Stock synthesis (SS) is a statistical age-structured population modeling framework that has been applied in a wide variety of fish assessments globally. The framework is highly scalable from ...data-weak situations where it operates as an age-structured production model, to complex situations where it can flexibly incorporate multiple data sources and account for biological and environmental processes. SS implements compensatory population dynamics through use of a function relating mean recruitment to spawner reproductive output. This function enhances the ability of SS to operate in data-weak situations and enables it to estimate fishery management quantities such as fishing rates that would provide for maximum sustainable yield and to employ these rates in forecasts of potential yield and future stock status. Complex model configurations such as multiple areas and multiple growth morphs are possible, tag-recapture data can be used to aid estimation of movement rates among areas, and most parameters can change over time in response to environmental and ecosystem factors. SS is coded using Auto-Differentiation Model Builder, so inherits its powerful capability to efficiently estimate hundreds of parameters using either maximum likelihood or Bayesian inference. Output processing, principally through a package developed in R, enables rapid model diagnosis. Details of the underlying population dynamics and the statistical framework used within SS are provided.
A common challenge for studying wildlife populations occurs when different survey methods provide inconsistent or incomplete inference on the trend, dynamics, or viability of a population. A ...potential solution to the challenge of conflicting or piecemeal data relies on the integration of multiple data types into a unified modeling framework, such as integrated population models (IPMs). IPMs are a powerful approach for species that inhabit spatially and seasonally complex environments. We provide guidance on exploiting the capabilities of IPMs to address inferential discrepancies that stem from spatiotemporal data mismatches. We illustrate this issue with analysis of a migratory species, the American Woodcock (Scolopax minor), in which individual monitoring programs suggest differing population trends. To address this discrepancy, we synthesized several long-term data sets (1963–2015) within an IPM to estimate continental-scale population trends, and link dynamic drivers across the full annual cycle and complete extent of the woodcock’s geographic range in eastern North America. Our analysis reveals the limiting portions of the life cycle by identifying time periods and regions where vital rates are lowest and most variable, as well as which demographic parameters constitute the main drivers of population change. We conclude by providing recommendations for resolving conflicting population estimates within an integrated modeling approach, and discuss how strategies (e.g., data thinning, expert opinion elicitation) from other disciplines could be incorporated into ecological analyses when attempting to combine multiple, incongruent data types.
The current extinction and climate change crises pressure us to predict population dynamics with ever‐greater accuracy. Although predictions rest on the well‐advanced theory of age‐structured ...populations, two key issues remain poorly explored. Specifically, how the age‐dependency in demographic rates and the year‐to‐year interactions between survival and fecundity affect stochastic population growth rates. We use inference, simulations and mathematical derivations to explore how environmental perturbations determine population growth rates for populations with different age‐specific demographic rates and when ages are reduced to stages. We find that stage‐ vs. age‐based models can produce markedly divergent stochastic population growth rates. The differences are most pronounced when there are survival‐fecundity‐trade‐offs, which reduce the variance in the population growth rate. Finally, the expected value and variance of the stochastic growth rates of populations with different age‐specific demographic rates can diverge to the extent that, while some populations may thrive, others will inevitably go extinct.
Fitness consequences of early‐life environmental conditions are often sex‐specific, but corresponding evidence for invertebrates remains inconclusive. Here, we use meta‐analysis to evaluate ...sex‐specific sensitivity to larval nutritional conditions in insects. Using literature‐derived data for 85 species with broad phylogenetic and ecological coverage, we show that females are generally more sensitive to food stress than males. Stressful nutritional conditions during larval development typically lead to female‐biased mortality and thus increasingly male‐biased sex ratios of emerging adults. We further demonstrate that the general trend of higher sensitivity to food stress in females can primarily be attributed to their typically larger body size in insects and hence higher energy needs during development. By contrast, there is no consistent evidence of sex‐biased sensitivity in sexually size‐monomorphic species. Drawing conclusions regarding sex‐biased sensitivity in species with male‐biased size dimorphism remains to wait for the accumulation of relevant data. Our results suggest that environmental conditions leading to elevated juvenile mortality may potentially affect the performance of insect populations further by reducing the proportion of females among individuals reaching reproductive age. Accounting for sex‐biased mortality is therefore essential to understanding the dynamics and demography of insect populations, not least importantly in the context of ongoing insect declines.
We provide meta‐analytic evidence that in insects, food stress during development typically leads to female‐biased mortality and thus increasingly male‐biased sex ratios of emerging adults. The results indicate that the higher environmental sensitivity of females is primarily mediated by their larger body size. Our study emphasizes that accounting for sex‐biased environmental sensitivity is essential to understanding the dynamics and demography of insect populations.
Abundant evidence supports the benefits accrued to the Florida panther (Puma concolor coryi) population via the genetic introgression project implemented in South Florida, USA, in 1995. Since then, ...genetic diversity has improved, the frequency of morphological and biomedical correlates of inbreeding depression have declined, and the population size has increased. Nevertheless, the panther population remains small and isolated and faces substantial challenges due to deterministic and stochastic forces. Our goals were 1) to comprehensively assess the demographics of the Florida panther population using long-term (1981–2015) field data and modeling to gauge the persistence of benefits accrued via genetic introgression and 2) to evaluate the effectiveness of various potential genetic management strategies. Translocation and introduction of female pumas (Puma concolor stanleyana) from Texas, USA, substantially improved genetic diversity. The average individual heterozygosity of canonical (non-introgressed) panthers was 0.386 ± 0.012 (SE); for admixed panthers, it was 0.615 ± 0.007. Survival rates were strongly age-dependent (kittens had the lowest survival rates), were positively affected by individual heterozygosity, and decreased with increasing population abundance. Overall annual kitten survival was 0.32 ± 0.09; sex did not have a clear effect on kitten survival. Annual survival of subadult and adult panthers differed by sex; regardless of age, females exhibited higher survival than males. Annual survival rates of subadult, prime adult, and old adult females were 0.97 ± 0.02, 0.86 ± 0.03, and 0.78 ± 0.09, respectively. Survival rates of subadult, prime adult, and old adult males were 0.66 ± 0.06, 0.77 ± 0.05, and 0.65 ± 0.10, respectively. For panthers of all ages, genetic ancestry strongly affected survival rate, where first filial generation (F1) admixed panthers of all ages exhibited the highest rates and canonical (mostly pre-introgression panthers and their post-introgression descendants) individuals exhibited the lowest rates. The most frequently observed causes of death of radio-collared panthers were intraspecific aggression and vehicle collision. Cause-specific mortality analyses revealed that mortality rates from vehicle collision, intraspecific aggression, other causes, and unknown causes were generally similar for males and females, although males were more likely to die from intraspecific aggression than females. The probability of reproduction and the annual number of kittens produced varied by age; evidence that ancestry or abundance influenced these parameters was weak. Predicted annual probabilities of reproduction were 0.35 ± 0.08, 0.50 ± 0.05, and 0.25 ± 0.06 for subadult, prime adult, and old adult females, respectively. The number of kittens predicted to be produced annually by subadult, prime adult, and old adult females were 2.80 ± 0.75, 2.67 ± 0.43, and 2.28 ± 0.83, respectively. The stochastic annual population growth rate estimated using a matrix population model was 1.04 (95% CI = 0.72–1.41). An individual-based population model predicted that the probability that the population would fall below 10 panthers within 100 years (quasi-extinction) was 1.4% (0–0.8%) if the adverse effects of genetic erosion were ignored. However, when the effect of genetic erosion was considered, the probability of quasi-extinction within 100 years increased to 17% (0–100%). Mean times to quasi-extinction, conditioned on going quasi-extinct within 100 years, was 22 (0–75) years when the effect of genetic erosion was considered. Sensitivity analyses revealed that the probability of quasi-extinction and expected time until quasi-extinction were most sensitive to changes in kitten survival parameters. Without genetic management intervention, the Florida panther population would face a substantially increased risk of quasi-extinction. The question, therefore, is not whether genetic management of the Florida panther population is needed but when and how it should be implemented. Thus, we evaluated genetic and population consequences of alternative genetic introgression strategies to identify optimal management actions using individual-based simulation models. Releasing 5 pumas every 20 years would cost much less ($200,000 over 100 years) than releasing 15 pumas every 10 years ($1,200,000 over 100 years) yet would reduce the risk of quasi-extinction by comparable amount (44–59% vs. 40–58%). Generally, releasing more females per introgression attempt provided little added benefit. The positive effects of the genetic introgression project persist in the panther population after 20 years. We suggest that managers contemplate repeating genetic introgression by releasing 5–10 individuals from other puma populations every 20–40 years. We also recommend that managers continue to collect data that will permit estimation and monitoring of kitten, adult, and subadult survival. We identified these parameters via sensitivity analyses as most critical in terms of their impact on the probability of and expected times to quasi-extinction. The continuation of long-term monitoring should permit the adaptation of genetic management strategies as necessary while collecting data that have proved essential in assessing the genetic and demographic health of the population. The prospects for recovery of the panther will certainly be improved by following these guidelines.
La población de pantera de Florida (Puma concolor coryi) mejoró tras la implementación en 1995 del proyecto de introgresión genética en el sur de Florida, USA, como lo demuestran varias líneas de evidencia. Desde entonces, su diversidad genética ha mejorado, la frecuencia de índices morfológicos y biomédicos correlacionados con depresión endogámica ha disminuido, y el tamaño de la población ha aumentado. Sin embargo, la población de panteras permanece pequeña, aislada, y se enfrenta a retos sustanciales producidos por fuerzas determinísticas y estocásticas. Los objetivos de este estudio fueron 1) evaluar exhaustivamente la demografía de la población de panteras de Florida usando datos de campo (del periodo 1981–2015) y modelos con el fin de calibrar en que medida persisten los beneficios adquiridos a través de la introgresión genética y 2) evaluar la efectividad de varias estrategias de manejo genético. La diversidad genética de la población mejoró sustancialmente con la introducción de pumas hembra (Puma concolor stanleyana) procedentes de Texas, USA. En panteras canónicas (no procedentes de introgresión), el valor medio de heterocigosidad individual fue 0.386 ± 0.012 (SE), y en panteras mezcladas 0.615 ± 0.007. En gran medida, las tasas de supervivencia dependieron de la edad (los cachorros tenían las tasas de supervivencia más bajas), estuvieron afectadas positivamente por la heterocigosidad individual, y disminuyeron cuando la población aumentó. La tasa de supervivencia total, independientemente del sexo del cachorro, fue de 0.32 ± 0.09. La tasa de supervivencia anual de panteras adultas y subadultas varió según el sexo; independientemente de la edad, las hembras vivieron más que los machos. Las tasas anuales de supervivencia de hembras subadultas, adultas y adultas mayores fueron 0.97 ± 0.02, 0.86 ± 0.03, y 0.78 ± 0.09, respectivamente. Las tasas de supervivencia de machos subadultos, adultos, y adultos mayores fueron 0.66 ± 0.06, 0.77 ± 0.05, y 0.65 ± 0.10, respectivamente. La ascendencia genética determinó en gran medida la tasa de supervivencia de panteras de cualquier edad, siendo mayor en la primera generación filiar (F1) de panteras mezcladas en todas las edades, y menor en los individuos canónicos (sobre todo panteras pre-introgresión y sus descendientes post-introgresión). En panteras con collares de radio telemetría, las causas de mortalidad más frecuentes fueron la agresión intraespecífica y la colisión con vehículos. El análisis de las causas de mortalidad reveló que en las categorías colisión con vehículos, agresión intraespecífica, otras causas y motivos desconocidos, la tasa de mortalidad de machos y hembras era similar, aunque los machos tenían más posibilidades de morir por agresión intraespecífica que las hembras. Las probabilidades de reproducción y el número anual de cachorros dependieron de la edad pero no de los ancestros o el tamaño de la población. Las probabilidades de reproducción de hembras subadultas, adultas, y adultas mayores se estimaron en 0.35 ± 0.08, 0.50 ± 0.05, y 0.25 ± 0.06, respectivamente. El número de cachorros por año y por pantera subadulta, adulta, y adulta mayor se estimó en 2.80 ± 0.75, 2.67 ± 0.43, y 2.28 ± 0.83, respectivamente. Usando un modelo demográfico matricial se estimó la tasa anual de crecimiento estocástico poblacional en 1.04 (95% CI = 0.72–1.41). Usando un modelo de población basado en el individuo e ignorando el impacto adverso de la erosión genética, se estimó la probabilidad de que la población disminuyese a menos de 10 panteras en 100 años (cuasi-extinción) en 1.4% (0–0.8%). Sin embargo, incluyendo el impacto de la erosión genética, la probabilidad de cuasi-extinción en 100 años aumentó al 17% (0–100%). El plazo medio para la cuasi-extinción, asumiendo que la cuasi-extinción ocurre en 100 años e incluyendo el impacto de la erosión genética, fue de 22 (0–75) años. Análisis de sensibilidad demostraron que la probabilidad de cuasi-extinción y el plazo hasta alcanzarla, dependían de los valores utilizados para los parámetros de supervivencia de cachorros. Sin manejo genético, la población de panteras de Florida se enfrentaría a un aumento sustancial del riesgo de cuasi-extinción. Por lo tanto, la pregunta no es si es necesario el manejo genético de la población de las panteras de Florida, sino cuándo y cómo implementarlo. Usando modelos de simulación basados en individuos, evaluamos diferentes estrategias de introgresión genética y sus posibles impactos en la población y en su genética. La reducción del r
Advances in species distribution modeling continue to be driven by a need to predict species responses to environmental change coupled with increasing data availability. Recent work has focused on ...development of methods that integrate multiple streams of data to model species distributions. Combining sources of information increases spatial coverage and can improve accuracy in estimates of species distributions. However, when fusing multiple streams of data, the temporal and spatial resolutions of data sources may be mismatched. This occurs when data sources have fluctuating geographic coverage, varying spatial scales and resolutions, and differing sources of bias and sparsity. It is well documented in the spatial statistics literature that ignoring the misalignment of different data sources will result in bias in both the point estimates and uncertainty. This will ultimately lead to inaccurate predictions of species distributions. Here, we examine the issue of misaligned data as it relates specifically to integrated species distribution models. We then provide a general solution that builds off work in the statistical literature for the change-of-support problem. Specifically, we leverage spatial correlation and repeat observations at multiple scales to make statistically valid predictions at the ecologically relevant scale of inference. An added feature of the approach is that addressing differences in spatial resolution between data sets can allow for the evaluation and calibration of lesser-quality sources in many instances. Using both simulations and data examples, we highlight the utility of this modeling approach and the consequences of not reconciling misaligned spatial data. We conclude with a brief discussion of the upcoming challenges and obstacles for species distribution modeling via data fusion.
ODE-based population models remain viable tools to investigate tumor growth and support clinical evidence. By following a fractional approach, this study derives analytical solutions for five of ...these models, whose parameters are best-fitted against extant clinical data. In terms of tumor growth prediction, results show that fractional models not only have better performance, which is mostly wanted for decision-making in oncology, but also reveal interesting characteristics to be further explored.
•Analytical solutions for five different ODE-based tumor growth models are derived.•Fractional power series method for investigating tumor growth dynamics is adopted.•Clinical data are used to best-fit free model parameters, including fractional order.•Numerical simulations are implemented for both fractional and classical models.•Results indicate that fractional models are better predictors, among other features.
Natural populations are exposed to seasonal variation in environmental factors that simultaneously affect several demographic rates (survival, development and reproduction). The resulting covariation ...in these rates determines population dynamics, but accounting for its numerous biotic and abiotic drivers is a significant challenge. Here, we use a factor‐analytic approach to capture partially unobserved drivers of seasonal population dynamics. We use 40 years of individual‐based demography from yellow‐bellied marmots (Marmota flaviventer) to fit and project population models that account for seasonal demographic covariation using a latent variable. We show that this latent variable, by producing positive covariation among winter demographic rates, depicts a measure of environmental quality. Simultaneously, negative responses of winter survival and reproductive‐status change to declining environmental quality result in a higher risk of population quasi‐extinction, regardless of summer demography where recruitment takes place. We demonstrate how complex environmental processes can be summarized to understand population persistence in seasonal environments.
The importance of seasonal processes determining the persistence of natural populations is increasingly recognised, but accounting for the numerous drivers of these processes is a significant challenge. Here, we apply a novel latent‐variable approach to circumvent this challenge in seasonal population models for yellow‐bellied marmots. The latent‐variable approach allows us to capture complex, partially unobserved drivers of marmot population dynamics into a univariate measure of environmental quality. We show that even in a winter‐adapted mammal, demographic responses to environmental quality in the inactive winter season determine population‐level effects of environmental change.
This article offers a well-organized and novel algorithm for solving time-fractional Fornberg–Whitham, Klein–Gordon equation and biological population models occurring from physics and engineering. ...The Elzaki (E)-transformation and decomposition process are combined in this algorithm. To evaluate the numerical outcomes of fractional-order partial differential equations, the E-transform decomposition method is generated in series form and nonlinearity terms are decayed. To demonstrate the feasibility of the proposed approach, numerical algorithms and examples are illustrated via graphs and tables. Moreover, it is viewed that the solutions of the new methodology are in strong correlation with the exact findings. Numerical simulations were carried out to ensure that the proposed methods are precise, as shown by the exact solutions resolving complex nonlinear problems.
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