{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-mechanistic-statistical-species","title":"A mechanistic-statistical species distribution model to explain and forecast wolf (Canis lupus) colonization in South-Eastern France","arxiv_id":"1912.09676","date":"2020-02-17","proceeding":null,"authors":[],"abstract":"Species distribution models (SDMs) are important statistical tools for\necologists to understand and predict species range. However, standard SDMs do\nnot explicitly incorporate dynamic processes like dispersal. This limitation\nmay lead to bias in inference about species distribution. Here, we adopt the\ntheory of ecological diffusion that has recently been introduced in statistical\necology to incorporate spatio-temporal processes in ecological models. As a\ncase study, we considered the wolf (Canis lupus) that has been recolonizing\nEastern France naturally through dispersal from the Apennines since the early\n90's. Using partial differential equations for modelling species diffusion and\ngrowth in a fragmented landscape, we develop a mechanistic-statistical\nspatio-temporal model accounting for ecological diffusion, logistic growth and\nimperfect species detection. We conduct a simulation study and show the ability\nof our model to i) estimate ecological parameters in various situations with\ncontrasted species detection probability and number of surveyed sites and ii)\nforecast the distribution into the future. We found that the growth rate of the\nwolf population in France was explained by the proportion of forest cover, that\ndiffusion was influenced by human density and that species detectability\nincreased with increasing survey effort. Using the parameters estimated from\nthe 2007-2015 period, we then forecasted wolf distribution in 2016 and found\ngood agreement with the actual detections made that year. Our approach may be\nuseful for managing species that interact with human activities to anticipate\npotential conflicts.","url_abs":"http://arxiv.org/abs/1912.09676v2","url_pdf":"http://arxiv.org/pdf/1912.09676v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"a-mechanistic-statistical-species","repo_url":"https://github.com/oliviergimenez/appendix_mecastat","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}