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Population dynamics models have long assumed that populations are composed of a restricted number of groups, where individuals in each group have identical demographic rates and where all groups are similarly affected by density-dependent and -independent effects. However, individuals usually vary tremendously in performance and in their sensitivity to environmental conditions or resource limitation, such that individual contributions to population growth will be highly variable. Recent efforts to integrate individual processes in population models open up new opportunities for the study of eco-evolutionary processes, such as the density-dependent influence of environmental conditions on the evolution of morphological, behavioral, and life-history traits. We review recent advances that demonstrate how including individual mechanisms in models of population dynamics contributes to a better understanding of the drivers of population dynamics within the framework of integrated population models (IPMs). IPMs allow for the integration in a single inferential framework of different data types as well as variable population structure including sex, social group, or territory, all of which can be formulated to include individual-level processes. Through a series of examples, we first show how IPMs can be beneficial for getting more accurate estimates of demographic traits than classic matrix population models by including basic population structure and their influence on population dynamics. Second, the integration of individual- and population-level data allows estimating density-dependent effects along with their inherent uncertainty by directly using the population structure and size to feedback on demography. Third, we show how IPMs can be used to study the influence of the dynamics of continuous individual traits and individual quality on population dynamics. We conclude by discussing the benefits and limitations of IPMs for integrating data at different spatial, temporal, and organismal levels to build more mechanistic models of population dynamics.

Persecution and overexploitation by humans are major causes of species extinctions. Rare species, often confined to small geographic ranges, are usually at highest risk, whereas extinctions of superabundant species with very large ranges are rare. The Yellow‐breasted Bunting (Emberiza aureola) used to be one of the most abundant songbirds of the Palearctic, with a very large breeding range stretching from Scandinavia to the Russian Far East. Anecdotal information about rapid population declines across the range caused concern about unsustainable trapping along the species’ migration routes. We conducted a literature review and used long‐term monitoring data from across the species’ range to model population trend and geographical patterns of extinction. The population declined by 84.3–94.7% between 1980 and 2013, and the species’ range contracted by 5000 km. Quantitative evidence from police raids suggested rampant illegal trapping of the species along its East Asian flyway in China. A population model simulating an initial harvest level of 2% of the population, and an annual increase of 0.2% during the monitoring period produced a population trajectory that matched the observed decline. We suggest that trapping strongly contributed to the decline because the consumption of Yellow‐breasted Bunting and other songbirds has increased as a result of economic growth and prosperity in East Asia. The magnitude and speed of the decline is unprecedented among birds with a comparable range size, with the exception of the Passenger Pigeon (Ectopistes migratorius), which went extinct in 1914 due to industrial‐scale hunting. Our results demonstrate the urgent need for an improved monitoring of common and widespread species’ populations, and consumption levels throughout East Asia.

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.

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.

Many historical processes exhibit recurrent patterns of change. Century-long periods of population expansion come before long periods of stagnation and decline; the dynamics of prices mirror population oscillations; and states go through strong expansionist phases followed by periods of state failure, endemic sociopolitical instability, and territorial loss. Peter Turchin and Sergey Nefedov explore the dynamics and causal connections between such demographic, economic, and political variables in agrarian societies and offer detailed explanations for these long-term oscillations--what the authors call secular cycles.

Many animal populations are subject to hunting or fishing in the wild. Detailed knowledge of demographic parameters (e.g. survival, reproduction) and temporal dynamics of such populations is crucial for sustainable management. Despite their relevance for management decisions, structure and size of exploited populations are often not known, and data limited. Recently, joint analysis of different types of demographic data, such as population counts, reproductive data and capture-mark-recapture data, within integrated population models (IPMs) has gained much popularity as it may allow estimating population size and structure, as well as key demographic rates, while fully accounting for uncertainty. IPMs built so far for exploited populations have typically been built as age-structured population models. However, the age of harvested individuals is usually difficult and/or costly to assess and therefore often not available. Here, we introduce an IPM structured by body size classes, which allows making efficient use of data commonly available in exploited populations for which accurate information on age is often missing. The model jointly analyzes size-at-harvest data, capture-mark-recapture-recovery data and reproduction data from necropsies, and we illustrate its applicability in a case study involving heavily hunted wild boar. This species has increased in abundance over the last decades despite intense harvest, and the IPM analysis provides insights into the roles of natural mortality, body growth, maturation schedules and reproductive output in compensating for the loss of individuals to hunting. Early maturation and high reproductive output contributed to wild boar population persistence despite a strong hunting pressure. We thus demonstrate the potential of size-class-structured IPMs as tools to investigate the dynamics of exploited populations with limited information on age, and highlight both the applicability of this framework to other species and its potential for follow-up analyses highly relevant to management.

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

Assessing the effects of climate and interspecific relationships on communities is challenging because of the complex interplay between species population dynamics, their interactions, and the need to integrate information across several biological levels (individuals – populations – communities). Usually used to quantify single‐species demography, integrated population models (IPMs) have recently been extended to communities. These models allow fitting multispecies matrix models to data from multiple sources while simultaneously accounting for uncertainty in each data source. We used multispecies IPMs accommodating climatic variables to quantify the relative contribution of climate vs. interspecific interactions on demographic parameters, such as survival and breeding success, in the dynamics of a predator–prey system. We considered a stage‐structured predator–prey system combining 22 years of capture–recapture data and population counts of two seabirds, the Brown Skua (Catharacta lönnbergi) and its main prey the Blue Petrel (Halobaena caerulea), both breeding on the Kerguelen Islands in the Southern Ocean. Our results showed that climate and predator–prey interactions drive the demography of skuas and petrels in different ways. The breeding success of skuas appeared to be largely driven by the number of petrels and to a lesser extent by intraspecific density‐dependence. In contrast, there was no evidence of predation effects on the demographic parameters of petrels, which were affected by oceanographic factors. We conclude that bottom‐up mechanisms are the main drivers of this skua–petrel system.