Controversy over the decline of arthropods: a matter of temporal baseline?

International audienceRecently, a number of studies have reported somewhat contradictory patterns of temporal trends in arthropod abundance, from decline to increase. Arthropods often exhibit non-monotonous variation in abundance over time, making it important to account for temporal coverage in interpretation of abundance trends, which is often overlooked in statistical analysis. Combining four recently analysed datasets that led to contrasting outcomes, we first show that temporal abundance variations of arthropods are non-monotonous. Using simulations, we show non-monotony is likely to bias estimated linear abundance trends. Finally, analysing empirical data, we show that heterogeneity in estimated abundance trends is significantly related to the variation in temporal baseline of analysed time series. Once differences in baseline years, habitats and continents are accounted for, we do not find any statistical difference in estimated linear abundance trends among the four datasets. We also show that short time series produce more stochastic abundance trends than long series, making the dearth of old and long-term time series a strong limitation in the assessment of temporal trends in arthropod abundance. The lack of time series with a baseline year before global change acceleration is likely to lead to an underestimation of global change effects on biodiversity

When cheating turns into a stabilizing mechanism of plant-pollinator communities.

Mutualistic interactions, such as plant-mycorrhizal or plant-pollinator interactions, are widespread in ecological communities and frequently exploited by cheaters, species that profit from interactions without providing benefits in return. Cheating usually negatively affects the fitness of the individuals that are cheated on, but the effects of cheating at the community level remains poorly understood. Here, we describe 2 different kinds of cheating in mutualistic networks and use a generalized Lotka-Volterra model to show that they have very different consequences for the persistence of the community. Conservative cheating, where a species cheats on its mutualistic partners to escape the cost of mutualistic interactions, negatively affects community persistence. In contrast, innovative cheating occurs with species with whom legitimate interactions are not possible, because of a physiological or morphological barrier. Innovative cheating can enhance community persistence under some conditions: when cheaters have few mutualistic partners, cheat at low or intermediate frequency and the cost associated with mutualism is not too high. Under these conditions, the negative effects of cheating on partner persistence are overcompensated at the community level by the positive feedback loops that arise in diverse mutualistic communities. Using an empirical dataset of plant-bird interactions (hummingbirds and flowerpiercers), we found that observed cheating patterns are highly consistent with theoretical cheating patterns found to increase community persistence. This result suggests that the cheating patterns observed in nature could contribute to promote species coexistence in mutualistic communities, instead of necessarily destabilizing them

Macroevolution of the plant–hummingbird pollination system

ABSTRACTPlant–hummingbird interactions are considered a classic example of coevolution, a process in which mutually dependent species influence each other's evolution. Plants depend on hummingbirds for pollination, whereas hummingbirds rely on nectar for food. As a step towards understanding coevolution, this review focuses on the macroevolutionary consequences of plant–hummingbird interactions, a relatively underexplored area in the current literature. We synthesize prior studies, illustrating the origins and dynamics of hummingbird pollination across different angiosperm clades previously pollinated by insects (mostly bees), bats, and passerine birds. In some cases, the crown age of hummingbirds pre‐dates the plants they pollinate. In other cases, plant groups transitioned to hummingbird pollination early in the establishment of this bird group in the Americas, with the build‐up of both diversities coinciding temporally, and hence suggesting co‐diversification. Determining what triggers shifts to and away from hummingbird pollination remains a major open challenge. The impact of hummingbirds on plant diversification is complex, with many tropical plant lineages experiencing increased diversification after acquiring flowers that attract hummingbirds, and others experiencing no change or even a decrease in diversification rates. This mixed evidence suggests that other extrinsic or intrinsic factors, such as local climate and isolation, are important covariables driving the diversification of plants adapted to hummingbird pollination. To guide future studies, we discuss the mechanisms and contexts under which hummingbirds, as a clade and as individual species (e.g. traits, foraging behaviour, degree of specialization), could influence plant evolution. We conclude by commenting on how macroevolutionary signals of the mutualism could relate to coevolution, highlighting the unbalanced focus on the plant side of the interaction, and advocating for the use of species‐level interaction data in macroevolutionary studies

ROBITT: a tool for assessing the risk-of-bias in studies of temporal trends in ecology

1. Aggregated species occurrence and abundance data from disparate sources are increasingly accessible to ecologists for the analysis of temporal trends in biodiversity. However, sampling biases relevant to any given research question are often poorly explored and infrequently reported; this can undermine statistical inference. In other disciplines, it is common for researchers to complete ‘risk-of-bias’ assessments to expose and document the potential for biases to undermine conclusions. The huge growth in available data, and recent controversies surrounding their use to infer temporal trends, indicate that similar assessments are urgently needed in ecology. 2. We introduce ROBITT, a structured tool for assessing the ‘Risk-Of-Bias In studies of Temporal Trends in ecology’. ROBITT has a similar format to its counterparts in other disciplines: it comprises signalling questions designed to elicit information on the potential for bias in key study domains. In answering these, users will define study inferential goal(s) and relevant statistical target populations. This information is used to assess potential sampling biases across domains relevant to the research question (e.g. geography, taxonomy, environment), and how these vary through time. If assessments indicate biases, then users must clearly describe them and/or explain what mitigating action will be taken. 3. Everything that users need to complete a ROBITT assessment is provided: the tool, a guidance document and a worked example. Following other disciplines, the tool and guidance document were developed through a consensus-forming process across experts working in relevant areas of ecology and evidence synthesis. 4. We propose that researchers should be strongly encouraged to include a ROBITT assessment when publishing studies of biodiversity trends, especially when using aggregated data. This will help researchers to structure their thinking, clearly acknowledge potential sampling issues, highlight where expert consultation is required and provide an opportunity to describe data checks that might go unreported. ROBITT will also enable reviewers, editors and readers to establish how well research conclusions are supported given a dataset combined with some analytical approach. In turn, it should strengthen evidence-based policy and practice, reduce differing interpretations of data and provide a clearer picture of the uncertainties associated with our understanding of reality

A bayesian inference procedure based on inverse dispersion modelling for source term estimation in built-up environments

International audienceIn atmospheric physics, reconstructing a pollution source is a challenging and important question. It provides better input parameters to dispersion models, and gives useful information to first-responder teams in case of an accidental toxic release. Various methods already exist, but using them requires an important amount of computational resources, especially when the accuracy of the dispersion model increases which is necessary in complex built-up environments. In this paper, a Bayesian probabilistic approach to estimate the location and the temporal emission profile of a pointwise source is proposed. More precisely, an Adaptive Multiple Importance Sampling (AMIS) algorithm is considered and enhanced by an efficient use of a Lagrangian Particle Dispersion Model (LPDM) in backward mode. Twin experiments empirically demonstrate the efficiency of the proposed inference strategy in very complex cases

Application of the Bayesian approach and inverse dispersion modelling to source term estimates in built-up environments

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Sequential Monte Carlo sampler applied to source term estimation in complex atmospheric environments

International audienceThe accurate and rapid reconstruction of a pollution source represents an important but challenging problem. Several strategies have been proposed to tackle this issue among which we find the Bayesian solutions that have the interesting ability to provide a complete characterization of the source parameters through their posterior probability density function. However, these existing techniques have certain limitations such as their computational complexity, the required model assumptions, their difficulty to converge, the sensitive choice of model/algorithm parameters which clearly limit their easy use in practical scenarios. In this paper, to overcome these limitations, we propose a novel Bayesian solution based on a general and flexible population-based Monte Carlo algorithm, namely the sequential Monte Carlo sampler. Owing to its full adaptivity through the learning process, the main advantage of such an algorithm lies in its capability to be used without requiring any specific assumptions on the underlying statistical model and also without requiring from the user any difficult choices of certain parameter values. The performance of the proposed inference strategy is assessed using twin experiments in complex built-up environments

European plants lagging behind climate change pay a climatic debt in the North, but are favoured in the South

International audienceFor many species, climate change leads to range shifts that are detectable, but often insufficient to track historical climatic conditions. These lags of species range shifts behind climatic conditions are often coined “climatic debts”, but the demographic costs entailed by the word “debt” have not been demonstrated. Here, we used opportunistic distribution data for c. 4000 European plant species to estimate the temporal shifts in climatic conditions experienced by these species and their occupancy trends, over the last 65 years. The resulting negative relationship observed between these two variables provides the first piece of evidence that European plants are already paying a climatic debt in Alpine, Atlantic and Boreal regions. In contrast, plants appear to benefit from a surprising “climatic bonus” in the Mediterranean. We also find that among multiple pressures faced by plants, climate change is now on par with other known drivers of occupancy trends, including eutrophication and urbanisation

New insights into inverse dispersion modelling and probabilistic source term estimate at local scale in complex environments

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Detection of Low Level Concentrations of Hazardous Materials in the Air Using Sequential Multivariate Detection Methods

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