A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Identifying territories using presence-only citizen science data : An application to the Finnish wolf population (2022)


Karppinen, S., Rajala, T., Mäntyniemi, S., Kojola, I., & Vihola, M. (2022). Identifying territories using presence-only citizen science data : An application to the Finnish wolf population. Ecological Modelling, 472, Article 110101. https://doi.org/10.1016/j.ecolmodel.2022.110101


JYU-tekijät tai -toimittajat


Julkaisun tiedot

Julkaisun kaikki tekijät tai toimittajat: Karppinen, Santeri; Rajala, Tuomas; Mäntyniemi, Samu; Kojola, Ilpo; Vihola, Matti

Lehti tai sarja: Ecological Modelling

ISSN: 0304-3800

eISSN: 1872-7026

Julkaisuvuosi: 2022

Ilmestymispäivä: 29.08.2022

Volyymi: 472

Artikkelinumero: 110101

Kustantaja: Elsevier BV

Julkaisumaa: Alankomaat

Julkaisun kieli: englanti

DOI: https://doi.org/10.1016/j.ecolmodel.2022.110101

Julkaisun avoin saatavuus: Avoimesti saatavilla

Julkaisukanavan avoin saatavuus: Osittain avoin julkaisukanava

Julkaisu on rinnakkaistallennettu (JYX): https://jyx.jyu.fi/handle/123456789/82904


Tiivistelmä

Citizens, community groups and local institutions participate in voluntary biological monitoring of population status and trends by providing species data e.g. for regulations and conservation. Sophisticated statistical methods are required to unlock the potential of such data in the assessment of wildlife populations.

We develop a statistical modelling framework for identifying territories based on presence-only citizen science data. The framework can be used to jointly estimate the number of active animal territories and their locations in time. Our approach is based on a data generating model which consists of a dynamic submodel for the appearance/removal of territories and an observation submodel that accounts for the varying observation intensity and links the data to the territories. We first estimate the observation intensity using past presence-only observations made by citizens, conditioning on previously known territories. We then infer the territories using a state-of-the-art sequential Monte Carlo method, which extends earlier approaches by allowing for spatial inhomogeneity in the observation process.

We verify our data generating model and inference method successfully in synthetic scenarios. We apply our framework for estimating the locations and number of wolf territories in March 2020 in Finland using one year of confirmed citizen-made wolf observations. The observation intensity is estimated using wolf observation data collected in 2011–2019, conditioning on official territory estimates and data from GPS-collared wolves.

Our experiments with synthetic data suggest that the estimation of territories can be feasible with presence-only data. Our location and territory count inferences for March 2020 based on past data are comparable to the official wolf population assessment of March 2020 by the Natural Resources Institute Finland. The results suggest that the framework can provide useful information for assessing populations of territorial animals. Furthermore, our methods and findings, such as the developed data generating model and the estimation of the spatio-temporal observation intensity can be relevant also beyond the strictly territorial setting.


YSO-asiasanat: susi; reviirit; eläinkannat; populaatiot; paikkatietoanalyysi; bayesilainen menetelmä; Monte Carlo -menetelmät; kansalaishavainnot; kansalaistiede

Vapaat asiasanat: citizen science data; Bayesian statistics; sequential Monte Carlo; spatio-temporal model; territory identification; presence-only data


Liittyvät organisaatiot


Hankkeet, joissa julkaisu on tehty


OKM-raportointi: Kyllä

Raportointivuosi: 2022

JUFO-taso: 1


Viimeisin päivitys 2024-22-04 klo 22:29