loading page

The importance of independence in unmarked spatial capture-recapture analysis
  • Catherine Sun,
  • Cole Burton
Catherine Sun
Zambian Carnivore Programme

Corresponding Author:[email protected]

Author Profile
Cole Burton
The University of British Columbia
Author Profile


Wildlife populations can be unmarked, meaning individuals lack visually distinguishing features for identification; populations may also exhibit non-independent movements, meaning individuals move together. For either unmarked or non-independent individuals, models based on spatial capture-recapture (SCR) approaches estimate abundance, density, and other population parameters critical for monitoring, management, and conservation. However, when individuals are both unmarked and non-independent, few model options exist. One approach has been to apply unmarked models and not address the non-independence despite unquantified impacts of overdispersion on bias, precision, and the ability to make robust ecological inferences. We conducted a simulation study to quantify the impact of non-independence on the performance of spatial count (SC) and spatial partial identity models (SPIM), two SCR-based unmarked modeling approaches, and used the performance of fully marked and independent SCR as a reference. We varied the levels of non-independence (aggregation and cohesion), detection probability, and the number of partial identity covariates used to resolve identities in SPIM estimation. We expected estimates of abundance and sigma (the spatial scale of individual movement) to be increasingly biased and less precise as aggregation and cohesion increased. Results showed that models indeed became less robust to increasing non-independence, especially for abundance, but importantly suggested that only SPIM could be reliably applied under low levels of cohesion when sufficient partial identity covariates are available. SC yielded consistently biased estimates with inflated precision that could not be corrected to nominal levels of coverage. SCR was the most robust across all combinations of aggregation and cohesion, as expected. We therefore advise against the use of SC models for estimating population parameters when individuals are known to be non-independent, caution that SPIM may be used under narrow ecological conditions, and encourage continued investigations into sampling design and methods development for populations of unmarked and non-independent individuals.
03 Jul 2023Submitted to Wildlife Biology
03 Jul 2023Submission Checks Completed
03 Jul 2023Assigned to Editor
03 Jul 2023Review(s) Completed, Editorial Evaluation Pending
05 Jul 2023Reviewer(s) Assigned
08 Aug 2023Editorial Decision: Revise Major
28 Oct 20231st Revision Received
30 Oct 2023Assigned to Editor
30 Oct 2023Submission Checks Completed
30 Oct 2023Review(s) Completed, Editorial Evaluation Pending