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Shifts in phenology are already resulting in disruptions to the timing of migration and breeding, and asynchronies between interacting species1–5. Recent syntheses have concluded that trophic level1, latitude6 and how phenological responses are measured7 are key to determining the strength of phenological responses to climate change. However, researchers still lack a comprehensive framework that can predict responses to climate change globally and across diverse taxa. Here, we synthesize hundreds of published time series of animal phenology from across the planet to show that temperature primarily drives phenological responses at mid-latitudes, with precipitation becoming important at lower latitudes, probably reflecting factors that drive seasonality in each region. Phylogeny and body size are associated with the strength of phenological shifts, suggesting emerging asynchronies between interacting species that differ in body size, such as hosts and parasites and predators and prey. Finally, although there are many compelling biological explanations for spring phenological delays, some examples of delays are associated with short annual records that are prone to sampling error. Our findings arm biologists with predictions concerning which climatic variables and organismal traits drive phenological shifts.

Climate change is expected to have complex effects on infectious diseases, causing some to increase, others to decrease, and many to shift their distributions. There have been several important advances in understanding the role of climate and climate change on wildlife and human infectious disease dynamics over the past several years. This essay examines 3 major areas of advancement, which include improvements to mechanistic disease models, investigations into the importance of climate variability to disease dynamics, and understanding the consequences of thermal mismatches between host and parasites. Applying the new information derived from these advances to climate–disease models and addressing the pressing knowledge gaps that we identify should improve the capacity to predict how climate change will affect disease risk for both wildlife and humans.

Recent epidemics of Zika, dengue, and chikungunya have heightened the need to understand the seasonal and geographic range of transmission by Aedes aegypti and Ae. albopictus mosquitoes. We use mechanistic transmission models to derive predictions for how the probability and magnitude of transmission for Zika, chikungunya, and dengue change with mean temperature, and we show that these predictions are well matched by human case data. Across all three viruses, models and human case data both show that transmission occurs between 18-34°C with maximal transmission occurring in a range from 26-29°C. Controlling for population size and two socioeconomic factors, temperature-dependent transmission based on our mechanistic model is an important predictor of human transmission occurrence and incidence. Risk maps indicate that tropical and subtropical regions are suitable for extended seasonal or year-round transmission, but transmission in temperate areas is limited to at most three months per year even if vectors are present. Such brief transmission windows limit the likelihood of major epidemics following disease introduction in temperate zones.

Extreme weather events are increasing in frequency and intensity as a result of modern climate change. During winter, species may be especially vulnerable to extreme weather as they are surviving on scarce resources and living at the edge of their thermal limits. We compiled data from eBird, a global citizen science initiative, to examine how 41 eastern North American birds shifted their occurrence and abundance patterns immediately following two recent extreme weather events each affecting > 2 million km2, the intrusion of a polar vortex and a winter heat wave. eBird data is continuously collected at high spatiotemporal resolution across large spatial extents, allowing us to compare species' responses immediately before and after these extreme events with trends in other winters across geographic scales. Overall, we found that birds responded differently to each extreme weather event. Bird occurrence rates did not change following the polar vortex, but where species occurred, population density was temporarily reduced, suggesting reductions in number of individuals driven by decreases in behavioral activity or temporary movement out of the area. However, birds demonstrated widespread increases in occurrence and increases in density and number of individuals where they occurred for at least 20 days after the heat wave, hinting at longer‐term range changes. Smaller‐bodied, warm‐adapted passerines tended to be most sensitive to extreme weather and responded most negatively to the polar vortex and most positively to the heat wave, while larger‐bodied, cold‐adapted waterbirds expressed only mild responses to either event. Thus, certain species may be exceptionally sensitive to extreme weather events while others are less sensitive. As climate change progresses and climatic variability increases, researchers and managers must better quantify the broad‐scale sensitivity of different species to multiple types of extreme weather events.

The temperature-dependence of many important mosquito-borne diseases has never been quantified. These relationships are critical for understanding current distributions and predicting future shifts from climate change. We used trait-based models to characterize temperature-dependent transmission of 10 vector–pathogen pairs of mosquitoes ( Culex pipiens , Cx. quinquefascsiatus , Cx. tarsalis , and others) and viruses (West Nile, Eastern and Western Equine Encephalitis, St. Louis Encephalitis, Sindbis, and Rift Valley Fever viruses), most with substantial transmission in temperate regions. Transmission is optimized at intermediate temperatures (23–26°C) and often has wider thermal breadths (due to cooler lower thermal limits) compared to pathogens with predominately tropical distributions (in previous studies). The incidence of human West Nile virus cases across US counties responded unimodally to average summer temperature and peaked at 24°C, matching model-predicted optima (24–25°C). Climate warming will likely shift transmission of these diseases, increasing it in cooler locations while decreasing it in warmer locations.

Humans are altering the distribution of species by changing the climate and disrupting biotic interactions and dispersal. A fundamental hypothesis in spatial ecology suggests that these effects are scale dependent; biotic interactions should shape distributions at local scales, whereas climate should dominate at regional scales. If so, common single-scale analyses might misestimate the impacts of anthropogenic modifications on biodiversity and the environment. However, large-scale datasets necessary to test these hypotheses have not been available until recently. Here we conduct a cross-continental, cross-scale (almost five orders of magnitude) analysis of the influence of biotic and abiotic processes and human population density on the distribution of three emerging pathogens: the amphibian chytrid fungus implicated in worldwide amphibian declines and West Nile virus and the bacterium that causes Lyme disease (Borrelia burgdorferi), which are responsible for ongoing human health crises. In all three systems, we show that biotic factors were significant predictors of pathogen distributions in multiple regression models only at local scales (∼10²–10³ km²), whereas climate and human population density always were significant only at relatively larger, regional scales (usually >10⁴ km²). Spatial autocorrelation analyses revealed that biotic factors were more variable at smaller scales, whereas climatic factors were more variable at larger scales, as is consistent with the prediction that factors should be important at the scales at which they vary the most. Finally, no single scale could detect the importance of all three categories of processes. These results highlight that common single-scale analyses can misrepresent the true impact of anthropogenic modifications on biodiversity and the environment.

[This corrects the article DOI: 10.1371/journal.pntd.0005568.].

Anthropogenic change is contributing to the rise in emerging infectious diseases, which are significantly correlated with socioeconomic, environmental and ecological factors 1 . Studies have shown that infectious disease risk is modified by changes to biodiversity 2 – 6 , climate change 7 – 11 , chemical pollution 12 – 14 , landscape transformations 15 – 20 and species introductions 21 . However, it remains unclear which global change drivers most increase disease and under what contexts. Here we amassed a dataset from the literature that contains 2,938 observations of infectious disease responses to global change drivers across 1,497 host–parasite combinations, including plant, animal and human hosts. We found that biodiversity loss, chemical pollution, climate change and introduced species are associated with increases in disease-related end points or harm, whereas urbanization is associated with decreases in disease end points. Natural biodiversity gradients, deforestation and forest fragmentation are comparatively unimportant or idiosyncratic as drivers of disease. Overall, these results are consistent across human and non-human diseases. Nevertheless, context-dependent effects of the global change drivers on disease were found to be common. The findings uncovered by this meta-analysis should help target disease management and surveillance efforts towards global change drivers that increase disease. Specifically, reducing greenhouse gas emissions, managing ecosystem health, and preventing biological invasions and biodiversity loss could help to reduce the burden of plant, animal and human diseases, especially when coupled with improvements to social and economic determinants of health. Reducing greenhouse gas emissions, managing ecosystem health, and preventing biological invasions and biodiversity loss could help to reduce the burden of plant, animal and human diseases, especially when coupled with improvements to social and economic determinants of health.

Aim Understanding and addressing the global biodiversity crisis requires ecological information compiled continuously from across the globe. Data from citizen science initiatives are useful for quantifying species' ecological niches and geographical distributions but can be difficult to apply towards biodiversity monitoring. The presence of fixed geographical locations reduces the opportunistic nature of citizen science data, allowing for more reliable and nuanced trend estimation. The eBird citizen‐science program contains predefined locations whose bird assemblages are sampled across years (‘hotspots’). For hotspots to function as a biodiversity monitoring resource, issues related to data coverage, biases, and trends need to be addressed. Location Global. Methods We estimated the survey completeness of species richness at 300,500 eBird hotspots during 2002–2022. We documented sampling biases at eBird hotspot and non‐hotspot locations during 2022 based on protection status, temperature, precipitation, and landcover. Results A total of 10,410 bird species (ca. 96.9% of total) were recorded at hotspots. The number of hotspots, checklists, and participants and the quality of species richness estimates increased worldwide with the Nearctic containing the strongest and most consistent trends. Compared to non‐hotspots, hotspots oversampled areas with higher protection status. Hotspots and non‐hotspots oversampled warmer and wetter locations in the Antarctic, Nearctic, and Palearctic, and cooler locations in the Afrotropics, Australasia, and the Neotropics. Hotspots and especially non‐hotspots oversampled urban areas. Hotspots and non‐hotspots undersampled shrublands in Australasia. Hotspots and especially non‐hotspots undersampled forests in the Afrotropics, Indomalaya, Neotropics, and Oceania. Main Conclusions Hotspots have captured a large component of the world's avian diversity but have done so inconsistently across space and time. Data quantity and quality are increasing in many regions, but the presence of regionally specific sampling biases and spatial uncertainty in hotspot locations should be addressed when applying the data.

Complex ecological relationships, such as host–parasite interactions, are often modeled with laboratory experiments. However, some experimental laboratory conditions, such as temperature or infection dose, are regularly chosen based on convenience or convention, and it is unclear how these decisions systematically affect experimental outcomes. Here, we conducted a meta-analysis of 58 laboratory studies that exposed amphibians to the pathogenic fungus Batrachochytrium dendrobatidis (Bd) to understand better how laboratory temperature, host life stage, infection dose, and host species affect host mortality. We found that host mortality was driven by thermal mismatches: hosts native to cooler environments experienced greater Bd-induced mortality at relatively warm experimental temperatures and vice versa. We also found that Bd dose positively predicted Bd-induced host mortality and that the superfamilies Bufonoidea and Hyloidea were especially susceptible to Bd. Finally, the effect of Bd on host mortality varied across host life stages, with larval amphibians experiencing lower risk of Bd-induced mortality than adults or metamorphs. Metamorphs were especially susceptible and experienced mortality when inoculated with much smaller Bd doses than the average dose used by researchers. Our results suggest that when designing experiments on species interactions, researchers should carefully consider the experimental temperature, inoculum dose, and life stage, and taxonomy of the host species.