Microclimate‐driven trends in spring‐emergence phenology in a temperate reptile (Vipera berus): Evidence for a potential “climate trap”?

Climate change can not only increase the exposure of organisms to higher temperatures but can also drive phenological shifts that alter their susceptibility to conditions at the onset of breeding cycles. Organisms rely on climatic cues to time annual life cycle events, but the extent to which climate change has altered cue reliability remains unclear. Here, we examined the risk of a “climate trap”—a climatically driven desynchronization of the cues that determine life cycle events and fitness later in the season in a temperate reptile, the European adder (Vipera berus). During the winter, adders hibernate underground, buffered against subzero temperatures, and re‐emerge in the spring to reproduce. We derived annual spring‐emergence trends between 1983 and 2017 from historical observations in Cornwall, UK, and related these trends to the microclimatic conditions that adders experienced. Using a mechanistic microclimate model, we computed below‐ and near‐ground temperatures to derive accumulated degree‐hour and absolute temperature thresholds that predicted annual spring‐emergence timing. Trends in annual‐emergence timing and subsequent exposure to ground frost were then quantified. We found that adders have advanced their phenology toward earlier emergence. Earlier emergence was associated with increased exposure to ground frost and, contradicting the expected effects of macroclimate warming, increased post‐emergence exposure to ground frost at some locations. The susceptibility of adders to this “climate trap” was related to the rate at which frost risk diminishes relative to advancement in phenology, which depends on the seasonality of climate. We emphasize the need to consider exposure to changing microclimatic conditions when forecasting biological impacts of climate change.     

Effect of Energy Under-Reporting on Secular Trends of Dietary Patterns in a Mediterranean Population

BackgroundDiet is an important factor in the prevention of chronic diseases. Analysis of secular trends of dietary patterns can be biased by energy under-reporting. Therefore, the objective of the present study was to analyse the impact of energy under-reporting on dietary patterns and secular trends in dietary patterns defined by cluster analysis.ResultsThree clusters, “healthy”, “mixed” and “western”, were identified for both surveys. The “mixed” cluster was the predominant cluster in both surveys. Excluding EUR reduced the proportion of the “mixed” cluster up to 6.40% in the 2000 survey; this caused secular trend increase in the prevalence of the “mixed” pattern. Cross-classification analysis of all participants and PER’ data showed substantial agreement in cluster assignments: 68.7% in 2000 and 84.4% in 2005. Excluding EUR did not cause meaningful (≥15%) changes in the “healthy” pattern. It provoked changes in consumption of some food groups in the “mixed” and “western” patterns: mainly decreases of unhealthy foods within the 2000 and increases of unhealthy foods within the 2005 surveys. Secular trend effects of EUR were similar to those within the 2005 survey. Excluding EUR reversed the direction of secular trends in consumption of several food groups in PER in the “mixed” and “western” patterns.ConclusionsEUR affected distribution of participants between dietary patterns within and between surveys, secular trends in food group consumption and amount of food consumed in all, but not in the “healthy” pattern. Our findings emphasize threats from energy under-reporting in dietary data analysis.     

Chapter 3: Small Molecules and Disease

“Big” molecules such as proteins and genes still continue to capture the imagination of most biologists, biochemists and bioinformaticians. “Small” molecules, on the other hand, are the molecules that most biologists, biochemists and bioinformaticians prefer to ignore. However, it is becoming increasingly apparent that small molecules such as amino acids, lipids and sugars play a far more important role in all aspects of disease etiology and disease treatment than we realized. This particular chapter focuses on an emerging field of bioinformatics called “chemical bioinformatics” – a discipline that has evolved to help address the blended chemical and molecular biological needs of toxicogenomics, pharmacogenomics, metabolomics and systems biology. In the following pages we will cover several topics related to chemical bioinformatics. First, a brief overview of some of the most important or useful chemical bioinformatic resources will be given. Second, a more detailed overview will be given on those particular resources that allow researchers to connect small molecules to diseases. This section will focus on describing a number of recently developed databases or knowledgebases that explicitly relate small molecules – either as the treatment, symptom or cause – to disease. Finally a short discussion will be provided on newly emerging software tools that exploit these databases as a means to discover new biomarkers or even new treatments for disease.

What to Learn in This Chapter

• The meaning of chemical bioinformatics
• Strengths and limitations of existing chemical bioinformatic databases
• Using databases to learn about the cause and treatment of diseases
• The Small Molecule Pathway Database (SMPDB)
• The Human Metabolome Database (HMDB)
• DrugBank
• The Toxin and Toxin-Target Database (T3DB)
• PolySearch and Metabolite Set Enrichment Analysis

This article is part of the “Translational Bioinformatics” collection for PLOS Computational Biology.

     

Thematic Minireview Series: New Directions in G Protein-coupled Receptor Pharmacology

Over the past half-century, The Journal of Biological Chemistry has been the venue for many landmark publications on the topic of G protein-coupled receptors (GPCRs, also known as seven-transmembrane receptors). The GPCR superfamily in humans is composed of about 800 members, and is the target of about one-third of all pharmaceuticals. Most of these drugs target a very small subset of GPCRs, and do so by mimicking or competing with endogenous hormones and neurotransmitters. This thematic minireview series examines some emerging trends in GPCR drug discovery. The first article describes efforts to systematically interrogate the human “GPCR-ome,” including more than 150 uncharacterized “orphan” receptors. The second article describes recent efforts to target alternative receptor binding sites with drugs that act as allosteric modulators of orthosteric ligands. The third article describes how the recent expansion of GPCR structures is providing new opportunities for computer-guided drug discovery. Collectively, these three articles provide a roadmap for the most important emerging trends in GPCR pharmacology.     

Emerging Infectious Disease Leads to Rapid Population Declines of Common British Birds

Emerging infectious diseases are increasingly cited as threats to wildlife, livestock and humans alike. They can threaten geographically isolated or critically endangered wildlife populations; however, relatively few studies have clearly demonstrated the extent to which emerging diseases can impact populations of common wildlife species. Here, we report the impact of an emerging protozoal disease on British populations of greenfinch Carduelis chloris and chaffinch Fringilla coelebs, two of the most common birds in Britain. Morphological and molecular analyses showed this to be due to Trichomonas gallinae. Trichomonosis emerged as a novel fatal disease of finches in Britain in 2005 and rapidly became epidemic within greenfinch, and to a lesser extent chaffinch, populations in 2006. By 2007, breeding populations of greenfinches and chaffinches in the geographic region of highest disease incidence had decreased by 35% and 21% respectively, representing mortality in excess of half a million birds. In contrast, declines were less pronounced or absent in these species in regions where the disease was found in intermediate or low incidence. Also, populations of dunnock Prunella modularis, which similarly feeds in gardens, but in which T. gallinae was rarely recorded, did not decline. This is the first trichomonosis epidemic reported in the scientific literature to negatively impact populations of free-ranging non-columbiform species, and such levels of mortality and decline due to an emerging infectious disease are unprecedented in British wild bird populations. This disease emergence event demonstrates the potential for a protozoan parasite to jump avian host taxonomic groups with dramatic effect over a short time period.     

The Impact of National Institutes of Health Funding on U.S. Cardiovascular Disease Research

Background

Intense interest surrounds the recent expansion of US National Institutes of Health (NIH) budgets as part of economic stimulus legislation. However, the relationship between NIH funding and cardiovascular disease research is poorly understood, making the likely impact of this policy change unclear.

Methods

The National Library of Medicine''s PubMed database was searched for articles published from 1996 to 2006, originating from U.S. institutions, and containing the phrases “cardiolog,” “cardiovascular,” or “cardiac,” in the first author''s department. Research methodology, journal of publication, journal impact factor, and receipt of NIH funding were recorded. Differences in means and trends were tested with t-tests and linear regression, respectively, with P≤0.05 for significance.

Results

Of 117,643 world cardiovascular articles, 36,684 (31.2%) originated from the U.S., of which 10,293 (28.1%) received NIH funding. The NIH funded 40.1% of U.S. basic science articles, 20.3% of overall clinical trials, 18.1% of randomized-controlled, and 12.2% of multicenter clinical trials. NIH-funded and total articles grew significantly (65 articles/year, P<0.001 and 218 articles/year, P<0.001, respectively). The proportion of articles receiving NIH funding was stable, but grew significantly for basic science and clinical trials (0.87%/year, P<0.001 and 0.67%/year, P = 0.029, respectively). NIH-funded articles had greater journal impact factors than non NIH-funded articles (5.76 vs. 3.71, P<0.001).

Conclusions

NIH influence on U.S. cardiovascular research expanded in the past decade, during the period of NIH budget doubling. A substantial fraction of research is now directly funded and thus likely sensitive to budget fluctuations, particularly in basic science research. NIH funding predicts greater journal impact.     

Analysis of 567,758 randomized controlled trials published over 30 years reveals trends in phrases used to discuss results that do not reach statistical significance

The power of language to modify the reader’s perception of interpreting biomedical results cannot be underestimated. Misreporting and misinterpretation are pressing problems in randomized controlled trials (RCT) output. This may be partially related to the statistical significance paradigm used in clinical trials centered around a P value below 0.05 cutoff. Strict use of this P value may lead to strategies of clinical researchers to describe their clinical results with P values approaching but not reaching the threshold to be “almost significant.” The question is how phrases expressing nonsignificant results have been reported in RCTs over the past 30 years. To this end, we conducted a quantitative analysis of English full texts containing 567,758 RCTs recorded in PubMed between 1990 and 2020 (81.5% of all published RCTs in PubMed). We determined the exact presence of 505 predefined phrases denoting results that approach but do not cross the line of formal statistical significance (P < 0.05). We modeled temporal trends in phrase data with Bayesian linear regression. Evidence for temporal change was obtained through Bayes factor (BF) analysis. In a randomly sampled subset, the associated P values were manually extracted. We identified 61,741 phrases in 49,134 RCTs indicating almost significant results (8.65%; 95% confidence interval (CI): 8.58% to 8.73%). The overall prevalence of these phrases remained stable over time, with the most prevalent phrases being “marginally significant” (in 7,735 RCTs), “all but significant” (7,015), “a nonsignificant trend” (3,442), “failed to reach statistical significance” (2,578), and “a strong trend” (1,700). The strongest evidence for an increased temporal prevalence was found for “a numerical trend,” “a positive trend,” “an increasing trend,” and “nominally significant.” In contrast, the phrases “all but significant,” “approaches statistical significance,” “did not quite reach statistical significance,” “difference was apparent,” “failed to reach statistical significance,” and “not quite significant” decreased over time. In a random sampled subset of 29,000 phrases, the manually identified and corresponding 11,926 P values, 68,1% ranged between 0.05 and 0.15 (CI: 67. to 69.0; median 0.06). Our results show that RCT reports regularly contain specific phrases describing marginally nonsignificant results to report P values close to but above the dominant 0.05 cutoff. The fact that the prevalence of the phrases remained stable over time indicates that this practice of broadly interpreting P values close to a predefined threshold remains prevalent. To enhance responsible and transparent interpretation of RCT results, researchers, clinicians, reviewers, and editors may reduce the focus on formal statistical significance thresholds and stimulate reporting of P values with corresponding effect sizes and CIs and focus on the clinical relevance of the statistical difference found in RCTs.

The power of language to modify the reader’s perception of interpreting biomedical results cannot be underestimated. An analysis of more than half a million randomized controlled trials reveals that researchers are using appealing phrases to describe non-significant findings as if they were below the p=0.05 significance threshold.     

Human Collective Intelligence under Dual Exploration-Exploitation Dilemmas

The exploration-exploitation dilemma is a recurrent adaptive problem for humans as well as non-human animals. Given a fixed time/energy budget, every individual faces a fundamental trade-off between exploring for better resources and exploiting known resources to optimize overall performance under uncertainty. Colonies of eusocial insects are known to solve this dilemma successfully via evolved coordination mechanisms that function at the collective level. For humans and other non-eusocial species, however, this dilemma operates within individuals as well as between individuals, because group members may be motivated to take excessive advantage of others'' exploratory findings through social learning. Thus, even though social learning can reduce collective exploration costs, the emergence of disproportionate “information scroungers” may severely undermine its potential benefits. We investigated experimentally whether social learning opportunities might improve the performance of human participants working on a “multi-armed bandit” problem in groups, where they could learn about each other''s past choice behaviors. Results showed that, even though information scroungers emerged frequently in groups, social learning opportunities reduced total group exploration time while increasing harvesting from better options, and consequentially improved collective performance. Surprisingly, enriching social information by allowing participants to observe others'' evaluations of chosen options (e.g., Amazon''s 5-star rating system) in addition to choice-frequency information had a detrimental impact on performance compared to the simpler situation with only the choice-frequency information. These results indicate that humans groups can handle the fundamental “dual exploration-exploitation dilemmas” successfully, and that social learning about simple choice-frequencies can help produce collective intelligence.     

Systematic Classification of Disease Severity for Evaluation of Expanded Carrier Screening Panels

Professional guidelines dictate that disease severity is a key criterion for carrier screening. Expanded carrier screening, which tests for hundreds to thousands of mutations simultaneously, requires an objective, systematic means of describing a given disease''s severity to build screening panels. We hypothesized that diseases with characteristics deemed to be of highest impact would likewise be rated as most severe, and diseases with characteristics of lower impact would be rated as less severe. We describe a pilot test of this hypothesis in which we surveyed 192 health care professionals to determine the impact of specific disease phenotypic characteristics on perceived severity, and asked the same group to rate the severity of selected inherited diseases. The results support the hypothesis: we identified four “Tiers” of disease characteristics (1–4). Based on these responses, we developed an algorithm that, based on the combination of characteristics normally seen in an affected individual, classifies the disease as Profound, Severe, Moderate, or Mild. This algorithm allows simple classification of disease severity that is replicable and not labor intensive.     

Why Has the Continuous Decline in German Suicide Rates Stopped in 2007?

Background

Whereas German suicide rates had a clear decreasing tendency between 1991 and 2006, they increased from 2007 to 2010. Deeper analyses of suicide data might help to understand better this change. The aim of this study was to analyze 1) whether recent trends can be related to changes in specific suicide methods and diverge by gender and age; 2) whether the decrease of suicide rates before 2007 as well as the increase from 2007 to 2010 are driven by the same suicide method.

Methods

Analyses were based on suicide data from the Federal Statistical Office of Germany. For 1998–2010, 136.583 suicide cases of men and women with known age and suicide method could be identified. These data were analyzed by joinpoint regression analysis, allowing identification of the best fitting point in time (“joinpoint”) at which the suicide rate significantly changes in magnitude or direction.

Results

The national downward trend between 1998 and 2007 was mainly due to corresponding changes in self-poisoning by other means than drugs (e.g., pesticides) (annual percentage change (APC) ≤ −4.33), drowning (APC ≤ −2.73), hanging (APC ≤ −2.69) and suicides by firearms (APC ≤ −1.46) in both genders. Regarding the overall increase of age-adjusted suicide rates in Germany 2007–2010, mainly the increase of self-poisoning (e.g., by drugs) and “being overrun” (APC ≥ 1.50) contributed to this trend.

Limitations

The true suicide rates might have been underestimated because of errors in the official death certificates.

Conclusions

Increase in suicide rates in Germany since 2007 went along with corresponding changes for “being overrun” and “self-poisoning”. Copycat suicides following the railway suicide of the goalkeeper Robert Enke partly contributed to the results. Thus, prevention of Werther effects and limitation of the availability of high pack sizes for drugs are of special relevance for the reversal of this trend.     

Mitochondrial DNA Genetics and the Heteroplasmy Conundrum in Evolution and Disease

The unorthodox genetics of the mtDNA is providing new perspectives on the etiology of the common “complex” diseases. The maternally inherited mtDNA codes for essential energy genes, is present in thousands of copies per cell, and has a very high mutation rate. New mtDNA mutations arise among thousands of other mtDNAs. The mechanisms by which these “heteroplasmic” mtDNA mutations come to predominate in the female germline and somatic tissues is poorly understood, but essential for understanding the clinical variability of a range of diseases. Maternal inheritance and heteroplasmy also pose major challengers for the diagnosis and prevention of mtDNA disease.     

Network-Based Elucidation of Human Disease Similarities Reveals Common Functional Modules Enriched for Pluripotent Drug Targets

Current work in elucidating relationships between diseases has largely been based on pre-existing knowledge of disease genes. Consequently, these studies are limited in their discovery of new and unknown disease relationships. We present the first quantitative framework to compare and contrast diseases by an integrated analysis of disease-related mRNA expression data and the human protein interaction network. We identified 4,620 functional modules in the human protein network and provided a quantitative metric to record their responses in 54 diseases leading to 138 significant similarities between diseases. Fourteen of the significant disease correlations also shared common drugs, supporting the hypothesis that similar diseases can be treated by the same drugs, allowing us to make predictions for new uses of existing drugs. Finally, we also identified 59 modules that were dysregulated in at least half of the diseases, representing a common disease-state “signature”. These modules were significantly enriched for genes that are known to be drug targets. Interestingly, drugs known to target these genes/proteins are already known to treat significantly more diseases than drugs targeting other genes/proteins, highlighting the importance of these core modules as prime therapeutic opportunities.     

Ten-Year Trends in Coronary Calcification in Individuals without Clinical Cardiovascular Disease in the Multi-Ethnic Study of Atherosclerosis

Background

Coronary heart disease (CHD) incidence has declined significantly in the US, as have levels of major coronary risk factors, including LDL-cholesterol, hypertension and smoking, but whether trends in subclinical atherosclerosis mirror these trends is not known.

Methods and Findings

To describe recent secular trends in subclinical atherosclerosis as measured by serial evaluations of coronary artery calcification (CAC) prevalence in a population over 10 years, we measured CAC using computed tomography (CT) and CHD risk factors in five serial cross-sectional samples of men and women from four race/ethnic groups, aged 55–84 and without clinical cardiovascular disease, who were members of Multi-Ethnic Study of Atherosclerosis (MESA) cohort from 2000 to 2012. Sample sizes ranged from 1062 to 4837. After adjusting for age, gender, and CT scanner, the prevalence of CAC increased across exams among African Americans, whose prevalence of CAC was 52.4% in 2000–02, 50.4% in 2003–04, 60.0% is 2005–06, 57.4% in 2007–08, and 61.3% in 2010–12 (p for trend <0.001). The trend was strongest among African Americans aged 55–64 [prevalence ratio for 2010–12 vs. 2000–02, 1.59 (95% confidence interval 1.06, 2.39); p = 0.005 for trend across exams]. There were no consistent trends in any other ethnic group. Risk factors generally improved in the cohort, and adjustment for risk factors did not change trends in CAC prevalence.

Conclusions

There was a significant secular trend towards increased prevalence of CAC over 10 years among African Americans and no change in three other ethnic groups. Trends did not reflect concurrent general improvement in risk factors. The trend towards a higher prevalence of CAC in African Americans suggests that CHD risk in this population is not improving relative to other groups.     

One Health: Past Successes and Future Challenges in Three African Contexts

Background

The recent emergence of zoonotic diseases such as Highly Pathogenic Avian Influenza (HPAI) and Severe Acute Respiratory Syndrome (SARS) have contributed to dominant Global Health narratives around health securitisation and pandemic preparedness, calling for greater co-operation between the health, veterinary and environmental sectors in the ever-evolving One Health movement. A decade later, One Health advocates face increasing pressure to translate the approach from theory into action.

Methodology/Principal Findings

A qualitative case study methodology was used to examine the emerging relationships between international One Health dialogue and its practical implementation in the African health policy context. A series of Key Informant Interviews (n = 32) with policy makers, government officials and academics in Nigeria, Tanzania and Uganda are presented as three separate case studies. Each case examines a significant aspect of One Health operationalisation, framed around the control of both emerging and Neglected Zoonotic Diseases including HPAI, Human African Trypanosomiasis and rabies. The research found that while there is general enthusiasm and a strong affirmative argument for adoption of One Health approaches in Africa, identifying alternative contexts away from a narrow focus on pandemics will help broaden its appeal, particularly for national or regionally significant endemic and neglected diseases not usually addressed under a “global” remit.

Conclusions/Significance

There is no ‘one size fits all’ approach to achieving the intersectoral collaboration, significant resource mobilisation and political co-operation required to realise a One Health approach. Individual country requirements cannot be underestimated, dismissed or prescribed in a top down manner. This article contributes to the growing discussion regarding not whether One Health should be operationalised, but how this may be achieved.     

Community species diversity mediates the trade‐off between aboveground and belowground biomass for grasses and forbs in degraded alpine meadow,Tibetan Plateau

Although many empirical experiments have shown that increasing degradation results in lower aboveground biomass (AGB), our knowledge of the magnitude of belowground biomass (BGB) for individual plants is a prerequisite for accurately revealing the biomass trade‐off in degraded grasslands. Here, by linking the AGB and BGB of individual plants, species in the community, and soil properties, we explored the biomass partitioning patterns in different plant functional groups (grasses of Stipa capillacea and forbs of Anaphalis xylorhiza). Our results indicated that 81% and 60% of the biomass trade‐off variations could be explained by environmental factors affecting grasses and forbs, respectively. The change in community species diversity dominated the biomass trade‐off via either direct or indirect effects on soil properties and biomass. However, the community species diversity imparted divergent effects on the biomass trade‐off for grasses (scored at −0.72) and forbs (scored at 0.59). Our findings suggest that plant communities have evolved two contrasting strategies of biomass allocation patterns in degraded grasslands. These are the “conservative” strategy in grasses, in which plants with larger BGB trade‐off depends on gigantic roots for soil resources, and the “opportunistic” strategy in forbs, in which plants can adapt to degraded lands using high variation and optimal biomass allocation.     

Pathocenosis: A Holistic Approach to Disease Ecology

The History of medicine describes the emergence and recognition of infectious diseases, and human attempts to stem them. It also throws light on the role of changing environmental conditions on disease emergence/re-emergence, establishment and, sometimes, disappearance. However, the dynamics of infectious diseases is also influenced by the relationships between the community of interacting infectious agents present at a given time in a given territory, a concept that Mirko Grmek, an historian of medicine, conceptualized with the word “pathocenosis”. The spatial and temporal evolution of diseases, when observed at the appropriate scales, illustrates how a change in the pathocenosis, whether of “natural” or anthropic origin, can lead to the emergence and spread of diseases.     

Unraveling Genomic Complexity at a Quantitative Disease Resistance Locus in Maize

Multiple disease resistance has important implications for plant fitness, given the selection pressure that many pathogens exert directly on natural plant populations and indirectly via crop improvement programs. Evidence of a locus conditioning resistance to multiple pathogens was found in bin 1.06 of the maize genome with the allele from inbred line “Tx303” conditioning quantitative resistance to northern leaf blight (NLB) and qualitative resistance to Stewart’s wilt. To dissect the genetic basis of resistance in this region and to refine candidate gene hypotheses, we mapped resistance to the two diseases. Both resistance phenotypes were localized to overlapping regions, with the Stewart’s wilt interval refined to a 95.9-kb segment containing three genes and the NLB interval to a 3.60-Mb segment containing 117 genes. Regions of the introgression showed little to no recombination, suggesting structural differences between the inbred lines Tx303 and “B73,” the parents of the fine-mapping population. We examined copy number variation across the region using next-generation sequencing data, and found large variation in read depth in Tx303 across the region relative to the reference genome of B73. In the fine-mapping region, association mapping for NLB implicated candidate genes, including a putative zinc finger and pan1. We tested mutant alleles and found that pan1 is a susceptibility gene for NLB and Stewart’s wilt. Our data strongly suggest that structural variation plays an important role in resistance conditioned by this region, and pan1, a gene conditioning susceptibility for NLB, may underlie the QTL.     

Observing climate change trends in ocean biogeochemistry: when and where

Understanding the influence of anthropogenic forcing on the marine biosphere is a high priority. Climate change‐driven trends need to be accurately assessed and detected in a timely manner. As part of the effort towards detection of long‐term trends, a network of ocean observatories and time series stations provide high quality data for a number of key parameters, such as pH, oxygen concentration or primary production (PP). Here, we use an ensemble of global coupled climate models to assess the temporal and spatial scales over which observations of eight biogeochemically relevant variables must be made to robustly detect a long‐term trend. We find that, as a global average, continuous time series are required for between 14 (pH) and 32 (PP) years to distinguish a climate change trend from natural variability. Regional differences are extensive, with low latitudes and the Arctic generally needing shorter time series (<~30 years) to detect trends than other areas. In addition, we quantify the ‘footprint’ of existing and planned time series stations, that is the area over which a station is representative of a broader region. Footprints are generally largest for pH and sea surface temperature, but nevertheless the existing network of observatories only represents 9–15% of the global ocean surface. Our results present a quantitative framework for assessing the adequacy of current and future ocean observing networks for detection and monitoring of climate change‐driven responses in the marine ecosystem.     

Climate change winners and losers among North American bumblebees

Mounting evidence suggests that climate change, agricultural intensification and disease are impacting bumblebee health and contributing to species’ declines. Identifying how these factors impact insect communities at large spatial and temporal scales is difficult, partly because species may respond in different ways. Further, the necessary data must span large spatial and temporal scales, which usually means they comprise aggregated, presence-only records collected using numerous methods (e.g. diversity surveys, educational collections, citizen-science projects, standardized ecological surveys). Here, we use occupancy models, which explicitly correct for biases in the species observation process, to quantify the effect of changes in temperature, precipitation and floral resources on bumblebee site occupancy over the past 12 decades in North America. We find no evidence of genus-wide declines in site occupancy, but do find that occupancy is strongly related to temperature, and is only weakly related to precipitation or floral resources. We also find that more species are likely to be climate change ‘losers’ than ‘winners’ and that this effect is primarily associated with changing temperature. Importantly, all trends were highly species-specific, highlighting that genus or community-wide measures may not reflect diverse species-specific patterns that are critical in guiding allocation of conservation resources.