generate species distribution models. These models are projected in the form of maps indicating areas where the species can find the most suitable habitats and, therefore, where one can
New trends in species distribution modelling
Ecography, 2010
Species distribution modelling has its origin in the late 1970s when computing capacity was limited. Early work in the field concentrated mostly on the development of methods to model effectively the shape of a species' response to environmental gradients (Austin 1987, Austin et al. 1990). The methodology and its framework were summarized in reviews 10Á15 yr ago (Franklin 1995, Guisan and Zimmermann 2000), and these syntheses are still widely used as reference landmarks in the current distribution modelling literature. However, ...
Measuring the accuracy of species distribution models: a review
2009
Species distribution models (SDMs) are empirical models relating species occurrence to environmental variables based on statistical or other response surfaces. Species distribution modeling can be used as a tool to solve many theoretical and applied ecological and environmental problems, which include testing biogeographical, ecological and evolutionary hypotheses, assessing species invasion and climate change impact, and supporting conservation planning and reserve selection. The utility of SDM in real world applications requires the knowledge of the model’s accuracy. The accuracy of a model includes two aspects: discrimination capacity and reliability. The former is the power of the model to differentiate presences from absences; and the latter refers to the capability of the predicted probabilities to reflect the observed proportion of sites occupied by the subject species. Similar methodology has been used for model accuracy assessment in different fields, including medical diag...