Data from: Equivalence between step selection functions and biased correlated random walks for statistical inference on animal movement

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关键词：

Animal movement biased walk models step selection functions Lagrangian methods maximum likelihood estimation

DOI：

doi:10.5061/dryad.217t3

摘要：

functions (SSF) are commonly used to study movements. Because no studies have contrasted the parameters and the statistical properties of their estimators

Data from: Incorporating animal spatial memory in step selection functions

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关键词：

animal movement Biased Brownian Bridge kernel estimation cognitive maps GPS-tracking habitat selection spatial memory

DOI：

doi:10.5061/dryad.s5812

摘要：

Functions (SSF) to understand how resource selection and spatial memory affect space use of feral hogs (Sus scrofa). We used Biased Random Bridge

Data from: Predicting the continuum between corridors and barriers to animal movements using Step Selection Functions and Randomized Shortest Paths

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关键词：

animal movement corridors Step Selection Function Randomized Shortest Path bottlenecks connectivity gene-flow graph-theory green infrastructures obstacles permeability

DOI：

doi:10.5061/dryad.4v13r

摘要：

selection functions (SSF) to predict friction maps quantifying corridor-barrier continua for tactical steps between consecutive locations. Secondly

Data from: Functional responses in animal movement explain spatial heterogeneity in animal-habitat relationships

负责人：

Mason, Tom

关键词：

SSF antipredator behaviour boreal forest foraging ungulates predator-prey spatial games resource selection taiga

DOI：

doi:10.5061/dryad.5p6kr

摘要：

1. Understanding why heterogeneity exists in animal-habitat spatial relationships is critical for identifying the drivers of animal distributions. Functional responses in habitat selection – whereby animals adjust their habitat selection depending on habitat availability – are useful for describing animal-habitat spatial heterogeneity. However, they could be yielded by different movement tactics, involving contrasting inter-specific interactions. 2. Identifying functional responses in animal movement, rather than in emergent spatial patterns like habitat selection, could disentangle the effects of different movement behaviours on spatial heterogeneity in animal-habitat relationships. This would clarify how functional responses in habitat selection emerge and provide a general tool for understanding the mechanistic drivers of animal distributions. 3. We tested this approach using data from GPS-collared woodland caribou (Rangifer tarandus), a prey species under top-down control. We tested how caribou selected and moved with respect to a key resource (lichen-conifer stands) as a function of the availability of surrounding refuge land-cover (closed-conifer stands), using step selection functions. 4. Caribou selected resource patches more strongly in areas richer in refuge land-cover – a functional response in habitat selection. However, adjustments in multiple movement behaviours could have generated this pattern: stronger directed movement towards resources patches and/or longer residency within resource patches, in areas richer in refuges. Different contributions of these behaviours would produce contrasting forager spatial dynamics. 5. We identified functional responses in both movement behaviours: caribou were more likely to move towards resource patches in areas richer in refuge land-cover, and to remain in these patches during movement steps. This tactic enables caribou to spend longer foraging in safer areas where they could rapidly seek refuge in dense cover when predators are detected. 6. Our study shows that functional responses in movement can expose the context-dependent movement decisions that generate heterogeneity in animal-habitat spatial relationships. We used these functional responses to characterise anti-predator movement tactics employed by a large herbivore, but they could be applied in many different scenarios. The movement rules from functional responses in movement are well-suited to integration in spatial explicit individual-based models for forecasting animal distributions in landscapes undergoing environmental change.