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Social-ecological networks (SENs) represent the complex relationships between ecological and social systems and are a useful tool for analyzing and managing ecosystem services. However, mainstreaming the application of SENs in ecosystem service research has been hindered by a lack of clarity about how to match research questions to ecosystem service conceptualizations in SEN (i.e., as nodes, links, attributes, or emergent properties). Building from different disciplines, we propose a typology to represent ecosystem service in SENs and identify opportunities and challenges of using SENs in ecosystem service research. Our typology provides guidance for this growing field to improve research design and increase the breadth of questions that can be addressed with SEN to understand human-nature interdependencies in a changing world.

This document summarizes the open community standards developed by the Ecological Forecasting Initiative (EFI) for the common formatting and archiving of ecological forecasts and the metadata associated with these forecasts. Such open standards are intended to promote interoperability and facilitate forecast adoption, distribution, validation, and synthesis. For output files EFI has adopted a three-tiered approach reflecting trade-offs in forecast data volume and technical expertise. The preferred output file format is netCDF following the Climate and Forecast Convention for dimensions and variable naming, including an ensemble dimension where appropriate. The second-tier option is a semi-long CSV format, with state variables as columns and each row representing a unique issue datetime, prediction datetime, location, ensemble member, etc. The third-tier option is similar to option 2, but each row represents a specific summary statistic (mean, upper/lower CI) rather than individual ensemble members. For metadata, EFI expands upon the Ecological Metadata Language (EML), using additionalMetadata tags to store information designed to facilitate cross-forecast synthesis (e.g. uncertainty propagation, data assimilation, model complexity) and setting a subset of base EML tags (e.g. temporal resolution, output variables) to be required. To facilitate community adoption we also provides a R package containing a number of vignettes on how to both write and read in the EFI standard, as well as a metadata validator tool.

Complex socio-environmental interdependencies drive biological invasions, causing damages across large spatial scales. For widespread invasions, targeting of management activities based on optimization approaches may fail due to computational or data constraints. Here we evaluate an alternative approach that embraces complexity by representing the invasion as a network and using network structure to inform management locations. We compare optimal versus network-guided invasive species management at a landscape-scale, considering siting of boat decontamination stations targeting 1.6 million boater movements among 9,182 lakes in Minnesota, USA. Studying performance for 58 counties, we find that when full information is known on invasion status and boater movements, the best-performing network-guided metric achieves a median and lower quartile performance of 100% of optimal. We also find that performance remains relatively high using different network metrics or with less information (median above 80% and lower quartile above 60% of optimal for most metrics), but is more variable, particularly at the lower quartile. Additionally, performance is generally stable across counties with varying lake counts, suggesting viability for large-scale invasion management., Comment: 40 pages, 8 figures, 7 tables

'Evolutionary rescue' is the potential for evolution to enable population persistence in a changing environment. Even with eventual rescue, evolutionary time lags can cause the population size to temporarily fall below a threshold susceptible to extinction. To reduce extinction risk given human-driven global change, conservation management can enhance populations through actions such as captive breeding. To quantify the optimal timing of, and indicators for engaging in, investment in temporary enhancement to enable evolutionary rescue, we construct a model of coupled demographic-genetic dynamics given a moving optimum. We assume 'decelerating change', as might be relevant to climate change, where the rate of environmental change initially exceeds a rate where evolutionary rescue is possible, but eventually slows. We analyze the optimal control path of an intervention to avoid the population size falling below a threshold susceptible to extinction, minimizing costs. We find that the optimal path of intervention initially increases as the population declines, then declines and ceases when the population growth rate becomes positive, which lags the stabilization in environmental change. In other words, the optimal strategy involves increasing investment even in the face of a declining population, and positive population growth could serve as a signal to end the intervention. In addition, a greater carrying capacity relative to the initial population size decreases the optimal intervention. Therefore, a one-time action to increase carrying capacity, such as habitat restoration, can reduce the amount and duration of longer-term investment in population enhancement, even if the population is initially lower than and declining away from the new carrying capacity.

In this paper, we study the general problem of extracting information from spatially explicit genomic data to inform inference of ecologically and geographically realistic population models. We describe methods and apply them to simulations motivated by the demography of the Mojave desert tortoise (Gopherus agassizii). The tortoise is an example of a long-lived, threatened species for which we have an excellent understanding of range, habitat preference, and certain aspects of demography, but inadequate information on other life history components that are important for conservation management. We use an individual-based model on a discretized geographic landscape with overlapping generations and age and sex-specific dispersal, fecundity, and mortality to develop and test a method that uses genomic data to infer demographic parameters. We do this by seeking parameters that best match a set of spatial statistics of genomes, which we introduce and discuss. We find that for inferring only overall population density and mean migration distance, a simple statistical learning method performs well using simulated training data, inferring parameters to within 10% accuracy. In the process, we introduce spatial analogues of common population genetics statistics, and discuss how and why they are expected to contain signal about the geography of population dynamics that are key for ecological modeling generally and conservation of endangered taxa.

The potential for extirpation of extremely small populations (ESPs) is high due to their vulnerability to demographic and environmental stochasticity and negative impacts of human activity. We argue that conservation actions that could aid ESPs are sometimes delayed because of a fear of failure. In human psychology, the fear of failure is composed of several distinct cognitive elements, including “uncertainty about the future” and “upsetting important others.” Uncertainty about the future is often driven by information obstacles in conservation: information is either not easily shared among practitioners or information is lacking. Whereas, fear of upsetting important others can be due to apprehension about angering constituents, peers, funders, and other stakeholders. We present several ways to address these fears in hopes of improving the conservation process. We describe methods for increased information sharing and improved decision-making in the face of uncertainty, and recommend a shift in focus to cooperative actions and improving methods for evaluating success. Our hope is that by tackling stumbling blocks due to the apprehension of failure, conservation and management organizations can take steps to move from fear to action.

The exchange of native pathogens between wild and domesticated animals can lead to novel disease threats to wildlife. However, the dynamics of wild host-parasite systems exposed to a reservoir of domesticated hosts are not well understood. A simple mathematical model reveals that the spill-back of native parasites from domestic to wild hosts may cause a demographic Allee effect in the wild host population. A second model is tailored to the particulars of pink salmon (Oncorhynchus gorbuscha) and salmon lice (Lepeophtheirus salmonis), for which parasite spill-back is a conservation and fishery concern. In both models, parasite spill-back weakens the coupling of parasite and wild host abundance-particularly at low host abundance-causing parasites per host to increase as a wild host population declines. These findings show that parasites shared across host populations have effects analogous to those of generalist predators and can similarly cause an unstable equilibrium in a focal host population that separates persistence and extirpation. Allee effects in wildlife arising from parasite spill-back are likely to be most pronounced in systems where the magnitude of transmission from domestic to wild host populations is high because of high parasite abundance in domestic hosts, prolonged sympatry of domestic and wild hosts, a high transmission coefficient for parasites, long-lived parasite larvae, and proximity of domesticated populations to wildlife migration corridors.

© 2016 The Author(s) Published by the Royal Society. All rights reserved. Phenotypic plasticity and its evolution may help evolutionary rescue in a novel and stressful environment, especially if environmental novelty reveals cryptic genetic variation that enables the evolution of increased plasticity. However, the environmental stochasticity ubiquitous in natural systems may alter these predictions, because high plasticity may amplify phenotype-environment mismatches. Although previous studies have highlighted this potential detrimental effect of plasticity in stochastic environments, they have not investigated how it affects extinction risk in the context of evolutionary rescue and with evolving plasticity. We investigate this question here by integrating stochastic demography with quantitative genetic theory in a model with simultaneous change in the mean and predictability (temporal autocorrelation) of the environment. We develop an approximate prediction of long-term persistence under the new pattern of environmental fluctuations, and compare it with numerical simulations for short- and long-term extinction risk. We find that reduced predictability increases extinction risk and reduces persistence because it increases stochastic load during rescue. This understanding of howstochastic demography, phenotypic plasticity, and evolution interact when evolution acts on cryptic genetic variation revealed in a novel environment can inform expectations for invasions, extinctions, or the emergence of chemical resistance in pests.

Exchange of diseases between domesticated and wild animals is a rising concern for conservation. In the ocean, many species display life histories that separate juveniles from adults. For pink salmon (Oncorhynchus gorbuscha) and parasitic sea lice (Lepeophtheirus salmonis), infection of juvenile salmon in early marine life occurs near salmon sea-cage aquaculture sites and is associated with declining abundance of wild salmon. Here, we develop a theoretical model for the pink salmon/sea lice host–parasite system and use it to explore the effects of aquaculture hosts, acting as reservoirs, on dynamics. Because pink salmon have a 2-year lifespan, even- and odd-year lineages breed in alternate years in a given river. These lineages can have consistently different relative abundances, a phenomenon termed “line dominance”. These dominance relationships between host lineages serve as a useful probe for the dynamical effects of introducing aquaculture hosts into this host–parasite system. We demonstrate how parasite spillover (farm-to-wild transfer) and spillback (wild-to-farm transfer) with aquaculture hosts can either increase or decrease the line dominance in an affected wild population. The direction of the effect depends on the response of farms to wild-origin infection. If aquaculture parasites are managed to a constant abundance, independent of the intensity of infections from wild to farm, then line dominance increases. On the other hand, if wild-origin parasites on aquaculture hosts are proportionally controlled to their abundance then line dominance decreases.

Author(s): Chmura, HE; Kharouba, HM; Ashander, J; Ehlman, SM; Rivest, EB; Yang, LH | Abstract: Species across a wide range of taxa and habitats are shifting phenological events in response to climate change. While advances are common, shifts vary in magnitude and direction within and among species, and the basis for this variation is relatively unknown. We examine previously suggested patterns of variation in phenological shifts in order to understand the cue–response mechanisms that underlie phenological change. Here, we review what is known about the mechanistic basis for nine factors proposed to predict phenological change (latitude, elevation, habitat type, trophic level, migratory strategy, ecological specialization, species’ seasonality, thermoregulatory mode, and generation time). We find that many studies either do not identify a specific underlying mechanism or do not evaluate alternative mechanistic hypotheses, limiting the ability of scientists to predict future responses to global change with accuracy. We present a conceptual framework that emphasizes a critical distinction between environmental (cue-driven) and organismal (response-driven) mechanisms causing variation in phenological shifts and discuss how this distinction can reduce confusion in the field and improve predictions of future phenological change.

Rapid evolution can increase or maintain the provision of ecosystem services, motivating the conservation of wild species and communities. We detail one such contemporary evosystem service by synthesizing theoretical evidence that rapid evolution can sustain parasiticide efficacy in salmon aquaculture, thus creating an added incentive for salmon conservation. Globally, wild and farmed salmon share native parasites: sea lice. In most major salmon farming areas sea lice have evolved resistance to parasiticides, but in the North Pacific, where farmed salmon coexist with large wild salmon populations, resistance has not emerged. We present a model to show that flow of susceptible genes from lice hosted on wild salmon to those hosted on farmed salmon can delay or preclude resistance. This theoretical and observational data suggests that wild salmon (both oceanic populations that function as a refuge and local migratory populations that connect this refuge to domesticated environments) provide an evosystem service by prolonging parasiticide efficacy. To preserve this service, aquaculture managers could avoid production quantities that exceed wild salmon abundances, and sustain wild salmon populations through regional and oceanic scale conservation. The evosystem service of resistance mitigation is one example of how a contemporary evolutionary process that benefits people can strengthen the case for conservation of intrinsically important wild species.

Many natural populations exhibit temporal fluctuations in abundance that are consistent with external forcing by a randomly changing environment. As fitness emerges from an interaction between the phenotype and the environment, such demographic fluctuations probably include a substantial contribution from fluctuating phenotypic selection. We study the stochastic population dynamics of a population exposed to random (plus possibly directional) changes in the optimum phenotype for a quantitative trait that evolves in response to this moving optimum. We derive simple analytical predictions for the distribution of log-population size over time, both transiently and at stationarity under Gompertz density regulation. These predictions are well matched by population- and individual-based simulations. The log-population size is approximately reverse gamma distributed, with a negative skew causing an excess of low relative to high population sizes, thus increasing extinction risk relative to a symmetric (e.g. normal) distribution with the same mean and variance. Our analysis reveals how the mean and variance of log population size change with the variance and autocorrelation of deviations of the evolving mean phenotype from the optimum. We apply our results to the analysis of evolutionary rescue in a stochastic environment, and show that random fluctuations in the optimum can substantially increase extinction risk, by both reducing the expected growth rate and increasing the variance of population size by several orders of magnitude.