A General Modeling Framework for Describing Spatially Structured Population Dynamics

Variation in movement across time and space fundamentally shapes the abundance and distribution of populations. Although a variety of approaches model structured population dynamics, they are limited to specific types of spatially structured populations and lack a unifying framework. Here, we propose a unified network‐based framework sufficiently novel in its flexibility to capture a wide variety of spatiotemporal processes including metapopulations and a range of migratory patterns. It can accommodate different kinds of age structures, forms of population growth, dispersal, nomadism and migration, and alternative life‐history strategies. Our objective was to link three general elements common to all spatially structured populations (space, time and movement) under a single mathematical framework. To do this, we adopt a network modeling approach. The spatial structure of a population is represented by a weighted and directed network. Each node and each edge has a set of attributes which vary through time. The dynamics of our network‐based population is modeled with discrete time steps. Using both theoretical and real‐world examples, we show how common elements recur across species with disparate movement strategies and how they can be combined under a unified mathematical framework. We illustrate how metapopulations, various migratory patterns, and nomadism can be represented with this modeling approach. We also apply our network‐based framework to four organisms spanning a wide range of life histories, movement patterns, and carrying capacities. General computer code to implement our framework is provided, which can be applied to almost any spatially structured population. This framework contributes to our theoretical understanding of population dynamics and has practical management applications, including understanding the impact of perturbations on population size, distribution, and movement patterns. By working within a common framework, there is less chance that comparative analyses are colored by model details rather than general principles

Spatial and temporal overlap of domestic cats (Felis catus) and native urban wildlife

Partial funding for Open Access provided by the UMD Libraries' Open Access Publishing Fund.Free-roaming domestic cats (Felis catus) are known to pose threats to ecosystem health via transmission of zoonotic diseases and predation of native wildlife. Likewise, free-roaming cats are also susceptible to predation or disease transmission from native wildlife. Physical interactions are required for many of these risks to be manifested, necessitating spatial and temporal overlap between cats and wildlife species. Therefore, knowledge of the location and extent of shared habitat and activity periods would benefit management programs. We used data from a 3-year camera trap survey to model species-specific occupancy and identify landscape variables that contribute to the distribution of free-roaming domestic cats and eight native mammal species in Washington, DC. (USA). Our analysis includes five species that are common prey items of domestic cats, and three species that are potential disease vectors or are otherwise known to be a risk to cats. We then predicted the probability of occupancy and estimated the probability of spatial overlap between cats and each native wildlife species at multiple scales. We also used kernel density estimations to calculate temporal overlap between cats and each native wildlife species. Across spatial scales, occupancy for potential disease vector species was generally positively correlated with canopy cover and open water. Prey species were also generally positively correlated with canopy cover, but displayed negative associations with human population density and inconsistent associations with average per capita income. Domestic cat occupancy was negatively correlated with natural habitat characteristics and positively correlated with human population density. Predicted spatial overlap between domestic cats and native wildlife was greatest for potential disease vector species. Temporal overlap was high (>0.50) between cats and all but two native wildlife species, indicating that temporal overlap is probable wherever species overlap spatially. Our findings indicate that the risk to and from domestic cats varies across urban landscapes, but primarily arises from human activities. As such, humans are implicated in the negative outcomes that result from cats interacting with wildlife. Data-driven management to reduce such interactions can aid in cat population management, biodiversity conservation, and public health campaigns

Alternate migration strategies of eastern monarch butterflies revealed by stable isotopes

Alternative life history strategies are mechanisms by which organisms are able to maximize fitness across a range of environmental conditions. Fitness is maximized by different strategies depending on context, resulting in trade-offs between life history strategies. Monarch butterflies (Danaus plexippus) employ both migratory and resident life history strategies. Since residents breed throughout the year, but migrants overwinter in reproductive diapause, there are fitness trade-offs between the two strategies. We used stable isotope analysis to evaluate the geographic origins of monarchs in a yearround population in south Florida. Based on stable isotope profiles of hydrogen and carbon (δ2H and δ13C values), we found that 48% (16/33) of monarchs collected in south Florida are migrants that originated from outside the sampling region. Migrants had a larger wing length than residents; thus, switching to a resident strategy could alter their probability of reproductive success. Further work is needed to investigate the mechanism underlying this pattern, but these findings show that alternate life history strategies and sex-specific behaviors are underexplored factors influencing monarch migration and evolution

Metals and metal isotopes incorporation in insect wings: Implications for geolocation and pollution exposure

data and R code for Reich et al. 2023 doi: 10.3389/fevo.2023.108590