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Abstract Accurate estimates of survival are crucial for many management decisions in translocation programs. Maximizing detection probabilities and reducing sampling biases for released animals can aid in estimates of survival. One important source of sampling bias is an animal’s behavior. For example, individuals that are consistently more exploratory or active may be more likely to be detected visually. Behavioral traits can be related to survival after reintroduction, and because many pre‐release treatments aim to manipulate animal behavior, it is critical to tease apart relationships between behavior and detection probability. Here, we assessed the repeatability (intra‐individual consistency and inter‐individual variation) of behavioral traits for an endangered amphibian, the mountain yellow‐legged frog (Rana muscosa). Because new technological tools offer one potential solution for reducing sampling biases while increasing detection, we also tested whether a long‐range passive integrated transponder (PIT) tag reader could enhance surveys for these individuals after translocation into the wild. After confirming thatex situbredR. muscosaexhibit repeatable behavioral traits (repeatability = 0.25–0.41) and releasing these frogs (N = 196) into the wild, we conducted post‐release surveys visually and with the long‐range PIT tag reader. Integrating the long‐range reader into surveys improved detection probability four‐fold in comparison to visual surveys alone (~0.09 to ~0.36). Moreover, mark–recapture modeling revealed that tag reader detection probability was not biased toward detecting individuals of specific behavioral types, while visual detection was significantly related to behavioral traits. These results will enable a more accurate understanding of individual differences in post‐release success in translocations. This may be particularly important for amphibian species, which can be difficult to detect and are expected to increasingly be involved in human‐managed breeding and translocation programs due to their vulnerable conservation status.
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Using Decision Analysis to Determine the Feasibility of a Conservation Translocation
Conservation translocations, intentional movements of species to protect against extinction, have become widespread in recent decades and are projected to increase further as biodiversity loss continues worldwide. The literature abounds with analyses to inform translocations and assess whether they are successful, but the fundamental question of whether they should be initiated at all is rarely addressed formally. We used decision analysis to assess northern leopard frog reintroduction in northern Idaho, with success defined as a population that persists for at least 50 years. The Idaho Department of Fish and Game was the decision maker (i.e., the agency that will use this assessment to inform their decisions). Stakeholders from government, indigenous groups, academia, land management agencies, and conservation organizations also participated. We built an age-structured population model to predict how management alternatives would affect probability of success. In the model, we explicitly represented epistemic uncertainty around a success criterion (probability of persistence) characterized by aleatory uncertainty. For the leading alternative, the mean probability of persistence was 40%. The distribution of the modelling results was bimodal, with most parameter combinations resulting in either very low (<5%) or relatively high (>95%) probabilities of success. Along with other considerations, including cost, the Idaho Department of Fish and Game will use this assessment to inform a decision regarding reintroduction of northern leopard frogs. Conservation translocations may benefit greatly from more widespread use of decision analysis to counter the complexity and uncertainty inherent in these decisions. History: This paper has been accepted for the Decision Analysis Special Issue on Further Environmental Sustainability. Funding: This work was supported by the Wilder Institute/Calgary Zoo, the U.S. Fish and Wildlife Service [Grant F18AS00095], the NSF Idaho EPSCoR Program and the National Science Foundation [Grant OIA-1757324], and the Hunt Family Foundation. Supplemental Material: The online appendix is available at https://doi.org/10.1287/deca.2023.0472 .
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- Award ID(s):
- 1757324
- PAR ID:
- 10426267
- Date Published:
- Journal Name:
- Decision Analysis
- ISSN:
- 1545-8490
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1287/deca.2023.0472
