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

Result code in 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>

Alternative codes found

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

Result on the web

<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>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10613 - Zoology

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2023

Confidentiality

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

Name of the periodical

Diversity and Distributions

ISSN

1366-9516

e-ISSN

1472-4642

Volume of the periodical

29

Issue of the periodical within the volume

12

Country of publishing house

US - UNITED STATES

Number of pages

15

Pages from-to

1546-1560

UT code for WoS article

001086586100001

EID of the result in the Scopus database

2-s2.0-85174273080