Accumulating evidence in ecology: Once is not enough

Population and community ecology: past progress and future directions

Population and community ecology as a science are about 100 years old, and we discuss here our opinion of what approaches have progressed well and which point to possible future directions. The three major threads within population and community ecology are theoretical ecology, statistical tests and models, and experimental ecology. We suggest that our major objective is to understand what factors determine the distribution and abundance of organisms within populations and communities, and we evaluate these threads against this major objective. Theoretical ecology is elegant and compelling and has laid the groundwork for achieving our overall objectives with useful simple models. Statistics and statistical models have contributed informative methods to analyze quantitatively our understanding of distribution and abundance for future research. Population ecology is difficult to carry out in the field, even though we may have all the statistical methods and models needed to achieve results. Community ecology is growing rapidly with much description but less understanding of why changes occur. Biodiversity science cuts across all these subdivisions but rarely digs into the necessary population and community science that might solve conservation problems. Climate change affects all aspects of ecology but to assume that everything in population and community ecology is driven by climate change is oversimplified. We make recommendations on how to advance the field with advice for present and future generations of population and community ecologists.

Assessment of temporal instability in the applied ecology and conservation evidence base

Outcomes of meta-analyses are increasingly used to inform evidence-based decision making in various research fields. However, a number of recent studies have reported rapid temporal changes in magnitude and significance of the reported effects which could make policy-relevant recommendations from meta-analyses to quickly go out of date. We assessed the extent and patterns of temporal trends in magnitude and statistical significance of the cumulative effects in meta-analyses in applied ecology and conservation published between 2004 and 2018. Of the 121 meta-analyses analysed, 93% showed a temporal trend in cumulative effect magnitude or significance with 27% of the datasets exhibiting temporal trends in both. The most common trend was the early study effect when at least one of the first 5 years effect size estimates exhibited more than 50% magnitude difference to the subsequent estimate. The observed temporal trends persisted in majority of datasets once moderators were accounted for. Only 5 datasets showed significant changes in sample size over time which could potentially explain the observed temporal change in the cumulative effects. Year of publication of meta-analysis had no significant effect on presence of temporal trends in cumulative effects. Our results show that temporal changes in magnitude and statistical significance in applied ecology are widespread and represent a serious potential threat to use of meta-analyses for decision-making in conservation and environmental management. We recommend use of cumulative meta-analyses and call for more studies exploring the causes of the temporal effects.

Are predictions of bovine tuberculosis-infected herds unbiased and precise?

Bovine tuberculosis (bTB) is prevalent among livestock and wildlife in many countries including New Zealand (NZ), a country which aims to eradicate bTB by 2055. This study evaluates predictions related to the numbers of livestock herds with bTB in NZ from 2012 to 2021 inclusive using both statistical and mechanistic (causal) modelling. Additionally, this study made predictions for the numbers of infected herds between 2022 and 2059. This study introduces a new graphical method representing the causal criteria of strength of association, such as R2, and the consistency of predictions, such as mean squared error. Mechanistic modelling predictions were, on average, more frequently (3 of 4) unbiased than statistical modelling predictions (1 of 4). Additionally, power model predictions were, on average, more frequently (3 of 4) unbiased than exponential model predictions (1 of 4). The mechanistic power model, along with annual updating, had the highest R2 and the lowest mean squared error of predictions. It also exhibited the closest approximation to unbiased predictions. Notably, significantly biased predictions were all underestimates. Based on the mechanistic power model, the biological eradication of bTB from New Zealand is predicted to occur after 2055. Disease eradication planning will benefit from annual updating of future predictions.

Empirical evidence of widespread exaggeration bias and selective reporting in ecology

In many scientific disciplines, common research practices have led to unreliable and exaggerated evidence about scientific phenomena. Here we describe some of these practices and quantify their pervasiveness in recent ecology publications in five popular journals. In an analysis of over 350 studies published between 2018 and 2020, we detect empirical evidence of exaggeration bias and selective reporting of statistically significant results. This evidence implies that the published effect sizes in ecology journals exaggerate the importance of the ecological relationships that they aim to quantify. An exaggerated evidence base hinders the ability of empirical ecology to reliably contribute to science, policy, and management. To increase the credibility of ecology research, we describe a set of actions that ecologists should take, including changes to scientific norms about what high-quality ecology looks like and expectations about what high-quality studies can deliver.

Models of underlying autotrophic biomass dynamics fit to daily river ecosystem productivity estimates improve understanding of ecosystem disturbance and resilience

Directly observing autotrophic biomass at ecologically relevant frequencies is difficult in many ecosystems, hampering our ability to predict productivity through time. Since disturbances can impart distinct reductions in river productivity through time by modifying underlying standing stocks of biomass, mechanistic models fit to productivity time series can infer underlying biomass dynamics. We incorporated biomass dynamics into a river ecosystem productivity model for six rivers to identify disturbance flow thresholds and understand the resilience of primary producers. The magnitude of flood necessary to disturb biomass and thereby reduce ecosystem productivity was consistently lower than the more commonly used disturbance flow threshold of the flood magnitude necessary to mobilize river bed sediment. The estimated daily maximum percent increase in biomass (a proxy for resilience) ranged from 5% to 42% across rivers. Our latent biomass model improves understanding of disturbance thresholds and recovery patterns of autotrophic biomass within river ecosystems.

Rapid prototyping for quantifying belief weights of competing hypotheses about emergent diseases

Emerging diseases can have devastating consequences for wildlife and require a rapid response. A critical first step towards developing appropriate management is identifying the etiology of the disease, which can be difficult to determine, particularly early in emergence. Gathering and synthesizing existing information about potential disease causes, by leveraging expert knowledge or relevant existing studies, provides a principled approach to quickly inform decision-making and management efforts. Additionally, updating the current state of knowledge as more information becomes available over time can reduce scientific uncertainty and lead to substantial improvement in the decision-making process and the application of management actions that incorporate and adapt to newly acquired scientific understanding. Here we present a rapid prototyping method for quantifying belief weights for competing hypotheses about the etiology of disease using a combination of formal expert elicitation and Bayesian hierarchical modeling. We illustrate the application of this approach for investigating the etiology of stony coral tissue loss disease (SCTLD) and discuss the opportunities and challenges of this approach for addressing emergent diseases. Lastly, we detail how our work may apply to other pressing management or conservation problems that require quick responses. We found the rapid prototyping methods to be an efficient and rapid means to narrow down the number of potential hypotheses, synthesize current understanding, and help prioritize future studies and experiments. This approach is rapid by providing a snapshot assessment of the current state of knowledge. It can also be updated periodically (e.g., annually) to assess changes in belief weights over time as scientific understanding increases. Synthesis and applications: The rapid prototyping approaches demonstrated here can be used to combine knowledge from multiple experts and/or studies to help with fast decision-making needed for urgent conservation issues including emerging diseases and other management problems that require rapid responses. These approaches can also be used to adjust belief weights over time as studies and expert knowledge accumulate and can be a helpful tool for adapting management decisions.

Territory and population attributes affect Florida scrub-jay fecundity in fire-adapted ecosystems

Fecundity, the number of young produced by a breeding pair during a breeding season, is a primary component in evolutionary and ecological theory and applications. Fecundity can be influenced by many environmental factors and requires long-term study due to the range of variation in ecosystem dynamics. Fecundity data often include a large proportion of zeros when many pairs fail to produce any young during a breeding season due to nest failure or when all young die independently after fledging. We conducted color banding and monthly censuses of Florida scrub-jays (Aphelocoma coerulescens) across 31 years, 15 populations, and 761 territories along central Florida's Atlantic coast. We quantified how fecundity (juveniles/pair-year) was influenced by habitat quality, presence/absence of nonbreeders, population density, breeder experience, and rainfall, with a zero-inflated Bayesian hierarchical model including both a Bernoulli (e.g., brood success) and a Poisson (counts of young) submodel, and random effects for year, population, and territory. The results identified the importance of increasing "strong" quality habitat, which was a mid-successional state related to fire frequency and extent, because strong territories, and the proportion of strong territories in the overall population, influenced fecundity of breeding pairs. Populations subject to supplementary feeding also had greater fecundity. Territory size, population density, breeder experience, and rainfall surprisingly had no or small effects. Different mechanisms appeared to cause annual variation in fecundity, as estimates of random effects were not correlated between the success and count submodels. The increased fecundity for pairs with nonbreeders, compared to pairs without, identified empirical research needed to understand how the proportion of low-quality habitats influences population recovery and sustainability, because dispersal into low-quality habitats can drain nonbreeders from strong territories and decrease overall fecundity. We also describe how long-term study resulted in reversals in our understanding because of complications involving habitat quality, sociobiology, and population density.

Evaluation Options for Wildlife Management and Strengthening of Causal Inference

Wildlife management aims to halt and then reverse the decline of threatened species, to sustainably harvest populations, and to control undesirable impacts of some species. We describe a unifying framework of three feasible options for evaluation of wildlife management, including conservation, and discuss their relative strengths of statistical and causal inference. The first option is trends in abundance, which can provide strong evidence a change has occurred (statistical inference) but does not identify the causes. The second option assesses population outcomes relative to management efforts, which provides strong evidence of cause and effect (causal inference) but not the trend. The third option combines the first and second options and therefore provides both statistical and causal inferences in an adaptive framework. We propose that wildlife management needs to explicitly use causal criteria and inference to complement adaptive management. We recommend incorporating these options into management plans.

An evidence-based approach to assessing the effectiveness of training regimen on athlete performance: Youth soccer as a case study

Athletic performance data are modeled in an effort to better understand the relationship between both hours spent training and a measurement of “commitment” to that training, and improvements in performance. Both increased training time and greater commitment were predicted to produce larger increases in performance improvement, and commitment was predicted to be the more important determinant of improvement. The performance of 108 soccer players (ages 9–18) was quantified over a 10-week training program. Hours spent training ranged from 16 to 90 during the course of the study, while commitment scores ranged from 0.55 to 2.00, based on a scale from 0.00 to 2.40. A model selection approach was used to discriminate among models specifying relationships between training hours and improvement, and commitment and improvement. Despite considerable variability in the data, results provided strong evidence for an increase in performance improvement with both training hours and commitment score. The best models for hours and commitment were directly compared by computing an evidence ratio of 5799, indicating much stronger evidence favoring the model based on commitment. Results of analyses such as these go beyond anecdotal experience in an effort to establish a formal evidentiary basis for athletic training programs.

Phylogeny explains capture mortality of sharks and rays in pelagic longline fisheries: a global meta-analytic synthesis

Apex and mesopredators such as elasmobranchs are important for maintaining ocean health and are the focus of conservation efforts to mitigate exposure to fishing and other anthropogenic hazards. Quantifying fishing mortality components such as at-vessel mortality (AVM) is necessary for effective bycatch management. We assembled a database for 61 elasmobranch species and conducted a global meta-synthesis to estimate pelagic longline AVM rates. Evolutionary history was a significant predictor of AVM, accounting for up to 13% of variance in Bayesian phylogenetic meta-regression models for Lamniformes and Carcharhiniformes clades. Phylogenetically related species may have a high degree of shared traits that explain AVM. Model-estimated posterior mean AVM rates ranged from 5% (95% HDI 0.1%-16%) for pelagic stingrays and 76% (95% HDI 49%-90%) for salmon sharks. Measures that reduce catch, and hence AVM levels, such as input controls, bycatch quotas and gear technology to increase selectivity are appropriate for species with higher AVM rates. In addition to reducing catchability, handling-and-release practices and interventions such as retention bans in shark sanctuaries and bans on shark finning and trade hold promise for species with lower AVM rates. Robust, and where applicable, phylogenetically-adjusted elasmobranch AVM rates are essential for evidence-informed bycatch policy.

A hierarchical model for jointly assessing ecological and anthropogenic impacts on animal demography

The management of sustainable harvest of animal populations is of great ecological and conservation importance. Development of formal quantitative tools to estimate and mitigate the impacts of harvest on animal populations has positively impacted conservation efforts. The vast majority of existing harvest models, however, do not simultaneously estimate ecological and harvest impacts on demographic parameters and population trends. Given that the impacts of ecological drivers are often equal to or greater than the effects of harvest, and can covary with harvest, this disconnect has the potential to lead to flawed inference. In this study, we used Bayesian hierarchical models and a 43-year capture-mark-recovery dataset from 404,241 female mallards Anas platyrhynchos released in the North American midcontinent to estimate mallard demographic parameters. Furthermore, we model the dynamics of waterfowl hunters and habitat, and the direct and indirect effects of anthropogenic and ecological processes on mallard demographic parameters. We demonstrate that density dependence, habitat conditions and harvest can simultaneously impact demographic parameters of female mallards, and discuss implications for existing and future harvest management models. Our results demonstrate the importance of controlling for multicollinearity among demographic drivers in harvest management models, and provide evidence for multiple mechanisms that lead to partial compensation of mallard harvest. We provide a novel model structure to assess these relationships that may allow for improved inference and prediction in future iterations of harvest management models across taxa.

Why and how we should join the shift from significance testing to estimation

A paradigm shift away from null hypothesis significance testing seems in progress. Based on simulations, we illustrate some of the underlying motivations. First, p-values vary strongly from study to study, hence dichotomous inference using significance thresholds is usually unjustified. Second, 'statistically significant' results have overestimated effect sizes, a bias declining with increasing statistical power. Third, 'statistically non-significant' results have underestimated effect sizes, and this bias gets stronger with higher statistical power. Fourth, the tested statistical hypotheses usually lack biological justification and are often uninformative. Despite these problems, a screen of 48 papers from the 2020 volume of the Journal of Evolutionary Biology exemplifies that significance testing is still used almost universally in evolutionary biology. All screened studies tested default null hypotheses of zero effect with the default significance threshold of p = 0.05, none presented a pre-specified alternative hypothesis, pre-study power calculation and the probability of 'false negatives' (beta error rate). The results sections of the papers presented 49 significance tests on average (median 23, range 0-390). Of 41 studies that contained verbal descriptions of a 'statistically non-significant' result, 26 (63%) falsely claimed the absence of an effect. We conclude that studies in ecology and evolutionary biology are mostly exploratory and descriptive. We should thus shift from claiming to 'test' specific hypotheses statistically to describing and discussing many hypotheses (possible true effect sizes) that are most compatible with our data, given our statistical model. We already have the means for doing so, because we routinely present compatibility ('confidence') intervals covering these hypotheses.

Implementing "from here to there": A case study of conceptual and practical challenges in implementation science

There is a significant challenge in global health and development research that pivots on the difficulties of delivering (cost-)effective treatments or interventions that are scalable andtransferable across settings. That is, how does one deliver "true effects", proven treatments, into new settings? This is often addressed in pragmatic trials or implementation research in which one makes adjustments to the delivery of the treatment to ensure that it works here and there. In this critical analytical review, we argue that the approach mis-characterises the cause-effect relationship and fails to recognise the local, highly contextual nature of what it means to say an intervention "works". We use an ongoing randomised controlled trial (RCT)-an informal settlement redevelopment intervention in Indonesia and Fiji to reduce human exposure to pathogenic faecal contamination-as a vehicle for exploring the ideas and implications of identifying interventions that work in global health and development. We describe the highly contextualised features of the research and the challenges these would pose in attempts to generalise the results. In other words, we detail that which is frequently elided from most RCTs. As our critical lens, we us the work of American philosopher, Nancy Cartwright, who argued that research produces dappled regions of causal insights-lacunae against a backdrop of causal ignorance. Rather than learn about a relationship between a treatment and an outcome, we learn that in the right sort of context, a treatment reliably produces a particular outcome. Moving a treatment from here to there becomes, therefore, something of an engineering exercise to ensure the right factors (or "shields") are in place so the cause-effect is manifest. As a consequence, one cannot assume that comparative effectiveness or cost-effectiveness would be maintained.

A niche for null models in adaptive resource management

As global systems rapidly change, our collective ability to predict future ecological dynamics will become increasingly important for successful natural resource management. By merging stakeholder objectives with system uncertainty, and by adapting actions to changing systems and knowledge, adaptive resource management (ARM) provides a rigorous platform for making sound decisions in a changing world. Critically, however, applications of ARM could be improved by employing benchmarks (i.e., points of reference) for determining when learning is occurring through the cycle of monitoring, modeling, and decision-making steps in ARM. Many applications of ARM use multiple model-based hypotheses to identify and reduce systematic uncertainty over time, but generally lack benchmarks for gauging discovery of scientific evidence and learning. This creates the danger of thinking that directional changes in model weights or rankings are indicative of evidence for hypotheses, when possibly all competing models are inadequate. There is thus a somewhat obvious, but yet to be filled niche for including benchmarks for learning in ARM. We contend that carefully designed "ecological null models," which are structured to produce an expected ecological pattern in the absence of a hypothesized mechanism, can serve as suitable benchmarks. Using a classic case study of mallard harvest management that is often used to demonstrate the successes of ARM for learning about ecological mechanisms, we show that simple ecological null models, such as population persistence (Nt +1 = Nt ), provide more robust near-term forecasts of population abundance than the currently used mechanistic models. More broadly, ecological null models can be used as benchmarks for learning in ARM that trigger the need for discarding model parameterizations and developing new ones when prevailing models underperform the ecological null model. Identifying mechanistic models that surpass these benchmarks will improve learning through ARM and help decision-makers keep pace with a rapidly changing world.

Promoting co-existence between humans and venomous snakes through increasing the herpetological knowledge base

Snakebite incidence at least partly depends on the biology of the snakes involved. However, studies of snake biology have been largely neglected in favour of anthropic factors, with the exception of taxonomy, which has been recognised for some decades to affect the design of antivenoms. Despite this, within-species venom variation and the unpredictability of the correlation with antivenom cross-reactivity has continued to be problematic. Meanwhile, other aspects of snake biology, including behaviour, spatial ecology and activity patterns, distribution, and population demography, which can contribute to snakebite mitigation and prevention, remain underfunded and understudied. Here, we review the literature relevant to these aspects of snakebite and illustrate how demographic, spatial, and behavioural studies can improve our understanding of why snakebites occur and provide evidence for prevention strategies. We identify the large gaps that remain to be filled and urge that, in the future, data and relevant metadata be shared openly via public data repositories so that studies can be properly replicated and data used in future meta-analyses.

THE RELATIONSHIP BETWEEN HARVEST MANAGEMENT AND CHRONIC WASTING DISEASE PREVALENCE TRENDS IN WESTERN MULE DEER (ODOCOILEUS HEMIONUS) HERDS

We analyzed retrospective data on harvest management practices and corresponding chronic wasting disease (CWD) prevalence trends in 36 western US and Canadian mule deer (Odocoileus hemionus) management units (units). Our analyses employed logistic regression and model selection, exploiting variation in practices within and among jurisdictions to examine relationships between harvest management and apparent prevalence (the proportion of positive animals among those sampled). Despite notable differences in hunting practices among jurisdictions, our meta-analysis of combined data revealed strong evidence that the amount of harvest was related to CWD prevalence trends among adult male mule deer in the 32 units where prevalence at the start of the analysis period was ≤5%. All competitive models included the number of male deer harvested or number of hunters 1-2 yr prior as an explanatory variable, with increasing harvest leading to lower prevalence among males harvested in the following year. Competitive models also included harvest timing. Although less definitive than the number harvested, median harvest dates falling closer to breeding seasons were associated with lower prevalence in the following year. Our findings suggest harvest-when sufficient and sustained-can be an effective tool for attenuating CWD prevalence in adult male mule deer across western ranges, especially early in the course of an epidemic. Evidence of a broad relationship between the amount of harvest and subsequent changes in CWD prevalence among adult male mule deer provides an empirical basis for undertaking adaptive disease management experimentation aimed at suppressing or curtailing CWD epidemics.

Predicted distribution of a rare and understudied forest carnivore: Humboldt marten (Martes caurina humboldtensis)

BACKGROUND

Many mammalian species have experienced range contractions. Following a reduction in distribution that has resulted in apparently small and disjunct populations, the Humboldt marten (Martes caurina humboldtensis) was recently designated as federally Threatened and state Endangered. This subspecies of Pacific marten occurring in coastal Oregon and northern California, also known as coastal martens, appear unlike martens that occur in snow-associated regions in that vegetation associations appear to differ widely between Humboldt marten populations. We expected current distributions represent realized niches, but estimating factors associated with long-term occurrence was challenging for this rare and little-known species. Here, we assessed the predicted contemporary distribution of Humboldt martens and interpret our findings as hypotheses correlated with the subspecies' niche to inform strategic conservation actions.

METHODS

We modeled Humboldt marten distribution using a maximum entropy (Maxent) approach. We spatially-thinned 10,229 marten locations collected from 1996-2020 by applying a minimum distance of 500-m between locations, resulting in 384 locations used to assess correlations of marten occurrence with biotic and abiotic variables. We independently optimized the spatial scale of each variable and focused development of model variables on biotic associations (e.g., hypothesized relationships with forest conditions), given that abiotic factors such as precipitation are largely static and not alterable within a management context.

RESULTS

Humboldt marten locations were positively associated with increased shrub cover (salal (Gautheria shallon)), mast producing trees (e.g., tanoak, Notholithocarpus densiflorus), increased pine (Pinus sp.) proportion of total basal area, annual precipitation at home-range spatial scales, low and high amounts of canopy cover and slope, and cooler August temperatures. Unlike other recent literature, we found little evidence that Humboldt martens were associated with old-growth structural indices. This case study provides an example of how limited information on rare or lesser-known species can lead to differing interpretations, emphasizing the need for study-level replication in ecology. Humboldt marten conservation would benefit from continued survey effort to clarify range extent, population sizes, and fine-scale habitat use.

Invader removal triggers competitive release in a threatened avian predator

Invasive species can cause extinctions of native species and widespread biodiversity loss. Invader removal is a common management response, but the use of long-term field experiments to characterize effectiveness of removals in benefitting impacted native species is rare. We used a large-scale removal experiment to investigate the demographic response of a threatened native species, the northern spotted owl, to removal of an invasive competitor species, the barred owl. Removal of barred owls had a strong, positive effect on survival of spotted owls, which arrested long-term population declines of spotted owls. The results demonstrate that the long-term persistence of spotted owls will depend heavily on reducing the negative impacts of barred owls while simultaneously addressing other threats, such as habitat loss.

Changes in the distribution and abundance of invasive species can have far-reaching ecological consequences. Programs to control invaders are common but gauging the effectiveness of such programs using carefully controlled, large-scale field experiments is rare, especially at higher trophic levels. Experimental manipulations coupled with long-term demographic monitoring can reveal the mechanistic underpinnings of interspecific competition among apex predators and suggest mitigation options for invasive species. We used a large-scale before–after control–impact removal experiment to investigate the effects of an invasive competitor, the barred owl (Strix varia), on the population dynamics of an iconic old-forest native species, the northern spotted owl (Strix occidentalis caurina). Removal of barred owls had a strong, positive effect on survival of sympatric spotted owls and a weaker but positive effect on spotted owl dispersal and recruitment. After removals, the estimated mean annual rate of population change for spotted owls stabilized in areas with removals (0.2% decline per year), but continued to decline sharply in areas without removals (12.1% decline per year). The results demonstrated that the most substantial changes in population dynamics of northern spotted owls over the past two decades were associated with the invasion, population expansion, and subsequent removal of barred owls. Our study provides experimental evidence of the demographic consequences of competitive release, where a threatened avian predator was freed from restrictions imposed on its population dynamics with the removal of a competitively dominant invasive species.

Iterative model predictions for wildlife populations impacted by rapid climate change

To improve understanding and management of the consequences of current rapid environmental change, ecologists advocate using long-term monitoring data series to generate iterative near-term predictions of ecosystem responses. This approach allows scientific evidence to increase rapidly and management strategies to be tailored simultaneously. Iterative near-term forecasting may therefore be particularly useful for adaptive monitoring of ecosystems subjected to rapid climate change. Here, we show how to implement near-term forecasting in the case of a harvested population of rock ptarmigan in high-arctic Svalbard, a region subjected to the largest and most rapid climate change on Earth. We fitted state-space models to ptarmigan counts from point transect distance sampling during 2005-2019 and developed two types of predictions: (1) explanatory predictions to quantify the effect of potential drivers of ptarmigan population dynamics, and (2) anticipatory predictions to assess the ability of candidate models of increasing complexity to forecast next-year population density. Based on the explanatory predictions, we found that a recent increasing trend in the Svalbard rock ptarmigan population can be attributed to major changes in winter climate. Currently, a strong positive effect of increasing average winter temperature on ptarmigan population growth outweighs the negative impacts of other manifestations of climate change such as rain-on-snow events. Moreover, the ptarmigan population may compensate for current harvest levels. Based on the anticipatory predictions, the near-term forecasting ability of the models improved nonlinearly with the length of the time series, but yielded good forecasts even based on a short time series. The inclusion of ecological predictors improved forecasts of sharp changes in next-year population density, demonstrating the value of ecosystem-based monitoring. Overall, our study illustrates the power of integrating near-term forecasting in monitoring systems to aid understanding and management of wildlife populations exposed to rapid climate change. We provide recommendations for how to improve this approach.

Volatile and Semi-Volatile Organic Compounds May Help Reduce Pollinator-Prey Overlap in the Carnivorous Plant Drosophyllum lusitanicum (Drosophyllaceae)

Most carnivorous plants show a conspicuous separation between flowers and leaf-traps, which has been interpreted as an adaptive response to minimize pollinator-prey conflicts which will reduce fitness. Here, we used the carnivorous subshrub Drosophyllum lusitanicum (Drosophyllaceae) to explore if and how carnivorous plants with minimal physical separation of flower and trap avoid or reduce a likely conflict of pollinator and prey. We carried out an extensive field survey in the Aljibe Mountains, at the European side of the Strait of Gibraltar, of pollinating and prey insects of D. lusitanicum. We also performed a detailed analysis of flower and leaf volatile and semi-volatile organic compounds (VOCs and SVOCs, respectively) by direct thermal desorption-gas chromatography/mass spectrometry (TD-GC/MS) to ascertain whether this species shows different VOC/SVOC profiles in flowers and leaf-traps that might attract pollinators and prey, respectively. Our results show a low overlap between pollinator and prey groups as well as clear differences in the relative abundance of VOCs and SVOCs between flowers and leaf-traps. Coleopterans and hymenopterans were the most represented groups of floral visitors, whereas dipterans were the most diverse group of prey insects. Regarding VOCs and SVOCs, while aldehydes and carboxylic acids presented higher relative contents in leaf-traps, alkanes and plumbagin were the main VOC/SVOC compounds detected in flowers. We conclude that D. lusitanicum, despite its minimal flower-trap separation, does not seem to present a marked pollinator-prey conflict. Differences in the VOCs and SVOCs produced by flowers and leaf-traps may help explain the conspicuous differences between pollinator and prey guilds.

Assessing trends and vulnerabilities in the mutualism between whitebark pine (Pinus albicaulis) and Clark's nutcracker (Nucifraga columbiana) in national parks of the Sierra-Cascade region

Dispersal of whitebark pine (Pinus albicaulis Engelm.), a keystone species of many high-elevation ecosystems in western North America, depends on Clark’s nutcracker (Nucifraga columbiana Wilson), a seed-caching bird with an affinity for whitebark seeds. To the extent that this dependence is mutual, declines in whitebark seed production could cause declines in nutcracker abundance. Whitebark pine is in decline across much of its range due to interacting stressors, including the non-native pathogen white pine blister rust (Cronartium ribicola J. C. Fisch.). We used avian point-count data and tree surveys from four national park units to investigate whether trends in whitebark pine can explain trends in Clark’s nutcracker. Spatial trends were modeled using recent data from two parks, while temporal trends were modeled using longer time-series of nutcracker and whitebark data from two additional parks. To assess the potential dependence of nutcrackers on whitebark, we linked a model of nutcracker density (accounting for detection probability) with a model of whitebark trends, using a Bayesian framework to translate uncertainty in whitebark metrics to uncertainty in nutcracker density. In Mount Rainier National Park, temporal models showed dramatic declines in nutcracker density concurrent with significant increases in whitebark crown mortality and trees infected with white pine blister rust. However, nutcrackers did not trend with whitebark metrics in North Cascades National Park Service Complex. In spatial models of data from Yosemite National Park and Sequoia-Kings Canyon National Park, nutcracker density varied not only with local cover of whitebark but also with elevation and, in Sequoia-Kings Canyon, with cover of another species of white pine. Our results add support for the hypothesis that the mutualism between whitebark pine and Clark’s nutcracker is vulnerable to disruption by blister rust, and our approach integrates data across monitoring programs to explore trends in species interactions.

A modern method of multiple working hypotheses to improve inference in ecology

Science provides a method to learn about the relationships between observed patterns and the processes that generate them. However, inference can be confounded when an observed pattern cannot be clearly and wholly attributed to a hypothesized process. Over-reliance on traditional single-hypothesis methods (i.e. null hypothesis significance testing) has resulted in replication crises in several disciplines, and ecology exhibits features common to these fields (e.g. low-power study designs, questionable research practices, etc.). Considering multiple working hypotheses in combination with pre-data collection modelling can be an effective means to mitigate many of these problems. We present a framework for explicitly modelling systems in which relevant processes are commonly omitted, overlooked or not considered and provide a formal workflow for a pre-data collection analysis of multiple candidate hypotheses. We advocate for and suggest ways that pre-data collection modelling can be combined with consideration of multiple working hypotheses to improve the efficiency and accuracy of research in ecology.

Stress hypothesis overload: 131 hypotheses exploring the role of stress in tradeoffs, transitions, and health

Stress is ubiquitous and thus, not surprisingly, many hypotheses and models have been created to better study the role stress plays in life. Stress spans fields and is found in the literature of biology, psychology, psychophysiology, sociology, economics, and medicine, just to name a few. Stress, and the hypothalamic-pituitaryadrenal/interrenal (HPA/I) axis and sympathetic nervous system (SNS), are involved in a multitude of behaviors and physiological processes, including life-history and ecological tradeoffs, developmental transitions, health, and survival. The goal of this review is to highlight and summarize the large number of available hypotheses and models, to aid in comparative and interdisciplinary thinking, and to increase reproducibility by a) discouraging hypothesizing after results are known (HARKing) and b) encouraging a priori hypothesis testing. For this review I collected 214 published hypotheses or models dealing broadly with stress. In the main paper, I summarized and categorized 131 of those hypotheses and models which made direct connections among stress and/or HPA/I and SNS, tradeoffs, transitions, and health. Of those 131, the majority made predictions about reproduction (n = 43), the transition from health to disease (n = 38), development (n = 23), and stress coping (n = 18). Additional hypotheses were classified as stage-spanning or models (n = 37). The additional 83 hypotheses found during searches were tangentially related, or pertained to immune function or oxidative stress, and these are listed separately. Many of the hypotheses share underlying rationale and suggest similar, if not identical, predictions, and are thus not mutually exclusive; some hypotheses spanned classification categories. Some of the hypotheses have been tested multiple times, whereas others have only been examined a few times. It is the hope that multi-disciplinary stress researchers will begin to harmonize their naming of hypotheses in the literature so as to build a clearer picture of how stress impacts various outcomes across fields. The paper concludes with some considerations and recommendations for robust testing of stress hypotheses.