Integrating animal tracking datasets at a continental scale for mapping Eurasian lynx habitat

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68081766%3A_____%2F23%3A00578436" target="_blank" >RIV/68081766:_____/23:00578436 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/60460709:41320/23:97917 RIV/62156489:43210/23:43924106 RIV/62156489:43410/23:43924106

Výsledek na webu

<a href="https://onlinelibrary.wiley.com/doi/10.1111/ddi.13784" target="_blank" >https://onlinelibrary.wiley.com/doi/10.1111/ddi.13784</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1111/ddi.13784" target="_blank" >10.1111/ddi.13784</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Integrating animal tracking datasets at a continental scale for mapping Eurasian lynx habitat

Popis výsledku v původním jazyce

Aim: The increasing availability of animal tracking datasets collected across many sites provides new opportunities to move beyond local assessments to enable detailed and consistent habitat mapping at biogeographical scales. However, integrating wildlife datasets across large areas and study sites is challenging, as species' varying responses to different environmental contexts must be reconciled. Here, we compare approaches for large-area habitat mapping and assess available habitat for a recolonizing large carnivore, the Eurasian lynx (Lynx lynx).nLocation: Europe.nMethods: We use a continental-scale animal tracking database (450 individuals from 14 study sites) to systematically assess modelling approaches, comparing (1) global strategies that pool all data for training versus building local, site-specific models and combining them, (2) different approaches for incorporating regional variation in habitat selection and (3) different modelling algorithms, testing nonlinear mixed effects models as well as machine-learning algorithms. nResults: Testing models on training sites and simulating model transfers, global and local modelling strategies achieved overall similar predictive performance. Model performance was the highest using flexible machine-learning algorithms and when incorporating variation in habitat selection as a function of environmental variation. Our best-performing model used a weighted combination of local, site-specific habitat models. Our habitat maps identified large areas of suitable, but currently unoccupied lynx habitat, with many of the most suitable unoccupied areas located in regions that could foster connectivity between currently isolated populations.nMain Conclusions: We demonstrate that global and local modelling strategies can achieve robust habitat models at the continental scale and that considering regional variation in habitat selection improves broad-scale habitat mapping. More generally, we highlight the promise of large wildlife tracking databases for large-area habitat mapping. Our maps provide the first high-resolution, yet continental assessment of lynx habitat across Europe, providing a consistent basis for conservation planning for restoring the species within its former range.

Název v anglickém jazyce

Integrating animal tracking datasets at a continental scale for mapping Eurasian lynx habitat

Popis výsledku anglicky

Aim: The increasing availability of animal tracking datasets collected across many sites provides new opportunities to move beyond local assessments to enable detailed and consistent habitat mapping at biogeographical scales. However, integrating wildlife datasets across large areas and study sites is challenging, as species' varying responses to different environmental contexts must be reconciled. Here, we compare approaches for large-area habitat mapping and assess available habitat for a recolonizing large carnivore, the Eurasian lynx (Lynx lynx).nLocation: Europe.nMethods: We use a continental-scale animal tracking database (450 individuals from 14 study sites) to systematically assess modelling approaches, comparing (1) global strategies that pool all data for training versus building local, site-specific models and combining them, (2) different approaches for incorporating regional variation in habitat selection and (3) different modelling algorithms, testing nonlinear mixed effects models as well as machine-learning algorithms. nResults: Testing models on training sites and simulating model transfers, global and local modelling strategies achieved overall similar predictive performance. Model performance was the highest using flexible machine-learning algorithms and when incorporating variation in habitat selection as a function of environmental variation. Our best-performing model used a weighted combination of local, site-specific habitat models. Our habitat maps identified large areas of suitable, but currently unoccupied lynx habitat, with many of the most suitable unoccupied areas located in regions that could foster connectivity between currently isolated populations.nMain Conclusions: We demonstrate that global and local modelling strategies can achieve robust habitat models at the continental scale and that considering regional variation in habitat selection improves broad-scale habitat mapping. More generally, we highlight the promise of large wildlife tracking databases for large-area habitat mapping. Our maps provide the first high-resolution, yet continental assessment of lynx habitat across Europe, providing a consistent basis for conservation planning for restoring the species within its former range.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10613 - Zoology

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Diversity and Distributions

ISSN

1366-9516

e-ISSN

1472-4642

Svazek periodika

29

Číslo periodika v rámci svazku

12

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

15

Strana od-do

1546-1560

Kód UT WoS článku

001086586100001

EID výsledku v databázi Scopus

2-s2.0-85174273080