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The success of species reintroductions can depend on a combination of environmental, demographic, and genetic factors. Although the importance of these factors in the success of reintroductions is well‐accepted, they are typically evaluated independently, which can miss important interactions. For species that persist in metapopulations, movement through and interaction with the landscape is predicted to be a vital component of persistence. Simulation‐based approaches are a promising technique for evaluating the independent and combined effects of these factors on the outcome of various reintroduction and associated management actions. We report results from a simulation study of bull trout (Salvelinus confluentus) reintroduction to three watersheds of the Pend Oreille River system in northeastern Washington State, USA. We used an individual‐based, spatially explicit simulation model to evaluate how reintroduction strategies, life history variation, and riverscape structure (e.g., network topology) interact to influence the demographic and genetic characteristics of reintroduced bull trout populations in three watersheds. Simulation scenarios included a range of initial genetic stocks (informed by empirical bull trout genetic data), variation in migratory tendency and life history, and two landscape connectivity alternatives representing a connected network (isolation‐by‐distance) and a fragmented network (isolation‐by‐barrier, using the known existing barriers). A novel feature of these simulations was the ability to consider the interaction of both demographic and genetic (i.e., demogenetic) factors in riverscapes with implicit asymmetric movement probabilities across the barriers. We found that connectivity (presence or absence of barriers) had the largest effect on demographic and genetic outcomes over 200 yr, with a greater effect than both initial genetic diversity and life history variation. We also identified regions of the study system in which bull trout populations persisted across a wide range of demographic, life history, and environmental connectivity parameters. Finally, we found no evidence that initial neutral genetic diversity influenced genetic diversity and structure after 200 yr; instead, genetic drift due to stray rate and population isolation dominated and erased any initial differences in genetic diversity. Our results highlight the utility of spatially explicit demogenetic approaches in exploring and understanding population dynamics—and their implications for management strategies—in fresh waters.

Drivers of recruitment in sessile marine organisms are often poorly understood, due to the rapidly changing requirements experienced during early ontogeny. The complex suite of physical, biological, and ecological interactions beginning at larval settlement involves a series of trade-offs that influence recruitment success. For example, while cryptic settlement within complex microhabitats is a commonly observed phenomenon in sessile marine organisms, it is unclear whether trade-offs between competition in cryptic refuges and predation on exposed surfaces leads to higher recruitment. To explore the trade-offs during the early ontogeny of scleractinian corals, we combined field observations with laboratory and field experiments to develop a mechanistic understanding of coral recruitment success. Multiple experiments conducted over 15 months in Palau (Micronesia) allowed a mechanistic approach to study the individual factors involved in recruitment: settlement behavior, growth, competition, and predation, as functions of microhabitat and ontogeny. We finally developed and tested a predictive recruitment model with the broader aim of testing whether our empirical insights explained patterns of coral recruitment and quantifying the relative importance of each trade-off. Coral settlement was higher in crevices than exposed microhabitats, but post-settlement bottlenecks differed markedly in the presence (uncaged) and absence (caged) of predators. Incidental predation by herbivores on exposed surfaces at early post-settlement (<3 mm) stages and targeted predation by corallivores at late post-settlement (3–10 mm) stages exceeded competition in crevices as major drivers of mortality. In contrast, when fish were excluded, competition with macroalgae and heterotrophic invertebrates intensified mortality, particularly in crevices. As a result, post-settlement trade-offs were reversed, and recruitment was more than twofold higher on exposed surfaces than crevices. Once post-settlement bottlenecks were overcome, survival was higher on exposed surfaces regardless of fish exclusion. However, maximum recruitment occurred in crevices of uncaged treatments, being ninefold higher than caged treatments. Overall, we characterize recruitment success throughout the earliest life-history stages of corals and uncover some intriguing trade-offs between growth, competition and predation, highlighting how these change and even reverse during ontogeny and under alternate disturbance regimes.

Forecasting the dynamics of influenza outbreaks could be useful for decision‐making regarding the allocation of public health resources. Reliable forecasts could also aid in the selection and implementation of interventions to reduce morbidity and mortality due to influenza illness. This paper reviews methods for influenza forecasting proposed during previous influenza outbreaks and those evaluated in hindsight. We discuss the various approaches, in addition to the variability in measures of accuracy and precision of predicted measures. PubMed and Google Scholar searches for articles on influenza forecasting retrieved sixteen studies that matched the study criteria. We focused on studies that aimed at forecasting influenza outbreaks at the local, regional, national, or global level. The selected studies spanned a wide range of regions including USA, Sweden, Hong Kong, Japan, Singapore, United Kingdom, Canada, France, and Cuba. The methods were also applied to forecast a single measure or multiple measures. Typical measures predicted included peak timing, peak height, daily/weekly case counts, and outbreak magnitude. Due to differences in measures used to assess accuracy, a single estimate of predictive error for each of the measures was difficult to obtain. However, collectively, the results suggest that these diverse approaches to influenza forecasting are capable of capturing specific outbreak measures with some degree of accuracy given reliable data and correct disease assumptions. Nonetheless, several of these approaches need to be evaluated and their performance quantified in real‐time predictions.

Aim Reintroducing carnivores is a widely used approach to restore the natural integrity of ecosystems. Species distribution models (SDMs) and connectivity analyses are valuable tools for planning reintroductions and identifying release sites but are rarely combined. We propose a new framework combining SDMs, connectivity modelling and individual‐based models (IBMs) to assess the feasibility of various reintroduction scenarios. As a case study, we applied this framework to plan a potential reintroduction of the Eurasian lynx (Lynx lynx) to the Apennines by: (i) assessing niche overlap between potential source and target populations; (ii) integrating habitat suitability and connectivity to select release sites and (iii) evaluating reintroduction outcomes through IBMs. Location Apennines, Peninsular Italy. Methods We combined niche overlap analysis, ensembles of fine‐tuned SDMs and circuit‐theory techniques to model connectivity. Then, we integrated suitability and connectivity predictions within a GIS environment to identify the optimal release sites under different scenarios. Finally, we used IBMs to assess population viability, site occupancy and dispersal. Results Niche overlap suggested that the Carpathian lynx populations may serve as a valid reintroduction source. Integrating habitat and connectivity models highlighted the most functional sites in the Central (CA) and Northern Apennines (NA). A scenario with individuals released in both CA and NA did not outperform the single‐area scenarios. Releasing individuals only in CA showed long‐term feasibility but a higher risk of isolation, while release only in NA would not result in viable populations in the long term, despite closer proximity to suitable areas in the Alps. Main Conclusions Our framework can help practitioners with integrating functional connectivity within the selection of release sites for species reintroductions. We recommend incorporating demography, as well as dispersal and settlement phases, when evaluating reintroduction scenarios. This approach identifies critical mortality areas, predicts population size, site occupancy and connectivity and enhances decision‐making for successful reintroductions.

Phenology models are becoming increasingly important tools to accurately predict how climate change will impact the life histories of organisms. We propose a class of integral projection phenology models derived from stochastic individual‐based models of insect development and demography. Our derivation, which is based on the rate summation concept, produces integral projection models that capture the effect of phenotypic rate variability on insect phenology, but which are typically more computationally frugal than equivalent individual‐based phenology models. We demonstrate our approach using a temperature‐dependent model of the demography of the mountain pine beetle (Dendroctonus ponderosae Hopkins), an insect that kills mature pine trees. This work illustrates how a wide range of stochastic phenology models can be reformulated as integral projection models. Due to their computational efficiency, these integral projection models are suitable for deployment in large‐scale simulations, such as studies of altered pest distributions under climate change. Phenology models are important tools for forecasting the effects of climate change on ecosystems. We derive integral projection models of phenology that are deterministic but which retain the effects of stochastic rate variation and seasonal forcing. The resultant integral projection models are useful for integration in large scale earth system models due to their computational efficiency.

Optimal foraging theory (OFT) is based on the ecological concept that organisms select behaviors that convey future fitness, and on the mathematical concept of optimization: finding the alternative that provides the best value of a fitness measure. As implemented in, for example, state-based dynamic modeling, OFT is powerful for one key problem of modern ecology: modeling behavior as a tradeoff among competing fitness elements such as growth, risk avoidance, and reproductive output. However, OFT is not useful for other modern problems such as representing feedbacks within systems of interacting, unique individuals: When we need to model foraging by each of many individuals that interact competitively or synergistically, optimization is impractical or impossible—there are no optimal behaviors. For such problems we can, however, still use the concept of future fitness to model behavior by replacing optimization with less precise (but perhaps more realistic) techniques for ranking alternatives. Instead of simplifying the systems we model until we can find optimal behavior, we can use theory based on inaccurate predictions, coarse approximations, and updating to produce good behavior in more complex and realistic contexts. This so-called state- and prediction-based theory (SPT) can, for example, produce realistic foraging decisions by each of many unique, interacting individuals when growth rates and predation risks vary over space and time. Because SPT lets us address more natural complexity and more realistic problems, it is more easily tested against more kinds of observation and more useful in management ecology. A simple foraging model illustrates how SPT readily accommodates complexities that make optimization intractable. Other models use SPT to represent contingent decisions (whether to feed or hide, in what patch) that are tradeoffs between growth and predation risk, when both growth and risk vary among hundreds of patches, vary unpredictably over time, depend on characteristics of the individuals, are subject to feedbacks from competition, and change over the daily light cycle. Modern ecology demands theory for tradeoff behaviors in complex contexts that produce feedbacks; when optimization is infeasible, we should not be afraid to use approximate fitness-seeking methods instead.

1. Animals are embedded in dynamically changing networks of relationships with conspecifics. These dynamic networks are fundamental aspects of their environment, creating selection on behaviours and other traits. However, most social network-based approaches in ecology are constrained to considering networks as static, despite several calls for such analyses to become more dynamic. 2. There are a number of statistical analyses developed in the social sciences that are increasingly being applied to animal networks, of which stochastic actor-oriented models (SAOMs) are a principal example. SAOMs are a class of individual-based models designed to model transitions in networks between discrete time points, as influenced by network structure and covariates. It is not clear, however, how useful such techniques are to ecologists, and whether they are suited to animal social networks. 3. We review the recent applications of SAOMs to animal networks, outlining findings and assessing the strengths and weaknesses of SAOMs when applied to animal rather than human networks. We go on to highlight the types of ecological and evolutionary processes that SAOMs can be used to study. 4. SAOMs can include effects and covariates for individuals, dyads and populations, which can be constant or variable. This allows for the examination of a wide range of questions of interest to ecologists. However, high-resolution data are required, meaning SAOMs will not be useable in all study systems. It remains unclear how robust SAOMs are to missing data and uncertainty around social relationships. 5. Ultimately, we encourage the careful application of SAOMs in appropriate systems, with dynamic network analyses likely to prove highly informative. Researchers can then extend the basic method to tackle a range of existing questions in ecology and explore novel lines of questioning.

A major issue in evolutionary biology is explaining patterns of differentiation observed in population genomic data, as divergence can be due to both direct selection on a locus and genetic hitchhiking. \"Divergence hitchhiking\" (DH) theory postulates that divergent selection on a locus reduces gene flow at physically linked sites, facilitating the formation of localized clusters of tightly linked, diverged loci. \"Genome hitchhiking\" (GH) theory emphasizes genome-wide effects of divergent selection. Past theoretical investigations of DH and GH focused on static snapshots of divergence. Here, we used simulations assessing a variety of strengths of selection, migration rates, population sizes, and mutation rates to investigate the relative importance of direct selection, GH, and DH in facilitating the dynamic buildup of genomic divergence as speciation proceeds through time. When divergently selected mutations were limiting, GH promoted divergence, but DH had little measurable effect. When populations were small and divergently selected mutations were common, DH enhanced the accumulation of weakly selected mutations, but this contributed little to reproductive isolation. In general, GH promoted reproductive isolation by reducing effective migration rates below that due to direct selection alone, and was important for genome-wide \"congealing\" or \"coupling\" of differentiation (F ST ) across loci as speciation progressed.

Conservation management is of increasing importance in ecology as most ecosystems nowadays are essentially managed ecosystems. Conservation managers work within a political-ecological system when they develop and attempt to implement a conservation plan that is designed to meet particular conservation goals. In this article, we develop a decision support tool that can identify a conservation policy for a managed wildlife population that is both sustainable and politically feasible. Part of our tool consists of a simulation model composed of interacting influence diagrams. We build, fit, and use our tool on the case of rhino horn trafficking between South Africa and Asia. Using these diagrams, we show how a rhino poacher’s belief system can be modified by such a policy and locate it in a perceived risks-benefits space before and after policy implementation. We statistically fit our model to observations on group actions and rhino abundance. We then use this fitted model to compute a politically feasible conservation policy.

1. Climate is known to influence breeding phenology and reproductive success in temperate-zone bats, but long-term population level studies and interspecific comparisons are rare. 2. Investigating the extent to which intrinsic (i.e. age), and extrinsic (i.e. spring weather conditions), factors influence such key demographic parameters as the proportion of females becoming pregnant, or completing lactation, each breeding season, is vital to understanding of bat population ecology and life-history traits. 3. Using data from 12 breeding seasons (2006-2017), encompassing the reproductive histories of 623 Myotis daubentonii and 436 Myotis nattereri adult females, we compare rates of recruitment to the breeding population and show that these species differ in their relative sensitivity to environmental conditions and climatic variation, affecting annual reproductive success at the population level. 4. We demonstrate that (1) spring weather conditions influence breeding phenology, with warm, dry and calm conditions leading to earlier parturition dates and advanced juvenile development, whilst cold, wet and windy weather delays birth timing and juvenile growth; (2) reproductive rates in first-year females are influenced by spring weather conditions in that breeding season and in the preceding breeding season when each cohort was born. Pregnancy and lactation rates were both higher when favourable spring foraging conditions were more prevalent; (3) reproductive success increases with age in both species, but at different rates; (4) reproductive rates were consistently higher, and showed less interannual variation, in second-year and older M. daubentonii (mean 91.55% ± 0.05 SD) than M. nattereri (mean 72.74% ± 0.15 SD); (5) estimates of reproductive success at the population level were highly correlated with the size of the juvenile cohort recorded each breeding season. 5. Improving understanding of the influence of environmental conditions, especially extreme climatic fluctuations, and the identification of critical periods (i.e. spring for reproductive female bats in temperate zones), which have disproportionate and lasting impacts on breeding phenology and reproductive success at a population level, is critical for improving predictions of the likely impact of climate change on bat populations.