Habitat Suitability and Distribution Models

Guisan, Antoine; Thuiller, Wilfried; Zimmermann, Niklaus E.

doi:10.1017/9781139028271

Cited by 1,064 publications

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“…A total of 10,000 global pseudoabsence records were randomly generated, and a five-fold crossvalidation approach with 10 repetitions was adopted to evaluate predictive performance of 10 algorithms. Relative contribution of each predictor variable and response curves of important variables were determined (Guisan et al, 2017;Thuiller et al, 2014). Algorithms with TSS over 0.85 and AUC over 0.90 for both species were selected to develop committee averaging ensemble models (Costa, Muelbert, Vieira, & Castello, 2015;Thuiller et al, 2014).…”

Section: Modelling Proceduresmentioning

confidence: 99%

“…There are a number of modelling approaches available for SDM studies including classification, regression, and machine learning methods. To reduce the model-based uncertainty and produce reliable predictions, the ensemble modelling technique has been proposed and frequently used, which combines prediction results of multiple modelling algorithms thus can reduce the uncertainties in model predictions (Araújo & New, 2007;Guisan et al, 2017;Thuiller et al, 2014Thuiller et al, , 2019. Previous studies have revealed that different single modelling algorithms have different predictive ability and can produce largely variable results (Elith & Graham, 2009;Pearson et al 2006;Qiao et al, 2015).…”

Section: Potential Distributions Under Current and Future Climate Smentioning

confidence: 99%

“…Climate change can greatly alter the distribution of suitable habitats to species including invasive species (Faleiro, Nemésio, & Loyola, 2018;Guisan, Thuiller, & Zimmermann, 2017), which may ultimately define the regions that will be susceptible to their negative impacts, including habitat competition, predation, hybridisation, and pathogen transmission (Huxel, 1999;Muhlfeld et al, 2014;Rosewarne et al, 2016;Schrimpf, Schmidt, & Schulz, 2014;Zhang, Yokota, & Strüssmann, 2019). Therefore, it is crucial to understand how future climates could alter the distribution of suitable habitats for invasive alien species, in order to effectively protect native biodiversity and ecosystem functioning.…”

Section: Introductionmentioning

confidence: 99%

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Impacts of climate change on the global potential distribution of two notorious invasive crayfishes

et al. 2019

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1. Invasive alien species and climate change are two of the most serious global environmental threats. In particular, it is of great interest to understand how changing climates could impact the distribution of invaders that pose serious threats to ecosystems and human activities.2. In this study, we developed ensemble species distribution models for predicting the current and future global distribution of the signal crayfish Pacifastacus leniusculus and the red swamp crayfish Procambarus clarkii, two of the most highly problematic invaders of freshwater ecosystems worldwide. We collected occurrence records of the species, from native and alien established ranges worldwide.These records in combination with averaged observations of current climatic conditions were used to calibrate a set of 10 distinct correlative models for estimating the climatic niche of each species. We next projected the estimated niches into the geographical space for the current climate conditions and for the 2050s and 2070s under representative concentration pathway 2.6 and 8.5 scenarios.3. Our species distribution models had high predictive abilities and suggest that annual mean temperature is the main driver of the distribution of both species.Model predictions indicated that the two crayfish species have not fully occupied their suitable climates and will respond differently to future climate scenarios in different geographic regions. Suitable climate for P. leniusculus was predicted to shift poleward and to increase in extent in North America and Europe but decrease in Asia. Regions with suitable climate for P. clarkii are predicted to widen in Europe but contract in North America and Asia.4. This study highlights that invasive species with different thermal preference are likely to respond differently to future climate changes. Our results provide important information for policy makers to design and implement anticipated measures for the prevention and control of these two problematic species. K E Y W O R D Sclimate change, habitat suitability, Pacifastacus leniusculus, Procambarus clarkii, species distribution modelling

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Section: Modelling Proceduresmentioning

confidence: 99%

Section: Potential Distributions Under Current and Future Climate Smentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Impacts of climate change on the global potential distribution of two notorious invasive crayfishes

et al. 2019

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“…These methods have been used since the 1920s (Cook, 1925;Sutherst, 2014), but recent years have seen rapid growth in the number of studies employing SDM in fields including ecology, conservation biology, evolutionary biology and epidemiology (Allen & Lendemer, 2016;Coro, Pagano, & Ellenbroek, 2013;Guisan, Thuiller, & Zimmermann, 2017;Gutierrez-Tapia & Palma, 2016;Lezama-Ochoa et al, 2016;Peterson et al, 2011;Raghavan et al, 2016). These methods have been used since the 1920s (Cook, 1925;Sutherst, 2014), but recent years have seen rapid growth in the number of studies employing SDM in fields including ecology, conservation biology, evolutionary biology and epidemiology (Allen & Lendemer, 2016;Coro, Pagano, & Ellenbroek, 2013;Guisan, Thuiller, & Zimmermann, 2017;Gutierrez-Tapia & Palma, 2016;Lezama-Ochoa et al, 2016;Peterson et al, 2011;Raghavan et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Evaluating presence‐only species distribution models with discrimination accuracy is uninformative for many applications

Warren

Matzke

Iglesias

2019

Journal of Biogeography

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Aim Species distribution models are used across evolution, ecology, conservation and epidemiology to make critical decisions and study biological phenomena, often in cases where experimental approaches are intractable. Choices regarding optimal models, methods and data are typically made based on discrimination accuracy: a model's ability to predict subsets of species occurrence data that were withheld during model construction. However, empirical applications of these models often involve making biological inferences based on continuous estimates of relative habitat suitability as a function of environmental predictor variables. We term the reliability of these biological inferences ‘functional accuracy.’ We explore the link between discrimination accuracy and functional accuracy. Methods Using a simulation approach we investigate whether models that make good predictions of species distributions correctly infer the underlying relationship between environmental predictors and the suitability of habitat. Results We demonstrate that discrimination accuracy is only informative when models are simple and similar in structure to the true niche, or when data partitioning is geographically structured. However, the utility of discrimination accuracy for selecting models with high functional accuracy was low in all cases. Main conclusions These results suggest that many empirical studies and decisions are based on criteria that are unrelated to models’ usefulness for their intended purpose. We argue that empirical modelling studies need to place significantly more emphasis on biological insight into the plausibility of models, and that the current approach of maximizing discrimination accuracy at the expense of other considerations is detrimental to both the empirical and methodological literature in this active field. Finally, we argue that future development of the field must include an increased emphasis on simulation; methodological studies based on ability to predict withheld occurrence data may be largely uninformative about best practices for applications where interpretation of models relies on estimating ecological processes, and will unduly penalize more biologically informative modelling approaches.

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“…Habitat suitability models (HSMs), also known as species distribution models (SDMs) or ecological niche models, are used to model the relationship between geographical occurrences of species and environmental variables (Guisan et al 2013(Guisan et al , 2017. While modelling habitat suitability and species distribution, ecologists must deal with the problem of spatial resolutions, related to species occurrence data (entering the models as a dependent variable) and to environmental data used as predictors (McPherson et al 2006, Guisan et al 2007, Gottschalk et al 2011, Rocchini et al 2011, Moudrý and Šímová 2012, Pradervand et al 2013, Lecours et al 2015.…”

Section: Introductionmentioning

confidence: 99%

Fine scale waterbody data improve prediction of waterbird occurrence despite coarse species data

et al. 2018

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While modelling habitat suitability and species distribution, ecologists must deal with issues related to the spatial resolution of species occurrence and environmental data. Indeed, given that the spatial resolution of species and environmental datasets range from centimeters to hundreds of kilometers, it underlines the importance of choosing the optimal combination of resolutions to achieve the highest possible modelling prediction accuracy. We evaluated how the spatial resolution of land cover/waterbody datasets (meters to 1 km) affect waterbird habitat suitability models based on atlas data (grid cell of 12 × 11 km). We hypothesized that the area, perimeter and number of waterbodies computed from high resolution datasets would explain distributions of waterbirds better because coarse resolution datasets omit small waterbodies affecting species occurrence. Specifically, we investigated which spatial resolution of waterbodies better explain the distribution of seven waterbirds nesting on ponds/lakes with areas ranging from 0.1 ha to hundreds of hectares. Our results show that the area and perimeter of waterbodies derived from high resolution datasets (raster data with 30 m resolution, vector data corresponding with map scale 1:10 000) explain the distribution of the waterbirds better than those calculated using less accurate datasets despite the coarse grain of the species data. Taking into account the spatial extent (global vs regional) of the datasets, we found the Global Inland Waterbody Dataset to be the most suitable for modelling distribution of waterbirds. In general, we recommend using land cover data of a resolution sufficient to capture the smallest patches of the habitat suitable for a given species' presence for both fine and coarse grain habitat suitability and distribution modelling.

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Habitat Suitability and Distribution Models

Cited by 1,064 publications

References 0 publications

Impacts of climate change on the global potential distribution of two notorious invasive crayfishes

Impacts of climate change on the global potential distribution of two notorious invasive crayfishes

Evaluating presence‐only species distribution models with discrimination accuracy is uninformative for many applications

Fine scale waterbody data improve prediction of waterbird occurrence despite coarse species data

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