Integrating Ecophysiological and Agent-Based Models to Simulate How Behavior Moderates Salamander Sensitivity to Climate

Developing rigorous ecological models is a fundamental goal of conservation biologists seeking to forecast biotic responses to climate change. A limitation of many models is they are amechanistic and lack integration of behavior, which is fundamental to animal biology. We integrated biophysical and agent-based models (ABM) to examine how behavior could affect the sensitivity of Plethodontid salamander activity time to climate. Specifically, our model used a temperature differential to stimulate plant climbing, a widely observed behavior among salamanders, which would allow salamanders to reduce body temperatures and associated dehydration rates. Consistent with expectations, predicted activity time was positively correlated with precipitation. The model showed that climbing plants increased activity time in drier conditions, particularly for smaller salamanders. The predicted importance of climbing behavior, a form of behavioral plasticity, was highly sensitive to assumptions about the threshold of water loss an individual was willing to tolerate. Because activity time is associated with fitness, increased activity time as a consequence of climbing behavior could moderate salamander sensitivity to shifts in weather patterns. Our results demonstrate the potential and importance of integrating behaviors into ecophysiological models when evaluating a species' potential sensitivity to climate

Charting a new frontier of science by integrating mathematical modeling to understand and predict complex biological systems

Biological systems are staggeringly complex. To untangle this complexity and make predictions about biological systems is a continuous goal of biological research. One approach to achieve these goals is to emphasize the use of quantitative measures of biological processes. Advances in quantitative biology data collection and analysis across scales (molecular, cellular, organismal, ecological) has transformed how we understand, categorize, and predict complex biological systems. Simultaneously, thanks to increased computational power, mathematicians, engineers and physical scientists -- collectively termed theoreticians -- have developed sophisticated models of biological systems at different scales. But there is still a disconnect between the two fields. This surge of quantitative data creates an opportunity to apply, develop, and evaluate mathematical models of biological systems and explore novel methods of analysis. The novel modeling schemes can also offer deeper understanding of principles in biology. In the context of this paper, we use “models” to refer to mathematical representations of biological systems. This data revolution puts scientists in a unique position to leverage information-rich datasets to improve descriptive modeling. Moreover, advances in technology allow inclusion of heterogeneity and variability within these datasets and mathematical models. This inclusion may lead to identifying previously undetermined variables driving or maintaining heterogeneity and diversity. Improved inclusion of variation may even improve biologically meaningful predictions about how systems will respond to perturbations. Although some of these practices are mainstream in specific sub-fields of biology, such practices are not widespread across all fields of biological sciences. With resources dedicated to better integrating biology and mathematical modeling, we envision a transformational improvement in the ability to describe and predict complex biological systems

Data from: Arboreal ecology of Plethodontidae: a review

Lungless salamanders in the family Plethodontidae are widely distributed and the most diverse lineage of caudates. Plethodontids occupy forested and freshwater habitats, where they can achieve remarkable abundance and biomass. The majority of tropical plethodontids are arboreal. Though generally considered ground dwelling, a large proportion of temperate species have been observed climbing shrubs, trees, and herbaceous vegetation. Approximately 45% of terrestrial and semi-aquatic (not including permanently aquatic) plethodontid species are known to obligately or facultatively climb vegetation; yet, with the exception of tropical plethodontids, the importance of arboreal habits is generally underappreciated. The potential benefits of arboreality vary based on life history and geography but may include improved olfaction, increased foraging potential, shelter and nesting, and predator avoidance. Constraints on arboreality include increased water loss rates and morphological limitations. Recognition of arboreal habits as a relevant component of salamander ecology is important in rapidly changing landscapes with anthropogenic alterations to midstory and canopy communities

List of Arboreal Plethodontid Salamanders

This list includes all plethodontid species known to climb plants, the general geographic region, the type of arboreality, and the sources of information

Charting a new frontier of science by integrating mathematical modeling to understand and predict complex biological systems

Biological systems are staggeringly complex. To untangle this complexity and make predictions about biological systems is a continuous goal of biological research. One approach to achieve these goals is to emphasize the use of quantitative measures of biological processes. Advances in quantitative biology data collection and analysis across scales (molecular, cellular, organismal, ecological) has transformed how we understand, categorize, and predict complex biological systems. Simultaneously, thanks to increased computational power, mathematicians, engineers and physical scientists -- collectively termed theoreticians -- have developed sophisticated models of biological systems at different scales. But there is still a disconnect between the two fields. This surge of quantitative data creates an opportunity to apply, develop, and evaluate mathematical models of biological systems and explore novel methods of analysis. The novel modeling schemes can also offer deeper understanding of principles in biology. In the context of this paper, we use “models” to refer to mathematical representations of biological systems. This data revolution puts scientists in a unique position to leverage information-rich datasets to improve descriptive modeling. Moreover, advances in technology allow inclusion of heterogeneity and variability within these datasets and mathematical models. This inclusion may lead to identifying previously undetermined variables driving or maintaining heterogeneity and diversity. Improved inclusion of variation may even improve biologically meaningful predictions about how systems will respond to perturbations. Although some of these practices are mainstream in specific sub-fields of biology, such practices are not widespread across all fields of biological sciences. With resources dedicated to better integrating biology and mathematical modeling, we envision a transformational improvement in the ability to describe and predict complex biological systems

Movement, demographics, and occupancy dynamics of a federally-threatened salamander: evaluating the adequacy of critical habitat

Critical habitat for many species is often limited to occupied localities. For rare and cryptic species, or those lacking sufficient data, occupied habitats may go unrecognized, potentially hindering species recovery. Proposed critical habitat for the aquatic Jollyville Plateau salamander (Eurycea tonkawae) and two sister species were delineated based on the assumption that surface habitat is restricted to springs and excludes intervening stream reaches. To test this assumption, we performed two studies to understand aspects of individual, population, and metapopulation ecology of E. tonkawae. First, we examined movement and population demographics using capture-recapture along a spring-influenced stream reach. We then extended our investigation of stream habitat use with a study of occupancy and habitat dynamics in multiple headwater streams. Indications of extensive stream channel use based on capture-recapture results included frequent movements of >15 m, and high juvenile abundance downstream of the spring. Initial occupancy of E. tonkawae was associated with shallow depths, maidenhair fern presence and low temperature variation (indicative of groundwater influence), although many occupied sites were far from known springs. Additionally, previously dry sites were three times more likely to be colonized than wet sites. Our results indicate extensive use of stream habitats, including intermittent ones, by E. tonkawae. These areas may be important for maintaining population connectivity or even as primary habitat patches. Restricting critical habitat to occupied sites will result in a mismatch with actual habitat use, particularly when assumptions of habitat use are untested, thus limiting the potential for recovery

Charting a New Frontier Integrating Mathematical Modeling in Complex Biological Systems from Molecules to Ecosystems

Advances in quantitative biology data collection and analysis across scales (molecular, cellular, organismal, and ecological) have transformed how we understand, categorize, and predict complex biological systems. This surge of quantitative data creates an opportunity to apply, develop, and evaluate mathematical models of biological systems and explore novel methods of analysis. Simultaneously, thanks to increased computational power, mathematicians, engineers, and physical scientists have developed sophisticated models of biological systems at different scales. Novel modeling schemes can offer deeper understanding of principles in biology, but there is still a disconnect between modeling and experimental biology that limits our ability to fully realize the integration of mathematical modeling and biology. In this work, we explore the urgent need to expand the use of existing mathematical models across biological scales, develop models that are robust to biological heterogeneity, harness feedback loops within the iterative-modeling process, and nurture a cultural shift toward interdisciplinary and cross-field interactions. Better integration of biological experimentation and robust mathematical modeling will transform our ability to understand and predict complex biological systems