On the sensitivity of food webs to multiple stressors.

Evaluating the effects of multiple stressors on ecosystems is becoming increasingly vital with global changes. The role of species interactions in propagating the effects of stressors, although widely acknowledged, has yet to be formally explored. Here, we conceptualise how stressors propagate through food webs and explore how they affect simulated three-species motifs and food webs of the Canadian St. Lawrence System. We find that overlooking species interactions invariably underestimate the effects of stressors, and that synergistic and antagonistic effects through food webs are prevalent. We also find that interaction type influences a species' susceptibility to stressors; species in omnivory and tri-trophic food chain interactions in particular are sensitive and prone to synergistic and antagonistic effects. Finally, we find that apex predators were negatively affected and mesopredators benefited from the effects of stressors due to their trophic position in the St. Lawrence System, but that species sensitivity is dependent on food web structure. In conceptualising the effects of multiple stressors on food webs, we bring theory closer to practice and show that considering the intricacies of ecological communities is key to assess the net effects of stressors on species.

Landscape modification and nutrient-driven instability at a distance.

Almost 50 years ago, Michael Rosenzweig pointed out that nutrient addition can destabilise food webs, leading to loss of species and reduced ecosystem function through the paradox of enrichment. Around the same time, David Tilman demonstrated that increased nutrient loading would also be expected to cause competitive exclusion leading to deleterious changes in food web diversity. While both concepts have greatly illuminated general diversity-stability theory, we currently lack a coherent framework to predict how nutrients influence food web stability across a landscape. This is a vitally important gap in our understanding, given mounting evidence of serious ecological disruption arising from anthropogenic displacement of resources and organisms. Here, we combine contemporary theory on food webs and meta-ecosystems to show that nutrient additions are indeed expected to drive loss in stability and function in human-impacted regions. Our models suggest that destabilisation is more likely to be caused by the complete loss of an equilibrium due to edible plant species being competitively excluded. In highly modified landscapes, spatial nutrient transport theory suggests that such instabilities can be amplified over vast distances from the sites of nutrient addition. Consistent with this theoretical synthesis, the empirical frequency of these distant propagating ecosystem imbalances appears to be growing. This synthesis of theory and empirical data suggests that human modification of the Earth is strongly connecting distantly separated ecosystems, causing rapid, expansive and costly nutrient-driven instabilities over vast areas of the planet. Similar to existing food web theory, the corollary to this spatial nutrient theory is that slowing down spatial nutrient pathways can be a potent means of stabilising degraded ecosystems.

Co-occurrence is not evidence of ecological interactions.

There is a rich amount of information in co-occurrence (presence-absence) data that could be used to understand community assembly. This proposition first envisioned by Forbes (1907) and then Diamond (1975) prompted the development of numerous modelling approaches (e.g. null model analysis, co-occurrence networks and, more recently, joint species distribution models). Both theory and experimental evidence support the idea that ecological interactions may affect co-occurrence, but it remains unclear to what extent the signal of interaction can be captured in observational data. It is now time to step back from the statistical developments and critically assess whether co-occurrence data are really a proxy for ecological interactions. In this paper, we present a series of arguments based on probability, sampling, food web and coexistence theories supporting that significant spatial associations between species (or lack thereof) is a poor proxy for ecological interactions. We discuss appropriate interpretations of co-occurrence, along with potential avenues to extract as much information as possible from such data.

Homogenization of freshwater lakes: Recent compositional shifts in fish communities are explained by gamefish movement and not climate change.

Globally, lake fish communities are being subjected to a range of scale-dependent anthropogenic pressures, from climate change to eutrophication, and from overexploitation to species introductions. As a consequence, the composition of these communities is being reshuffled, in most cases leading to a surge in taxonomic similarity at the regional scale termed homogenization. The drivers of homogenization remain unclear, which may be a reflection of interactions between various environmental changes. In this study, we investigate two potential drivers of the recent changes in the composition of freshwater fish communities: recreational fishing and climate change. Our results, derived from 524 lakes of Ontario, Canada sampled in two periods (1965-1982 and 2008-2012), demonstrate that the main contributors to homogenization are the dispersal of gamefish species, most of which are large predators. Alternative explanations relating to lake habitat (e.g., area, phosphorus) or variations in climate have limited explanatory power. Our analysis suggests that human-assisted migration is the primary driver of the observed compositional shifts, homogenizing freshwater fish community among Ontario lakes and generating food webs dominated by gamefish species.

Identifying key needs for the integration of social-ecological outcomes in arctic wildlife monitoring.

For effective monitoring in social-ecological systems to meet needs for biodiversity, science, and humans, desired outcomes must be clearly defined and routes from direct to derived outcomes understood. The Arctic is undergoing rapid climatic, ecological, social, and economic changes and requires effective wildlife monitoring to meet diverse stakeholder needs. To identify stakeholder priorities concerning desired outcomes of arctic wildlife monitoring, we conducted in-depth interviews with 29 arctic scientists, policy and decision makers, and representatives of indigenous organizations and nongovernmental organizations. Using qualitative content analysis, we identified and defined desired outcomes and documented links between outcomes. Using network analysis, we investigated the structure of perceived links between desired outcomes. We identified 18 desired outcomes from monitoring and classified them as either driven by monitoring information, monitoring process, or a combination of both. Highly cited outcomes were make decisions, conserve, detect change, disseminate, and secure food. These reflect key foci of arctic monitoring. Infrequently cited outcomes (e.g., govern) were emerging themes. Three modules comprised our outcome network. The modularity highlighted the low strength of perceived links between outcomes that were primarily information driven or more derived (e.g., detect change, make decisions, conserve, or secure food) and outcomes that were primarily process driven or more derived (e.g., cooperate, learn, educate). The outcomes expand monitoring community and disseminate created connections between these modules. Key desired outcomes are widely applicable to social-ecological systems within and outside the Arctic, particularly those with wildlife subsistence economies. Attributes and motivations associated with outcomes can guide development of integrated monitoring goals for biodiversity conservation and human needs. Our results demonstrated the disconnect between information- and process-driven goals and how expansion of the monitoring community and improved integration of monitoring stakeholders will help connect information- and process-derived outcomes for effective ecosystem stewardship.

Identificación de las Necesidades Clave para la Integración de Resultados Socio-Ecológicos en el Monitoreo de Fauna en el Ártico Resumen Para que el monitoreo efectivo en los sistemas socio-ecológicos cumpla con las necesidades de la biodiversidad, la ciencia, y los humanos, se deben definir claramente los resultados deseados y se deben entender las rutas que se toman de los resultados directos hacia los resultados derivados. El Ártico está sufriendo rápidamente cambios climáticos, ecológicos, y económicos, y requiere de un monitoreo efectivo de fauna para cumplir con las necesidades de diversos accionistas. Realizamos entrevistas a profundidad con 29 científicos del Ártico, responsables de decisiones y políticas, y representativos de organizaciones indígenas y organizaciones no gubernamentales para identificar las prioridades de los accionistas con respecto a los resultados deseados del monitoreo de fauna ártica. Mediante un análisis cualitativo de contenido identificamos y definimos los resultados deseados y documentamos las conexiones entre los resultados. Con un análisis de redes investigamos la estructura de las conexiones percibidas y las clasificamos como causadas por el monitoreo de información, el monitoreo del proceso, o una combinación de ambos. Los resultados con un mayor número de menciones fueron tomar decisiones, conservar, detectar cambios, diseminar, y asegurar alimento. Estos reflejan los enfoques más importantes del monitoreo en el Ártico. Los resultados con poca frecuencia en las menciones (p. ej.: regular) correspondían a temas emergentes. Nuestra red de resultados estuvo compuesta por tres módulos. La modularidad resaltó la poca fuerza de las conexiones percibidas entre los resultados que fueron causados principalmente por la información o que estuvieron más derivados (p. ej.: detectar el cambio, tomar decisiones, conservar o asegurar alimento) y los resultados que fueron causados principalmente por el proceso o que estuvieron más derivados (p. ej.: cooperar, aprender, educar). Los resultados expanden la comunidad monitora y diseminan las conexiones creadas entre estos módulos. Los resultados clave deseados se pueden aplicar extensamente a los sistemas socio-ecológicos dentro y fuera del Ártico, particularmente aquellos con economías de sustento basadas en la fauna. Los atributos y motivaciones asociados con los resultados pueden guiar el desarrollo de los objetivos integrados de monitoreo para la conservación de la biodiversidad y las necesidades humanas. Nuestros resultados demostraron la desconexión entre los objetivos conducidos por la información y aquellos conducidos por el proceso y cómo la expansión de la comunidad monitora y una mejor integración de los accionistas monitores ayudarán a conectar los resultados derivados de la información y derivados del proceso para una administración efectiva del ecosistema.

Food-web structure of willow-galling sawflies and their natural enemies across Europe.

Communities consist of species and their interactions. They can thus be described as networks, with species as nodes and interactions as links. Within such networks, the diversity of nodes and the distribution of links may affect patterns of energy transfer between trophic levels, the dynamics of the system, and the outcome in terms of ecosystem functioning. To date, most descriptions of networks have focused on single or relatively few sites, and have oftentimes been built on poorly resolved nodes and links. Yet, comparisons of local interaction networks reveal variation in space and in time, thus spurring interest in methods and theory for understanding patterns, drivers, and consequences of this variation. Progress in this field relies on access to replicate samples of comparable food webs across large spatiotemporal scales, resolved to species rather than to compound nodes. Due to the massive efforts required, high-quality data sets are still scarce. We created a data set on a single community type sampled across Europe: willow species (Salix), willow-galling sawflies (Hymenoptera: Tenthredinidae: Nematinae: Euurina), and their natural enemies (hymenopteran parasitoids and coleopteran, lepidopteran, dipteran, and hymenopteran inquilines). Each sample was referenced in space and time, and each node resolved with the highest possible resolution, including taxonomic affinity, gall type (for herbivores), and mode of parasitism (for natural enemies). Galler survival and link structure were resolved by dissection and rearing of gall inhabitants. In total, the data set is based on 641 site visits over 29 years, and on 165,424 galls representing 96 herbivore nodes and 52 plant nodes. The dissections and rearings yielded 42,129 natural enemies belonging to 126 species, and revealed 1,173 different links. The spatiotemporal and taxonomic resolution of these data make them amenable to analyses of both ecological and evolutionary processes of network assembly. Thus, this data set will facilitate testing of important hypotheses in recent community theory, concerning, e.g., the sampling effort needed to adequately describe interaction structure within ecological communities, the impact of environmental conditions and biotic filters on the distribution of species and their interactions, and the relationship between the global "metaweb" and its local realizations.

Disentangling local and global climate drivers in the population dynamics of mosquito-borne infections.

Identifying climate drivers is essential to understand and predict epidemics of mosquito-borne infections whose population dynamics typically exhibit seasonality and multiannual cycles. Which climate covariates to consider varies across studies, from local factors such as temperature to remote drivers such as the El Niño-Southern Oscillation. With partial wavelet coherence, we present a systematic investigation of nonstationary associations between mosquito-borne disease incidence and a given climate factor while controlling for another. Analysis of almost 200 time series of dengue and malaria around the globe at different geographical scales shows a systematic effect of global climate drivers on interannual variability and of local ones on seasonality. This clear separation of time scales of action enhances detection of climate drivers and indicates those best suited for building early-warning systems.

Ecological network complexity scales with area.

Larger geographical areas contain more species-an observation raised to a law in ecology. Less explored is whether biodiversity changes are accompanied by a modification of interaction networks. We use data from 32 spatial interaction networks from different ecosystems to analyse how network structure changes with area. We find that basic community structure descriptors (number of species, links and links per species) increase with area following a power law. Yet, the distribution of links per species varies little with area, indicating that the fundamental organization of interactions within networks is conserved. Our null model analyses suggest that the spatial scaling of network structure is determined by factors beyond species richness and the number of links. We demonstrate that biodiversity-area relationships can be extended from species counts to higher levels of network complexity. Therefore, the consequences of anthropogenic habitat destruction may extend from species loss to wider simplification of natural communities.

Spatial Fingerprinting: Horizontal Fusion of Multi-Dimensional Bio-Tracers as Solution to Global Food Provenance Problems.

Building the capacity of efficiently determining the provenance of food products represents a crucial step towards the sustainability of the global food system. Despite species specific empirical examples of multi-tracer approaches to provenance, the precise benefit and efficacy of multi-tracers remains poorly understood. Here we show why, and when, data fusion of bio-tracers is an extremely powerful technique for geographical provenance discrimination. Specifically, we show using extensive simulations how, and under what conditions, geographical relationships between bio-tracers (e.g., spatial covariance) can act like a spatial fingerprint, in many naturally occurring applications likely allowing rapid identification with limited data. To highlight the theory, we outline several statistic methodologies, including artificial intelligence, and apply these methodologies as a proof of concept to a limited data set of 90 individuals of highly mobile Sockeye salmon that originate from 3 different areas. Using 17 measured bio-tracers, we demonstrate that increasing combined bio-tracers results in stronger discriminatory power. We argue such applications likely even work for such highly mobile and critical fisheries as tuna.

Food web rewiring in a changing world.

Climate change is asymmetrically altering environmental conditions in space, from local to global scales, creating novel heterogeneity. Here, we argue that this novel heterogeneity will drive mobile generalist consumer species to rapidly respond through their behaviour in ways that broadly and predictably reorganize - or rewire - food webs. We use existing theory and data from diverse ecosystems to show that the rapid behavioural responses of generalists to climate change rewire food webs in two distinct and critical ways. First, mobile generalist species are redistributing into systems where they were previously absent and foraging on new prey, resulting in topological rewiring - a change in the patterning of food webs due to the addition or loss of connections. Second, mobile generalist species, which navigate between habitats and ecosystems to forage, will shift their relative use of differentially altered habitats and ecosystems, causing interaction strength rewiring - changes that reroute energy and carbon flows through existing food web connections and alter the food web's interaction strengths. We then show that many species with shared traits can exhibit unified aggregate behavioural responses to climate change, which may allow us to understand the rewiring of whole food webs. We end by arguing that generalists' responses present a powerful and underutilized approach to understanding and predicting the consequences of climate change and may serve as much-needed early warning signals for monitoring the looming impacts of global climate change on entire ecosystems.

The marine fish food web is globally connected.

The productivity of marine ecosystems and the services they provide to humans are largely dependent on complex interactions between prey and predators. These are embedded in a diverse network of trophic interactions, resulting in a cascade of events following perturbations such as species extinction. The sheer scale of oceans, however, precludes the characterization of marine feeding networks through de novo sampling. This effort ought instead to rely on a combination of extensive data and inference. Here we investigate how the distribution of trophic interactions at the global scale shapes the marine fish food web structure. We hypothesize that the heterogeneous distribution of species ranges in biogeographic regions should concentrate interactions in the warmest areas and within species groups. We find that the inferred global metaweb of marine fish-that is, all possible potential feeding links between co-occurring species-is highly connected geographically with a low degree of spatial modularity. Metrics of network structure correlate with sea surface temperature and tend to peak towards the tropics. In contrast to open-water communities, coastal food webs have greater interaction redundancy, which may confer robustness to species extinction. Our results suggest that marine ecosystems are connected yet display some resistance to perturbations because of high robustness at most locations.

The spatial scaling of species interaction networks.

Species-area relationships (SARs) are pivotal to understand the distribution of biodiversity across spatial scales. We know little, however, about how the network of biotic interactions in which biodiversity is embedded changes with spatial extent. Here we develop a new theoretical framework that enables us to explore how different assembly mechanisms and theoretical models affect multiple properties of ecological networks across space. We present a number of testable predictions on network-area relationships (NARs) for multi-trophic communities. Network structure changes as area increases because of the existence of different SARs across trophic levels, the preferential selection of generalist species at small spatial extents and the effect of dispersal limitation promoting beta-diversity. Developing an understanding of NARs will complement the growing body of knowledge on SARs with potential applications in conservation ecology. Specifically, combined with further empirical evidence, NARs can generate predictions of potential effects on ecological communities of habitat loss and fragmentation in a changing world.

Context-dependent interactions and the regulation of species richness in freshwater fish.

Species richness is regulated by a complex network of scale-dependent processes. This complexity can obscure the influence of limiting species interactions, making it difficult to determine if abiotic or biotic drivers are more predominant regulators of richness. Using integrative modeling of freshwater fish richness from 721 lakes along an 11o latitudinal gradient, we find negative interactions to be a relatively minor independent predictor of species richness in lakes despite the widespread presence of predators. Instead, interaction effects, when detectable among major functional groups and 231 species pairs, were strong, often positive, but contextually dependent on environment. These results are consistent with the idea that negative interactions internally structure lake communities but do not consistently 'scale-up' to regulate richness independently of the environment. The importance of environment for interaction outcomes and its role in the regulation of species richness highlights the potential sensitivity of fish communities to the environmental changes affecting lakes globally.

Wavelet analysis in ecology and epidemiology: impact of statistical tests.

Wavelet analysis is now frequently used to extract information from ecological and epidemiological time series. Statistical hypothesis tests are conducted on associated wavelet quantities to assess the likelihood that they are due to a random process. Such random processes represent null models and are generally based on synthetic data that share some statistical characteristics with the original time series. This allows the comparison of null statistics with those obtained from original time series. When creating synthetic datasets, different techniques of resampling result in different characteristics shared by the synthetic time series. Therefore, it becomes crucial to consider the impact of the resampling method on the results. We have addressed this point by comparing seven different statistical testing methods applied with different real and simulated data. Our results show that statistical assessment of periodic patterns is strongly affected by the choice of the resampling method, so two different resampling techniques could lead to two different conclusions about the same time series. Moreover, our results clearly show the inadequacy of resampling series generated by white noise and red noise that are nevertheless the methods currently used in the wide majority of wavelets applications. Our results highlight that the characteristics of a time series, namely its Fourier spectrum and autocorrelation, are important to consider when choosing the resampling technique. Results suggest that data-driven resampling methods should be used such as the hidden Markov model algorithm and the 'beta-surrogate' method.