Explore the vast range of titles available.

Classical approaches for the analyses of density dependence assume that all the individuals in a population equally respond and equally contribute to density dependence. However, in age-structured populations, individuals of different ages may differ in their responses to changes in population size and how they contribute to density dependence affecting the growth rate of the whole population. Here we apply the concept of critical age classes, i.e., a specific scalar function that describes how one or a combination of several age classes affect the demographic rates negatively, in order to examine how total density dependence acting on the population growth rate depends on the age-specific population sizes. In a 38-yr dataset of an age-structured great tit (Parus major) population, we find that the age classes, including the youngest breeding females, were the critical age classes for density regulation. These age classes correspond to new breeders that attempt to take a territory and that have the strongest competitive effect on other breeding females. They strongly affected population growth rate and reduced recruitment and survival rates of all breeding females. We also show that depending on their age class, females may differently respond to varying density. In particular, the negative effect of the number of breeding females was stronger on recruitment rate of the youngest breeding females. These findings question the classical assumptions that all the individuals of a population can be treated as having an equal contribution to density regulation and that the effect of the number of individuals is age independent. Our results improve our understanding of density regulation in natural populations.

Sandhill cranes (Antigone canadensis) inhabiting the midcontinent of North America have been hunted since the 1960s under management goals of maintaining abundance, retaining geographic distribution, and maximizing sustainable harvest. Some biologists have raised concerns regarding harvest sustainability because sandhill cranes have lower reproductive rates than other game birds. We summarized demographic information in an age-structured matrix model to better understand population dynamics and harvest. Population indices and recovered harvest since the early 1980s suggest midcontinent sandhill cranes have experienced an average long-term annual growth of 0.9%; meanwhile, harvest has increased 1.8% annually. Adult survival and recruitment rates estimated from field data required modest adjustments (1–3%) so that model-derived growth rates matched growth estimated from a long-term survey (0.887 adult survival and 0.199 females/breeding female). Considering 0.9% long-term annual growth, sandhill cranes could be harvested at a rate of 6.6% if harvest was additive to natural mortality (assumed to be 0.05) or 11.3% if harvest mortality compensated for natural mortality. Life-history characteristics for long-lived organisms and demographic evidence suggested that hunter harvest was primarily additive. Differential harvest rates of segments of sandhill cranes in the midcontinent population derived from differential exposure to hunting suggested potentially unsustainable harvest for greater sandhill cranes (A. c. tabida) from 2 breeding segments. Overall, demographic evidence suggests that the harvest of sandhill cranes in the midcontinent population has been managed sustainably. Monitoring activities that reduce nuisance variation and estimate vital and harvest rates by subspecies would support continued management of sandhill cranes that are of interest to hunters and bird watchers.

We contribute a full analysis of theoretical and numerical aspects of the collocation approach recently proposed by some of the authors to compute the basic reproduction number of structured population dynamics as spectral radius of certain infinite-dimensional operators. On the one hand, we prove under mild regularity assumptions on the models coefficients that the concerned operators are compact, so that the problem can be properly recast as an eigenvalue problem thus allowing for numerical discretization. On the other hand, we prove through detailed and rigorous error and convergence analyses that the method performs the expected spectral accuracy. Several numerical tests validate the proposed analysis by highlighting diverse peculiarities of the investigated approach.

For species that utilize different habitats throughout their life cycle, the habitat limitation at a given stage can act as a bottleneck on population abundance, impacting density-dependent processes such as individual growth and survival. We explore the influence of habitat limitation on population dynamics by developing a multi-stage population model based on lake-dwelling rainbow trout (Oncorhynchus mykiss) populations where adults occupy the lake habitat but use tributaries for spawning and juvenile rearing. The model details density-dependent ecological processes and ontogenetic habitat shifts, harvest mortality, and the impact of climate on growth. We ran model simulations using a range of early life stage habitat availabilities and climatic conditions representative of the native range of rainbow trout in Canada and compared the results to empirical data. The results suggest that (1) increases in early life stage habitat leads to increases in population abundance but, due to density-dependent processes, also results in slower growing stunted populations; (2) population bottlenecks can occur at any life stage, even at the adult stage if spawning and rearing habitats are abundant; (3) when the level of competition for early life stages is increased, inter-cohort competition can lead to population cycles. The model’s conclusions are further reinforced by empirical data showing a similar trend in the relationship between fish density and maximum size and providing evidence that limited early life stage habitat leads to lower fish densities and larger fish size. We provide a model that links environmental conditions to population dynamics and is useful for fisheries management and habitat conservation decisions.

Modelling population dynamics in ecological systems reveals properties that are difficult to find by empirical means, such as the probability that a population will go extinct when it is exposed to harvesting. To study these properties, we use an aquatic ecological system containing one fish species and an underlying resource as our models. In particular, we study a class of stage-structured population systems with and without starvation. In these models, we study the resilience, the recovery potential, and the probability of extinction and show how these properties are affected by different harvesting rates, both in a deterministic and stochastic setting. In the stochastic setting, we develop methods for deriving estimates of these properties. We estimate the expected outcome of emergent population properties in our models, as well as measures of dispersion. In particular, two different approaches for estimating the probability of extinction are developed. We also construct a method to determine the recovery potential of a species that is introduced in a virgin environment.

Jaguars are the largest carnivores in the Neotropics with high conservation priority. Population trends of top predators such as jaguars provide valuable information on demography, use of habitats, and individual interactions, which serve to establish conservation and management actions. We studied spatiotemporal population trends of jaguars in a tropical rainforest in the Calakmul Biosphere Reserve (CBR) located in southern Mexico. We used sex‐structured open capture–recapture models (OSCR) to estimate jaguar population density related to environmental variables and overlap of individual activity centers to evaluate their spatial interactions. To measure the overlap in the circadian activity, a kernel estimator was used, and intervals of time among pairs of co‐occurrences were analyzed to detect spatiotemporal associations between individuals. The population density of jaguars was 1.03 individuals/100 km2 (0.59 and 0.44 individuals/100 km2 for males and females, respectively). Capture probabilities differed between males and females probably due to differences in their use of human‐made trails. Despite temporal variations in population density and individual activity centers, these appeared to have no effect on parameters. Instead, we observed a relationship between (1) distance to roads and detection, (2) net primary productivity and movement, and (3) distance to borders and population density. Temporal circadian overlap showed low values between sexes, although slightly higher values were observed in the rainy seasons. Overall, we found jaguars segregating in space and time. We highlight the importance of the CBR in maintaining continuous suitable habitat and reducing edge effects detrimental to jaguar population density. Jaguar density in the largest population in Mesoamerica was similar among sexes but affected by distance from the borders. Overall, jaguars segregate in space and time, highlighting the importance of maintaining a continuous, suitable habitat to allow intraspecific interactions and persistence in the long term.

Population viability analysis (PVA) is widely used to assess population-level impacts of environmental changes on species. When combined with sensitivity analysis, PVA yields insights into the effects of parameter and model structure uncertainty. This helps researchers prioritize efforts for further data collection so that model improvements are efficient and helps managers prioritize conservation and management actions. Usually, sensitivity is analyzed by varying one input parameter at a time and observing the influence that variation has over model outcomes. This approach does not account for interactions among parameters. Global sensitivity analysis (GSA) overcomes this limitation by varying several model inputs simultaneously. Then, regression techniques allow measuring the importance of input-parameter uncertainties. In many conservation applications, the goal of demographic modeling is to assess how different scenarios of impact or management cause changes in a population. This is challenging because the uncertainty of input-parameter values can be confounded with the effect of impacts and management actions. We developed a GSA method that separates model outcome uncertainty resulting from parameter uncertainty from that resulting from projected ecological impacts or simulated management actions, effectively separating the 2 main questions that sensitivity analysis asks. We applied this method to assess the effects of predicted sea-level rise on Snowy Plover (Charadrius nivosus). A relatively small number of replicate models (approximately 100) resulted in consistent measures of variable importance when not trying to separate the effects of ecological impacts from parameter uncertainty. However, many more replicate models (approximately 500) were required to separate these effects. These differences are important to consider when using demographic models to estimate ecological impacts of management actions. El análisis de viabilidad poblacional (AVP) se usa ampliamente para valorar los impactos a nivel poblacional de los cambios ambientales sobre las especies. Cuando se combina con el análisis de sensibilidad, el AVP produce percepciones sobre los efectos de los parámetros y la incertidumbre de estructura de modelo. Esto ayuda a los investigadores a priorizar los esfuerzos para la futura recolección de datos de tal manera que las mejoras del modelo son eficientes y ayudan a los administradores a priorizar las acciones de manejo y de conservación. Generalmente, la sensibilidad se analiza al variar un parámetro de entrada a la vez y observar la influencia que la variación tiene sobre los resultados de los modelos. Esta estrategia no toma en cuenta las interacciones entre los parámetros. El análisis de sensibilidad global (ASG) supera esta limitación al variar diferentes contribuciones de modelos simultáneamente. Después, las técnicas de regresión permiten que se mida la importancia de la incertidumbre de la contribución de los parámetros. En muchas aplicaciones de la conservación, el objetivo del modelado demográfico es valorar cómo los escenarios diferentes de impacto o manejo causan cambios en una población. Esto es un obstáculo porque la incertidumbre de los valores de contribución del parámetro puede ser confundidos con el efecto de los impactos y las acciones de manejo. Desarrollamos un método de ASG que separa la incertidumbre del resultado del modelo producto de la incertidumbre del parámetro de aquella que es producto de los impactos ecológicos proyectados o las acciones de manejo simuladas, separando efectivamente las dos preguntas principales que hace el análisis de sensibilidad. Aplicamos este método para valorar los efectos del alza pronosticada del nivel del mar sobre el chorlitejo blanco (Charadrius nivosus). Un número relativamente pequeño de modelos replicados (aproximadamente 100) resultaron en medidas consistentes de importancia de variables cuando no se trataron de separar los efectos de los impactos ecológicos de la incertidumbre del parámetro. Sin embargo, se requirieron mucho más modelos replicados (aproximadamente 500) para separar estos efectos. Es importante considerar estas diferencias cuando se usan modelos demográficos para estimar los impactos ecológicos de las acciones de manejo.

Spatial synchrony plays an important role in dictating the dynamics of spatial and stage-structured populations. Here we argue that, unlike the Moran effect where spatial synchrony is driven by exogenous factors, spatial correlation in intrinsic/local-scale processes can affect the level of spatial synchrony among distinct sub-populations, and therefore the persistence of the entire population. To explore this mechanism, we modelled the consequences of spatial heterogeneity in aquatic habitat quality, and that of temporal variation in local extinction probability, on the persistence of stage-structured mosquito populations. As a model system, we used two widely distributed mosquito species, Aedes albopictus and Culex pipiens, both key vectors of a range of infectious diseases. Spatial heterogeneity in aquatic habitat quality led to increased population persistence, and this pattern was more pronounced at intermediate dispersal rates, and in the long-dispersing species (C. pipiens). The highest regional persistence was obtained at high dispersal rates. This is probably because dispersal, in our model, did not carry any additional costs. Population persistence of both species was negatively correlated with increased temporal variation in local extinction probability. These differences were stronger in the short-dispersing species (A. albopictus), especially at intermediate dispersal rates. The dispersal of A. albopictus adults in each time step was limited to the nearest habitat patches, weakening the positive effect of spatial heterogeneity in aquatic habitat quality on population persistence. In contrast, C. pipiens adults could disperse into more remote sub-populations, resulting in much higher recolonization rates. Hence, the negative effect of temporal variation in local extinction probability on patch occupancy disappeared at intermediate dispersal rates. We suggest that effectively controlling these two mosquito species requires making few spatially synchronized control efforts (i.e., generating high temporal variation in local extinction probability), rather than many asynchronized local control efforts. Finally, our model can be easily fitted to other organisms characterized by complex life cycles, and it can be also used to examine alternative scenarios, including the effect of spatial configuration of local habitat patches and dispersal kernel shape on population persistence.

Survival probability is fundamental for understanding population dynamics. Methods for estimating survival probability from field data typically require marking individuals, but marking methods are not possible for arthropod species that molt their exoskeleton between life stages. We developed a novel Bayesian state‐space model to estimate arthropod larval survival probability from stage‐structured count data. We performed simulation studies to evaluate estimation bias due to detection probability, individual variation in stage duration, and study design (sampling frequency and sample size). Estimation of cumulative survival probability from oviposition to pupation was robust to potential sources of bias. Our simulations also provide guidance for designing field studies with minimal bias. We applied the model to the monarch butterfly (Danaus plexippus), a declining species in North America for which conservation programs are being implemented. We estimated cumulative survival from egg to pupation from monarch counts conducted at 18 field sites in three landcover types in Iowa, USA, and Ontario, Canada: road right‐of‐ways, natural habitats (gardens and restored meadows), and agricultural field borders. Mean predicted survival probability across all landcover types was 0.014 (95% CI: 0.004–0.024), four times lower than previously published estimates using an ad hoc estimator. Estimated survival probability ranged from 0.002 (95% CI: 7.0E−7 to 0.034) to 0.058 (95% CI: 0.013–0.113) at individual sites. Among landcover types, agricultural field borders in Ontario had the highest estimated survival probability (0.025 with 95% CI: 0.008–0.043) and natural areas had the lowest estimated survival probability (0.008 with 95% CI: 0.009–0.024). Monarch production was estimated as adults produced per milkweed stem by multiplying survival probabilities by eggs per milkweed at these sites. Monarch production ranged from 1.0 (standard deviation [SD] = 0.68) adult in Ontario natural areas in 2016 to 29.0 (SD = 10.42) adults in Ontario agricultural borders in 2015 per 6809 milkweed stems. Survival estimates are critical to monarch population modeling and habitat restoration efforts. Our model is a significant advance in estimating survival probability for monarch butterflies and can be readily adapted to other arthropod species with stage‐structured life histories.

Dispersal and dormancy are two strategies that allow recolonization of empty patches and escape from kin competition. Because they presumably respond to similar evolutionary forces, it is tempting to consider that these strategies may substitute for each other. Yet in order to predict the outcome of the evolution of dispersal and dormancy, and to characterize the emerging covariation between both traits, it is necessary to consider models where dispersal and dormancy evolve jointly. Here, we analyze the evolution of dispersal and dormancy as a function of direct fitness costs, environmental variation, and competition among relatives. We consider two scenarios depending on whether the rates of dormancy for philopatric and dispersed individuals are constrained to be the same (unconditional dormancy) or allowed to be different (conditional dormancy). We show that only philopatric individuals should enter dormancy, at a rate increasing with increasing rates of local extinction and decreasing population sizes. When dormancy and dispersal evolve jointly, we observe a wide range of evolutionary outcomes. In particular, we find that the pattern of covariation between the evolutionarily stable rates of dispersal and dormancy is molded by the rate of extinction and the local population size.