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Evaluating how management policies affect red wolf mortality and disappearance (1987-2020)

Citation

Santiago-Ávila, Francisco; Agan, Suzanne; Hinton, Joseph (2022), Evaluating how management policies affect red wolf mortality and disappearance (1987-2020), Dryad, Dataset, https://doi.org/10.5061/dryad.8cz8w9gsr

Abstract

We analyzed data acquired from the USFWS Red Wolf Recovery Program (hereafter ‘Recovery Program’) on radio-marked (hereafter collared), monitored red wolves (i.e., within the NC recovery area). The Recovery Program data include the monitoring history for all collared and monitored adult red wolves released to the wild since the beginning of releases in 1987 to March 1, 2020; n=526. The Recovery Program survival dataset contains the following individual covariates employed in our analyses: endpoint (i.e., final wolf fate by cause of death or disappearance [lost-to-follow-up, LTF]), capture date (beginning of monitoring), and date of endpoint (end of monitoring). The dataset also provided data on sex and other individual covariates not used for our study and hence not described. For recovered wolf carcasses, cause of death was estimated by USFWS using standard methods following necropsy and radiography. Agency removal occurred when a red wolf was captured and removed permanently (lethally or not) from the NC population, generally because the wolf was considered a problem animal by USFWS but also some wolves were removed to supplement other wild populations (n=5). The LTF endpoint occurred when a wolf in the wild disappeared from monitoring because the affixed radio-collar stopped functioning due to either mechanical/battery failure or tampering/destruction by external causes including humans.

We reclassified the marked animals’ fates obtained from the Recovery Program survival data into the following mutually-exclusive endpoints, following previous studies: agency removals (lethal or not, by agency personnel; n=40, 7.6%), collision (trauma by vehicle(s); n=68, 12.9%), reported poached (n=150, 28.5%), nonhuman (unrelated to humans; e.g., disease, intraspecific strife, n=66, 12.5%), and unknown (unable to discern cause of death in necropsy; n=82, 15.6%). We include LTF (disappeared individuals; n=117, 22.2%) as one of multiple mutually-exclusive endpoints.

We estimated the time between collaring (capture date) and endpoint in days (t) for each red wolf (n=526) in our dataset. We calculated time collared (t) differently for surviving, dead, removed to captivity and LTF endpoints, following previous studies. For our mortality endpoints, we estimated t for wolves monitored by telemetry until death. For wolves relocated to captivity, we used the date of final removal to captivity by agency action. For LTF wolves, we used the last date of telemetry contact. We censored any wolves who were alive at the end of our study period (March 1, 2020, n=3 wolves).

In our study, we exploit the complementarity of both models: our joint stratified Cox model allowed us to test the hypothesis that our management covariates affected the rate of occurrence (i.e., hazard) of specific endpoints, and endpoint-specific FG models allowed us to test if and how much these same covariates affected the probability and incidence of said endpoints.

Methods

Data was collected by the US Fish and Wildlife Service Red Wolf Recovery Program between 1987-2020, and processed following available statistical code (see study SM).

Usage Notes

Metadata provided only for attributes used (see abstract and study Methods).