Developing species distribution models for critically endangered species using participatory data: The European sturgeon marine habitat suitability

Charbonnel, Anaïs; Lambert, Patrick; Lassalle, Géraldine; Quinton, Eric; Guisan, Antoine; Mas, Lise; Paquignon, Guillaume; Lecomte, Marie; Acolas, Marie‐Laure

doi:10.1016/j.ecss.2022.108136

Cited by 10 publications

(2 citation statements)

References 87 publications

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“…Based on the performance of the Ensemble Species Distribution Model, the ensemble model was regarded as suitable in predicting environmental suitability for the anthrax outbreaks in Zimbabwe under different climate scenarios as it had ROC > 0.7, TSS and Kappa > 0.6. The employment of multiple metrics for model validation is a standard practice that ensures the reliability of the model results and helps uncover errors and inconsistencies from different angles [54][55][56][57][58]. For instance, [54] clearly demonstrated how TSS can overcome some of the weaknesses associated with Kappa statistic while preserving the positive aspects of the evaluation technique.…”

Section: Model Validationmentioning

confidence: 99%

Modelling climate change impacts on the spatial distribution of anthrax in Zimbabwe

John,

Shekede,

Gwitira

et al. 2024

BMC Public Health

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Background In Zimbabwe, anthrax is endemic with outbreaks being reported almost annually in livestock, wildlife, and humans over the past 40 years. Accurate modelling of its spatial distribution is key in formulating effective control strategies. In this study, an Ensemble Species Distribution Model was used to model the current and future distribution of anthrax occurrence in Zimbabwe. Methods Bioclimatic variables derived from the Beijing Climate Centre Climate System Model were used to model the disease. Collinearity testing was conducted on the 19 bioclimatic variables and elevation to remove redundancy. Variables that had no collinearity were used for anthrax habitat suitability modelling. Two future climate change scenarios for different Representative Concentration Pathways (RCP), RCP4.5 and RCP8.5 were used. Model evaluation was done using true skill, Kappa statistics and receiver operating characteristics. Results The results showed that under current bioclimatic conditions, eastern and western districts of Zimbabwe were modelled as highly suitable, central districts moderately suitable and southern parts marginally suitable for anthrax occurrence. Future predictions demonstrated that the suitable (8%) and highly suitable (7%) areas for anthrax occurrence would increase under RCP4.5 scenario. In contrast, a respective decrease (11%) and marginal increase (0.6%) of suitable and highly suitable areas for anthrax occurrence were predicted under the RCP8.5 scenario. The percentage contribution of the predictors varied for the different scenarios; Bio6 and Bio18 for the current scenario, Bio2, Bio4 and Bio9 for the RCP4.5 and Bio3 and Bio15 for the RCP8.5 scenarios. Conclusions The study revealed that areas currently suitable for anthrax should be targeted for surveillance and prevention. The predicted future anthrax distribution can be used to guide and prioritise surveillance and control activities and optimise allocation of limited resources. In the marginally to moderately suitable areas, effective disease surveillance systems and awareness need to be put in place for early detection of outbreaks. Targeted vaccinations and other control measures including collaborative ‘One Health’ strategies need to be implemented in the predicted highly suitable areas. In the southern part where a high decrease in suitability was predicted, continued monitoring would be necessary to detect incursions early.

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Section: Model Validationmentioning

confidence: 99%

Modelling climate change impacts on the spatial distribution of anthrax in Zimbabwe

John,

Shekede,

Gwitira

et al. 2024

BMC Public Health

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“…In the field of Ecology, SDMs have been employed in diverse contexts and have provided insights into species distributions patterns, species-environment relationships, and potential habitat suitability [29][30][31][32][33] . To implement SDMs, researchers can choose from a variety of approaches and software tools that facilitate the inference and forecasting processes 5 .…”

Section: Introductionmentioning

confidence: 99%

Machine learning applied to global scale species distribution models (SDMs)

Fuster-Alonso,

Mestre-Tomás,

Baez

et al. 2024

Preprint

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Species Distribution Models (SDMs) have been widely applied in ecology to analyze the historical and future patterns of marine species' distributions. With the increasing impact of climate change in recent decades, understanding potential shifts in species distributions has become a crucial challenge. Research on alterations in spatial and temporal distributions has revealed an increasing focus on developing different statistical approaches for global-scale and long-term forecasts. One promising approach is Bayesian Additive Regression Trees (BART), a non-parametric machine learning tool based on a sum-of-trees model that could be useful for addressing ecological problems. The goal of this study is to apply BART on a global scale and use it to estimate and predict possible present and future habitats of marine species under different climate change scenarios. Here we show an application of BART focused on the functional group of marine turtles, analyzing their historical and future distributions both individually and as a taxonomic group, their relationship with environmental variables, and BART's capacity to predict long-term distributions at global scales. Furthermore, to assess the capabilities of BART, we conduct a simulation study under two distinct scenarios: 1) simulating a hypothetical cosmopolitan species distribution and 2) simulating a hypothetical persistent species distribution. Our results show that BART is a promising approach to predict the potential distribution of our target species, as well as their relationship with key environmental variables, on a global scale.

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Data‐centric species distribution modeling: Impacts of modeler decisions in a case study of invasive European frog‐bit

Hansen,

Monfils,

Hackett

et al. 2024

Appl Plant Sci

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PremiseSpecies distribution models (SDMs) are widely utilized to guide conservation decisions. The complexity of available data and SDM methodologies necessitates considerations of how data are chosen and processed for modeling to enhance model accuracy and support biological interpretations and ecological applications.MethodsWe built SDMs for the invasive aquatic plant European frog‐bit using aggregated and field data that span multiple scales, data sources, and data types. We tested how model results were affected by five modeler decision points: the exclusion of (1) missing and (2) correlated data and the (3) scale (large‐scale aggregated data or systematic field data), (4) source (specimens or observations), and (5) type (presence‐background or presence‐absence) of occurrence data.ResultsDecisions about the exclusion of missing and correlated data, as well as the scale and type of occurrence data, significantly affected metrics of model performance. The source and type of occurrence data led to differences in the importance of specific explanatory variables as drivers of species distribution and predicted probability of suitable habitat.DiscussionOur findings relative to European frog‐bit illustrate how specific data selection and processing decisions can influence the outcomes and interpretation of SDMs. Data‐centric protocols that incorporate data exploration into model building can help ensure models are reproducible and can be accurately interpreted in light of biological questions.

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Developing species distribution models for critically endangered species using participatory data: The European sturgeon marine habitat suitability

Cited by 10 publications

References 87 publications

Modelling climate change impacts on the spatial distribution of anthrax in Zimbabwe

Modelling climate change impacts on the spatial distribution of anthrax in Zimbabwe

Machine learning applied to global scale species distribution models (SDMs)

Data‐centric species distribution modeling: Impacts of modeler decisions in a case study of invasive European frog‐bit

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