A spatially-explicit, individual-based demogenetic simulation framework for evaluating hybridization dynamics

Simulating the efficacy of wolf-dog hybridization management with individual-based modeling

Introgressive hybridization between wolves and dogs is a conservation concern due to its potentially deleterious long-term evolutionary consequences. European legislation requires that wolf-dog hybridization be mitigated through effective management. We developed an individual-based model (IBM) to simulate the life cycle of gray wolves that incorporates aspects of wolf sociality that affect hybridization rates (e.g., the dissolution of packs after the death of one/both breeders) with the goal of informing decision-making on management of wolf-dog hybridization. We applied our model by projecting hybridization dynamics in a local wolf population under different mate choice and immigration scenarios and contrasted results of removal of admixed individuals with their sterilization and release. In several scenarios, lack of management led to complete admixture, whereas reactive management interventions effectively reduced admixture in wolf populations. Management effectiveness, however, strongly depended on mate choice and number and admixture level of individuals immigrating into the wolf population. The inclusion of anthropogenic mortality affecting parental and admixed individuals (e.g., poaching) increased the probability of pack dissolution and thus increased the probability of interbreeding with dogs or admixed individuals and boosted hybridization and introgression rates in all simulation scenarios. Recognizing the necessity of additional model refinements (appropriate parameterization, thorough sensitivity analyses, and robust model validation) to generate management recommendations applicable in real-world scenarios, we maintain confidence in our model's potential as a valuable conservation tool that can be applied to diverse situations and species facing similar threats.

Using social-ecological models to explore stream connectivity outcomes for stakeholders and Yellowstone cutthroat trout

Despite growing interest in conservation and re-establishment of ecological connectivity, few studies have explored its context-specific social-ecological outcomes. We aimed to explore social and ecological outcomes to changing stream connectivity for both stakeholders and native fish species impacted by habitat fragmentation and nonnative species. We (1) investigated stakeholder perceptions of the drivers and outcomes of stream connectivity, and (2) evaluated the effects of stakeholder-identified connectivity and nonnative species scenarios on Yellowstone cutthroat trout (YCT) populations. Our study was conducted in the Teton River, Idaho, USA. We integrated two modeling approaches, mental modeling and individual-based ecological modeling, to explore social-ecological outcomes for stakeholders and YCT populations. Aggregation of mental models revealed an emergent pattern of increasing complexity as more types of stakeholders were considered, as well as gaps and linkages among different stakeholder knowledge areas. These results highlight the importance of knowledge sharing among stakeholders when making decisions about connectivity. Additionally, the results from the individual-based models suggested that the potential for a large, migratory life history form of YCT, in addition to self-preference mating where they overlap with rainbow trout, had the strongest effects on outcomes for YCT. Exploring social and ecological drivers and outcomes to changing connectivity is useful for anticipating and adapting to unintended outcomes, as well as making decisions for desirable outcomes. The results from this study can contribute to the management dialogue surrounding stream connectivity in the Teton River, as well as to our understanding of connectivity conservation and its outcomes more broadly.

Simulating plasticity as a framework for understanding habitat selection and its role in adaptive capacity and extinction risk through an expansion of CDMetaPOP

Adaptive capacity can present challenges for modelling as it encompasses multiple ecological and evolutionary processes such as natural selection, genetic drift, gene flow and phenotypic plasticity. Spatially explicit, individual-based models provide an outlet for simulating these complex interacting eco-evolutionary processes. We expanded the existing Cost-Distance Meta-POPulation (CDMetaPOP) framework with inducible plasticity modelled as a habitat selection behaviour, using temperature or habitat quality variables, with a genetically based selection threshold conditioned on past individual experience. To demonstrate expected results in the new module, we simulated hypothetical populations and then evaluated model performance in populations of redband trout (Oncorhynchus mykiss gairdneri) across three watersheds where temperatures induce physiological stress in parts of the stream network. We ran simulations using projected warming stream temperature data under four scenarios for alleles that: (1) confer thermal tolerance, (2) bestow plastic habitat selection, (3) give both thermal tolerance and habitat selection preference and (4) do not provide either thermal tolerance or habitat selection. Inclusion of an adaptive allele decreased declines in population sizes, but this impact was greatly reduced in the relatively cool stream networks. As anticipated with the new module, high-temperature patches remained unoccupied by individuals with the allele operating plastically after exposure to warm temperatures. Using complete habitat avoidance above the stressful temperature threshold, habitat selection reduced the overall population size due to the opportunity cost of avoiding areas with increased, but not guaranteed, mortality. Inclusion of plasticity within CDMetaPOP will provide the potential for genetic or plastic traits and 'rescue' to affect eco-evolutionary dynamics for research questions and conservation applications.

Evaluating the evolutionary mechanisms maintaining alternative mating strategies in a simulated bull trout (Salvelinus confluentus) population

The coexistence of distinct alternative mating strategies (AMS) is often explained by mechanisms involving trade-offs between reproductive traits and lifetime fitness; yet their relative importance remains poorly understood. Here, we used an established individual-based, spatially explicit model to simulate bull trout (Salvelinus confluentus) in the Skagit River (Washington, USA) and investigated the influence of female mating preference, sneaker-specific mortality, and variation in age-at-maturity on AMS persistence using global sensitivity analyses and boosted regression trees. We assumed that two genetically fixed AMS coexisted within the population: sneaker males (characterized by younger age-at-maturity, greater AMS-specific mortality, and lower reproductive fitness) and territorial males. After 300 years, variation in relative sneaker success in the system was explained by sneaker males' reproductive fitness (72%) and, to a lesser extent, the length of their reproductive lifespan (21%) and their proportion in the initial population (8%). However, under a wide range of parameter values, our simulated scenarios predicted the extinction of territorial males or their persistence in small, declining populations. Although these results do not resolve the coexistence of AMS in salmonids, they reinforce the importance of mechanisms reducing sneaker's lifetime reproductive success in favoring AMS coexistence within salmonid populations but also limit the prediction that, without any other selective mechanisms at play, strong female preference for mating with territorial males and differences in reproductive lifespan allow the stable coexistence of distinct AMS.

Importance of interindividual interactions in eco-evolutionary population dynamics: The rise of demo-genetic agent-based models

The study of eco-evolutionary dynamics, that is of the intertwinning between ecological and evolutionary processes when they occur at comparable time scales, is of growing interest in the current context of global change. However, many eco-evolutionary studies overlook the role of interindividual interactions, which are hard to predict and yet central to selective values. Here, we aimed at putting forward models that simulate interindividual interactions in an eco-evolutionary framework: the demo-genetic agent-based models (DG-ABMs). Being demo-genetic, DG-ABMs consider the feedback loop between ecological and evolutionary processes. Being agent-based, DG-ABMs follow populations of interacting individuals with sets of traits that vary among the individuals. We argue that the ability of DG-ABMs to take into account the genetic heterogeneity-that affects individual decisions/traits related to local and instantaneous conditions-differentiates them from analytical models, another type of model largely used by evolutionary biologists to investigate eco-evolutionary feedback loops. Based on the review of studies employing DG-ABMs and explicitly or implicitly accounting for competitive, cooperative or reproductive interactions, we illustrate that DG-ABMs are particularly relevant for the exploration of fundamental, yet pressing, questions in evolutionary ecology across various levels of organization. By jointly modelling the effects of management practices and other eco-evolutionary processes on interindividual interactions and population dynamics, DG-ABMs are also effective prospective and decision support tools to evaluate the short- and long-term evolutionary costs and benefits of management strategies and to assess potential trade-offs. Finally, we provide a list of the recent practical advances of the ABM community that should facilitate the development of DG-ABMs.