Bayesian species distribution models integrate presence-only and presence-absence data to predict deer distribution and relative abundance

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41320%2F23%3A96970" target="_blank" >RIV/60460709:41320/23:96970 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.1111/ecog.06451" target="_blank" >http://dx.doi.org/10.1111/ecog.06451</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1111/ecog.06451" target="_blank" >10.1111/ecog.06451</a>

Result language

angličtina

Original language name

Bayesian species distribution models integrate presence-only and presence-absence data to predict deer distribution and relative abundance

Original language description

Using geospatial data of wildlife presence to predict a species distribution across a geographic area is among the most common tools in management and conservation. The collection of high-quality presence-absence (PA) data through structured surveys is, however, expensive, and managers usually have access to larger amounts of low-quality presence-only (PO) data collected by citizen scientists, opportunistic observations and culling returns for game species. Integrated species distribution models (ISDMs) have been developed to make the most of the data available by combining the higher-quality, but usually scarcer and more spatially restricted, PA data with the lower-quality, unstructured, but usually more extensive PO datasets. Joint-likelihood ISDMs can be run in a Bayesian context using integrated nested laplace approximation methods that allow the addition of a spatially structured random effect to account for data spatial autocorrelation. Here, we apply this innovative approach to fit ISDMs to empirical data, using PA and PO data for the three prevalent deer species in Ireland: red, fallow and sika deer. We collated all deer data available for the past 15 years and fitted models predicting distribution and relative abundance at a 25 km(2) resolution across the island. Model predictions were associated to spatial estimate of uncertainty, allowing us to assess the quality of the model and the effect that data scarcity has on the certainty of predictions. Furthermore, we checked the performance of the three species-specific models using two datasets, independent deer hunting returns and deer densities based on faecal pellet counts. Our work clearly demonstrates the applicability of spatially explicit ISDMs to empirical data in a Bayesian context, providing a blueprint for managers to exploit unexplored and seemingly unusable data that can, when modelled with the proper tools, serve to inform management and conservation policies.

Czech name

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Czech description

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Type

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

CEP classification

—

OECD FORD branch

10619 - Biodiversity conservation

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2023

Confidentiality

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

Name of the periodical

Ecography

ISSN

0906-7590

e-ISSN

0906-7590

Volume of the periodical

2023

Issue of the periodical within the volume

2

Country of publishing house

US - UNITED STATES

Number of pages

14

Pages from-to

1-14

UT code for WoS article

000898388900001

EID of the result in the Scopus database

2-s2.0-85143891534