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Western pond turtles were recently split into two species the southwestern pond turtle and the northwestern pond turtles. Both species have exhibited substantial population declines and range contractions in recent decades. The causes of the declines are varied but include habitat loss, the introduction of exotic plant and wildlife species, and disease.
One of only two native freshwater turtle species in the western United States, western pond turtles are declining in portions of their original range. Declines are mostly due to habitat loss, introduction of non-native species, pollution, and lack of connectivity among populations.
Species status assessments (SSA) are the new framework that the U.S. Fish and Wildlife Service uses to compile, organize, conduct scientific analyses to support species conservation decisions under the Endangered Species Act (ESA). SSAs entail three primary components, an ecological needs description, a current status assessment, and a future status prediction. The ecological needs and current-status assessment often entail reviewing existing literature and some analysis of existing, available data, whereas the future status requires some form of a predictive model. For some species, population projections could be as simple as categorical condition tables, while more complicated models such as stochastic demographic matrix models could be used to simulate alternative future environmental conditions under multiple scenarios.
This research supports SSA development, focusing on predicting future status (i.e., future redundancy and resiliency). The specific methods for accomplishing this goal will depend on the types of data available to the SSA core team; however, in general, the researchers envision a two-step process. First, the team will work with the FWS and species experts to elicit conceptual models of the species population and ecological dynamics, and second, the researchers will use the results of those elicitations to design projection models for the two species and implement scenarios. This approach largely follows our work with SSA core teams on various species, including Eastern Black Rail, Alligator Snapping Turtle, and Sonora Desert Tortoise. The researchers will work the SSA core team to design scenarios to simulate varied possible future conditions allowing us to incorporate uncertainty in future system states along with uncertainty and variation in population dynamics.
Project Objectives:
Develop a predictive population model for each species of pond turtle to assess the future status of the species with respect to resiliency and redundancy.
Incorporate multiple scenarios into the simulation model to assess the probable effects of continuing threats and emerging threats (e.g., climate change, etc.) on future status
Methods:
Objective 1: Population projection model and estimating extinction risk
Researchers will conduct expert model elicitation workshops to elicit from species experts conceptual models of species’ life history and population dynamics and elicit conceptualizations of the species’ ecological relationships. These will require in-person meetings to discuss species biology, ecology, threats, and conservation initiatives.
Researchers will convert the expert elicited models into stochastic projection models for each species. The structure of the models will depend on available data and existing parameter estimates in published (or unpublished reports.
Objective 2: Forecasting multiple scenarios
Researchers will work with expert teams to identify key threats and conservation actions for each species of pond turtle. The simulation model will incorporate functions to mimic environmental threats or conservation actions. For example, G. polyphemus requires fire-maintained habitat, but forest management with prescribed fire may be limited under climate change scenarios. To model this effect, we will incorporate a habitat-loss function into simulations that implement an annual reduction in the carrying capacity of local populations. In this function, we are not directly removing habitat but instead mimicking the effects of habitat degradation on the population size (carrying capacity) of local populations.
The team will employ a master's student at the University of Florida to conduct the technical work (i.e., code the stochastic model, implement the scenarios). The student will learn about the process for SSA development and how to develop decision-focused population models.