More than 75 percent decline over 27 years in total flying insect biomass in protected areas.
TL;DR: This analysis estimates a seasonal decline of 76%, and mid-summer decline of 82% in flying insect biomass over the 27 years of study, and shows that this decline is apparent regardless of habitat type, while changes in weather, land use, and habitat characteristics cannot explain this overall decline.
Abstract: Global declines in insects have sparked wide interest among scientists, politicians, and the general public. Loss of insect diversity and abundance is expected to provoke cascading effects on food webs and to jeopardize ecosystem services. Our understanding of the extent and underlying causes of this decline is based on the abundance of single species or taxonomic groups only, rather than changes in insect biomass which is more relevant for ecological functioning. Here, we used a standardized protocol to measure total insect biomass using Malaise traps, deployed over 27 years in 63 nature protection areas in Germany (96 unique location-year combinations) to infer on the status and trend of local entomofauna. Our analysis estimates a seasonal decline of 76%, and mid-summer decline of 82% in flying insect biomass over the 27 years of study. We show that this decline is apparent regardless of habitat type, while changes in weather, land use, and habitat characteristics cannot explain this overall decline. This yet unrecognized loss of insect biomass must be taken into account in evaluating declines in abundance of species depending on insects as a food source, and ecosystem functioning in the European landscape.
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Harvard University1, Potsdam Institute for Climate Impact Research2, Stockholm Resilience Centre3, University of Oxford4, City University London5, World Wide Fund for Nature6, Chatham House7, Environmental Change Institute8, University of Minnesota9, University of California, Santa Barbara10, CGIAR11, Johns Hopkins University12, American University of Beirut13, Wageningen University and Research Centre14, Institute for Health Metrics and Evaluation15, Indian Institute of Technology Kanpur16, ETH Zurich17, Commonwealth Scientific and Industrial Research Organisation18, University of Indonesia19, World Health Organization20, Food and Agriculture Organization21, International Food Policy Research Institute22, Royal Swedish Academy of Sciences23, University of Auckland24, Public Health Foundation of India25, Centre for Science and Environment26
TL;DR: Food in the Anthropocene : the EAT-Lancet Commission on healthy diets from sustainable food systems focuses on meat, fish, vegetables and fruit as sources of protein.
4,710 citations
TL;DR: In this paper, a comprehensive review of 73 historical reports of insect declines from across the globe, and systematically assess the underlying drivers of insect extinction, reveals dramatic rates of decline that may lead to the extinction of 40% of the world's insect species over the next few decades.
1,754 citations
Cites background from "More than 75 percent decline over 2..."
...Rather surprisingly, 80% of observed inter-annual variability in insect numbers was left unexplained (Hallmann et al., 2017)....
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...T shocking 76% decline in flying insect biomass at several of Germany's protected areas (Hallmann et al., 2017)....
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...The large declines in insect biomass observed in Europe (Hallmann et al., 2017) and Puerto Rico (Lister and Garcia, 2018) inevitably lead to a starvation of dependent vertebrates (Hallmann et al., 2014; Lister and Garcia, 2018; Poulin et al., 2010; Wickramasinghe et al., 2003)....
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...The large declines in insect biomass observed in Europe (Hallmann et al., 2017) and Puerto Rico 0 0....
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...shocking 76% decline in flying insect biomass at several of Germany's protected areas (Hallmann et al., 2017)....
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TL;DR: The results suggest that major drivers of arthropod decline act at larger spatial scales, and are associated with agriculture at the landscape level, which implies that policies need to address the landscape scale to mitigate the negative effects of land-use practices.
Abstract: Recent reports of local extinctions of arthropod species1, and of massive declines in arthropod biomass2, point to land-use intensification as a major driver of decreasing biodiversity. However, to our knowledge, there are no multisite time series of arthropod occurrences across gradients of land-use intensity with which to confirm causal relationships. Moreover, it remains unclear which land-use types and arthropod groups are affected, and whether the observed declines in biomass and diversity are linked to one another. Here we analyse data from more than 1 million individual arthropods (about 2,700 species), from standardized inventories taken between 2008 and 2017 at 150 grassland and 140 forest sites in 3 regions of Germany. Overall gamma diversity in grasslands and forests decreased over time, indicating loss of species across sites and regions. In annually sampled grasslands, biomass, abundance and number of species declined by 67%, 78% and 34%, respectively. The decline was consistent across trophic levels and mainly affected rare species; its magnitude was independent of local land-use intensity. However, sites embedded in landscapes with a higher cover of agricultural land showed a stronger temporal decline. In 30 forest sites with annual inventories, biomass and species number—but not abundance—decreased by 41% and 36%, respectively. This was supported by analyses of all forest sites sampled in three-year intervals. The decline affected rare and abundant species, and trends differed across trophic levels. Our results show that there are widespread declines in arthropod biomass, abundance and the number of species across trophic levels. Arthropod declines in forests demonstrate that loss is not restricted to open habitats. Our results suggest that major drivers of arthropod decline act at larger spatial scales, and are (at least for grasslands) associated with agriculture at the landscape level. This implies that policies need to address the landscape scale to mitigate the negative effects of land-use practices. Analyses of a dataset of arthropod biomass, abundance and diversity in grassland and forest habitats in Germany for the period 2008–2017 reveal that drivers of arthropod declines act at the landscape level.
625 citations
TL;DR: Wagner et al. as discussed by the authors found that more than half of all amphibians are imperiled and more than 80% of all vertebrate species are in danger of extinction over the next few decades.
Abstract: Nature is under siege. In the last 10,000 y the human population has grown from 1 million to 7.8 billion. Much of Earth’s arable lands are already in agriculture (1), millions of acres of tropical forest are cleared each year (2, 3), atmospheric CO2 levels are at their highest concentrations in more than 3 million y (4), and climates are erratically and steadily changing from pole to pole, triggering unprecedented droughts, fires, and floods across continents. Indeed, most biologists agree that the world has entered its sixth mass extinction event, the first since the end of the Cretaceous Period 66 million y ago, when more than 80% of all species, including the nonavian dinosaurs, perished.
Ongoing losses have been clearly demonstrated for better-studied groups of organisms. Terrestrial vertebrate population sizes and ranges have contracted by one-third, and many mammals have experienced range declines of at least 80% over the last century (5). A 2019 assessment suggests that half of all amphibians are imperiled (2.5% of which have recently gone extinct) (6). Bird numbers across North America have fallen by 2.9 billion since 1970 (7). Prospects for the world’s coral reefs, beyond the middle of this century, could scarcely be more dire (8). A 2020 United Nations report estimated that more than a million species are in danger of extinction over the next few decades (9), but also see the more bridled assessments in refs. 10 and 11.
Although a flurry of reports has drawn attention to declines in insect abundance, biomass, species richness, and range sizes (e.g., refs. 12⇓⇓⇓⇓⇓–18; for reviews see refs. 19 and 20), whether the rates of declines for insects are on par with or exceed those for other groups remains unknown. There are still too …
[↵][1]1To whom correspondence may be addressed. Email: david.wagner{at}uconn.edu.
[1]: #xref-corresp-1-1
609 citations
TL;DR: Because the geographic extent and magnitude of insect declines are largely unknown, there is an urgent need for monitoring efforts, especially across ecological gradients, which will help to identify important causal factors in declines.
Abstract: Insect declines are being reported worldwide for flying, ground, and aquatic lineages. Most reports come from western and northern Europe, where the insect fauna is well-studied and there are consi...
607 citations
References
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TL;DR: In this article, a model is described in an lmer call by a formula, in this case including both fixed-and random-effects terms, and the formula and data together determine a numerical representation of the model from which the profiled deviance or the profeatured REML criterion can be evaluated as a function of some of model parameters.
Abstract: Maximum likelihood or restricted maximum likelihood (REML) estimates of the parameters in linear mixed-effects models can be determined using the lmer function in the lme4 package for R. As for most model-fitting functions in R, the model is described in an lmer call by a formula, in this case including both fixed- and random-effects terms. The formula and data together determine a numerical representation of the model from which the profiled deviance or the profiled REML criterion can be evaluated as a function of some of the model parameters. The appropriate criterion is optimized, using one of the constrained optimization functions in R, to provide the parameter estimates. We describe the structure of the model, the steps in evaluating the profiled deviance or REML criterion, and the structure of classes or types that represents such a model. Sufficient detail is included to allow specialization of these structures by users who wish to write functions to fit specialized linear mixed models, such as models incorporating pedigrees or smoothing splines, that are not easily expressible in the formula language used by lmer.
50,607 citations
TL;DR: The focus is on applied inference for Bayesian posterior distributions in real problems, which often tend toward normal- ity after transformations and marginalization, and the results are derived as normal-theory approximations to exact Bayesian inference, conditional on the observed simulations.
Abstract: The Gibbs sampler, the algorithm of Metropolis and similar iterative simulation methods are potentially very helpful for summarizing multivariate distributions. Used naively, however, iterative simulation can give misleading answers. Our methods are simple and generally applicable to the output of any iterative simulation; they are designed for researchers primarily interested in the science underlying the data and models they are analyzing, rather than for researchers interested in the probability theory underlying the iterative simulations themselves. Our recommended strategy is to use several independent sequences, with starting points sampled from an overdispersed distribution. At each step of the iterative simulation, we obtain, for each univariate estimand of interest, a distributional estimate and an estimate of how much sharper the distributional estimate might become if the simulations were continued indefinitely. Because our focus is on applied inference for Bayesian posterior distributions in real problems, which often tend toward normality after transformations and marginalization, we derive our results as normal-theory approximations to exact Bayesian inference, conditional on the observed simulations. The methods are illustrated on a random-effects mixture model applied to experimental measurements of reaction times of normal and schizophrenic patients.
13,884 citations
"More than 75 percent decline over 2..." refers background in this paper
...potential scale reduction factor [54] (commonly R̂), that measures the ratio of posterior distributions between independent MCM chains (in all models, all parameters attained values below 1....
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TL;DR: In this paper, the authors consider the problem of comparing complex hierarchical models in which the number of parameters is not clearly defined and derive a measure pD for the effective number in a model as the difference between the posterior mean of the deviances and the deviance at the posterior means of the parameters of interest, which is related to other information criteria and has an approximate decision theoretic justification.
Abstract: Summary. We consider the problem of comparing complex hierarchical models in which the number of parameters is not clearly defined. Using an information theoretic argument we derive a measure pD for the effective number of parameters in a model as the difference between the posterior mean of the deviance and the deviance at the posterior means of the parameters of interest. In general pD approximately corresponds to the trace of the product of Fisher's information and the posterior covariance, which in normal models is the trace of the ‘hat’ matrix projecting observations onto fitted values. Its properties in exponential families are explored. The posterior mean deviance is suggested as a Bayesian measure of fit or adequacy, and the contributions of individual observations to the fit and complexity can give rise to a diagnostic plot of deviance residuals against leverages. Adding pD to the posterior mean deviance gives a deviance information criterion for comparing models, which is related to other information criteria and has an approximate decision theoretic justification. The procedure is illustrated in some examples, and comparisons are drawn with alternative Bayesian and classical proposals. Throughout it is emphasized that the quantities required are trivial to compute in a Markov chain Monte Carlo analysis.
11,691 citations
TL;DR: The nature and extent of reported declines, and the potential drivers of pollinator loss are described, including habitat loss and fragmentation, agrochemicals, pathogens, alien species, climate change and the interactions between them are reviewed.
Abstract: Pollinators are a key component of global biodiversity, providing vital ecosystem services to crops and wild plants. There is clear evidence of recent declines in both wild and domesticated pollinators, and parallel declines in the plants that rely upon them. Here we describe the nature and extent of reported declines, and review the potential drivers of pollinator loss, including habitat loss and fragmentation, agrochemicals, pathogens, alien species, climate change and the interactions between them. Pollinator declines can result in loss of pollination services which have important negative ecological and economic impacts that could significantly affect the maintenance of wild plant diversity, wider ecosystem stability, crop production, food security and human welfare.
4,608 citations
01 Jan 2003
TL;DR: JAGS is a program for Bayesian Graphical modelling which aims for compatibility with Classic BUGS and could eventually be developed as an R package.
Abstract: JAGS is a program for Bayesian Graphical modelling which aims for compatibility with Classic BUGS. The program could eventually be developed as an R package. This article explains the motivations for this program, briefly describes the architecture and then discusses some ideas for a vectorized form of the BUGS language.
4,548 citations
"More than 75 percent decline over 2..." refers methods in this paper
...Parameter values are obtained by the use of Markov chain Monte Carlo (MCMC) methods by the aid of JAGS (Just Another Gibbs Sampler [50]) invoked through R [51] and the R2Jags package [52]....
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