Premium
Occupancy patterns of large mammals in the Far North of Ontario under imperfect detection and spatial autocorrelation
Author(s) -
Poley Lucy G.,
Pond Bruce A.,
Schaefer James A.,
Brown Glen S.,
Ray Justina C.,
Johnson Devin S.
Publication year - 2014
Publication title -
journal of biogeography
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.7
H-Index - 158
eISSN - 1365-2699
pISSN - 0305-0270
DOI - 10.1111/jbi.12200
Subject(s) - occupancy , woodland caribou , geography , spatial analysis , ecology , woodland , habitat , covariate , spatial ecology , sampling (signal processing) , physical geography , statistics , biology , mathematics , remote sensing , computer science , computer vision , filter (signal processing)
Abstract Aim An understanding of the factors that influence species distributions in heterogeneous landscapes is important when making decisions regarding conservation. Moreover, occupancy probabilities based on detection data can reveal important species–habitat relationships. Accounting for the spatial autocorrelation of detection data increases the statistical validity of occupancy models, but is not often considered. Using novel occupancy modelling that explicitly incorporates detectability and spatial autocorrelation, we assessed the influence of habitat on occupancy patterns of woodland caribou ( Rangifer tarandus caribou ), moose ( Alces alces ) and wolves ( Canis lupus ) across a broad biogeographical extent where fire is the dominant agent of disturbance. Location Northern Ontario, Canada. Methods We aerially surveyed 3851 sampling units, each covering 100 km 2 , for woodland caribou, moose and wolves in February–March in 2009, 2010 and 2011, and visited 1663 units more than once to estimate detectability. We used restricted spatial regression to model occupancy probabilities of each species with respect to habitat factors in two ecozones, accounting for both imperfect detection and lack of independence of sampling units. Results Covariates influencing species detection varied among ecozones and species. Caribou occupancy was positively related to bogs and negatively related to disturbed areas, while moose occupancy showed opposite responses to these covariates. Wolf occupancy was related to high prey occupancy. Explicitly accounting for spatial autocorrelation in detection data reduced the chance of type I error in occupancy estimates compared with non‐spatial models. Main conclusions Habitat relationships and occupancy patterns support the hypothesis that caribou remain spatially segregated from moose to reduce predation risk. The broad scale of analysis indicated changes in species–habitat relationships, suggesting that limiting factors vary across biogeographical gradients. The spatial pattern in caribou occupancy allowed us to identify important areas used by caribou across the region, including the ecotone between fire‐driven boreal forests and peatland complexes. The evidence for significant relationships between caribou and land cover, predators and alternate prey underscores the need for careful planning of development and infrastructure in the area.