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Aim: The scale of environmental data is often defined by their extent (spatial area, temporal duration) and resolution (grain size, temporal interval). Although describing climate data scale via these terms is appropriate for most meteorological applications, for ecology and biogeography, climate data of the same spatiotemporal resolution and extent may differ in their relevance to an organism. Here, we propose that climate proximity, or how well climate data represent the actual conditions that an organism is exposed to, is more important for ecological realism than the spatiotemporal resolution of the climate data. Location: Temperature comparison in nine countries across four continents; ecological case studies in Alberta (Canada), Sabah (Malaysia) and North Carolina/Tennessee (USA). Time Period: 1960–2018. Major Taxa Studied: Case studies with flies, mosquitoes and salamanders, but concepts relevant to all life on earth. Methods: We compare the accuracy of two macroclimate data sources (ERA5 and WorldClim) and a novel microclimate model (microclimf) in predicting soil temperatures. We then use ERA5, WorldClim and microclimf to drive ecological models in three case studies: temporal (fly phenology), spatial (mosquito thermal suitability) and spatiotemporal (salamander range shifts) ecological responses. Results: For predicting soil temperatures, microclimf had 24.9% and 16.4% lower absolute bias than ERA5 and WorldClim respectively. Across the case studies, we find that increasing proximity (from macroclimate to microclimate) yields a 247% improvement in performance of ecological models on average, compared to 18% and 9% improvements from increasing spatial resolution 20‐fold, and temporal resolution 30‐fold respectively. Main Conclusions: We propose that increasing climate proximity, even if at the sacrifice of finer climate spatiotemporal resolution, may improve ecological predictions. We emphasize biophysically informed approaches, rather than generic formulations, when quantifying ecoclimatic relationships. Redefining the scale of climate through the lens of the organism itself helps reveal mechanisms underlying how climate shapes ecological systems. [ABSTRACT FROM AUTHOR]

Climate change has triggered poleward expansions in the distributions of various taxonomic groups, including tree species. Given the ecological significance of trees as keystone species in forests and their socio‐economic importance, projecting the potential future distributions of tree species is crucial for devising effective adaptation strategies for both biomass production and biodiversity conservation in future forest ecosystems. Here, we fitted physiographically informed habitat suitability models (HSMs) at 50‐m resolution across Sweden (55–68° N) to estimate the potential northward expansion of seven broadleaved tree species within their leading‐edge distributions in Europe under different future climate change scenarios and for different time periods. Overall, we observed that minimum temperature was the most crucial variable for comprehending the spatial distribution of broadleaved tree species at their cold limits. Our HSMs projected a complex range expansion pattern for 2100, with individualistic differences among species. However, a frequent and rather surprising pattern was a northward expansion along the east coast followed by narrow migration pathways along larger valleys towards edaphically suitable areas in the north‐west, where most of the studied species were predicted to expand. The high‐resolution maps generated in this study offer valuable insights for our understanding of range shift dynamics at the leading edge of southern tree species as they expand into the receding boreal biome. These maps suggest areas where broadleaved tree species could already be translocated to anticipate forest and biodiversity conservation adaptation efforts in the face of future climate change. [ABSTRACT FROM AUTHOR]

Societal Impact Statement: Adapting agriculture to climate change requires an understanding of the long‐term relationship between climate, disease dynamics, and yield. While some countries have monitored major crop diseases for decades or centuries, comparable data is scarce or non‐existent for many countries that are most vulnerable to climate change. For this, a novel approach was developed to reconstruct climate‐mediated changes in disease dynamics and yield. Here, a case study on Arabica coffee in its area of origin demonstrates how to combine local knowledge, climate data, and spatial field surveys to reconstruct disease and yield time series and to postulate and test hypotheses for climate–disease–yield relationships. Summary: While some countries have monitored crop diseases for several decades or centuries, other countries have very limited historical time series. In such areas, we lack data on long‐term patterns and drivers of disease dynamics, which is important for developing climate‐resilient disease management strategies.We adopted a novel approach, combining local knowledge, climate data, and spatial field surveys to understand long‐term climate‐mediated changes in disease dynamics in coffee agroforestry systems. For this, we worked with 58 smallholder farmers in southwestern Ethiopia, the area of origin of Arabica coffee.The majority of farmers perceived an increase in coffee leaf rust and a decrease in coffee berry disease, whereas perceptions of changes in coffee wilt disease and Armillaria root rot were highly variable among farmers. Climate data supported farmers' understanding of the climatic drivers (increased temperature, less rainy days) of these changes. Temporal disease‐climate relationships were matched by spatial disease‐climate relationships, as expected with space‐for‐time substitution.Understanding long‐term disease dynamics and yield is crucial to adapt disease management to climate change. Our study demonstrates how to combine local knowledge, climate data and spatial field surveys to reconstruct disease time series and postulate hypotheses for disease‐climate relationships in areas where few long‐term time series exist. [ABSTRACT FROM AUTHOR]

Extreme droughts are globally increasing in frequency and severity. Most research on drought in forests focuses on the response of trees, while less is known about the impacts of drought on forest understory species and how these effects are moderated by the local environment. We assessed the impacts of a 45‐day experimental summer drought on the performance of six boreal forest understory plants, using a transplant experiment with rainout shelters replicated across 25 sites. We recorded growth, vitality and reproduction immediately, 2 months, and 1 year after the simulated drought, and examined how differences in ambient soil moisture and canopy cover among sites influenced the effects of drought on the performance of each species. Drought negatively affected the growth and/or vitality of all species, but the effects were stronger and more persistent in the bryophytes than in the vascular plants. The two species associated with older forests, the moss Hylocomiastrum umbratum and the orchid Goodyera repens, suffered larger effects than the more generalist species included in the experiment. The drought reduced reproductive output in the moss Hylocomium splendens in the next growing season, but increased reproduction in the graminoid Luzula pilosa. Higher ambient soil moisture reduced some negative effects of drought on vascular plants. Both denser canopy cover and higher soil moisture alleviated drought effects on bryophytes, likely through alleviating cellular damage. Our experiment shows that boreal understory species can be adversely affected by drought and that effects might be stronger for bryophytes and species associated with older forests. Our results indicate that the effects of drought can vary over small spatial scales and that forest landscapes can be actively managed to alleviate drought effects on boreal forest biodiversity. For example, by managing the tree canopy and protecting hydrological networks. [ABSTRACT FROM AUTHOR]

Natural enemies of plant pathogens might play an important role in controlling plant disease levels in natural and agricultural systems. Yet, plant pathogen–natural enemy interactions might be sensitive to climatic changes. Understanding the relationship between climate, plant pathogens and their natural enemies is thus important for developing climate‐resilient, sustainable agriculture.To this aim, we recorded shade cover, daily minimum and maximum temperature, relative humidity, coffee leaf rust and its hyperparasite at 58 sites in southwestern Ethiopia during the dry and wet season for 2 years.Coffee leaf rust severity was positively related to the maximum temperature. Hyperparasite severity was higher when the minimum temperature was low (i.e. in places with cold night temperatures). While canopy cover did not have a direct effect on rust severity, it reduced rust severity indirectly by lowering the maximum temperature. Canopy cover had a direct positive effect on the hyperparasite severity during one surveying period.Synthesis and applications. Our findings highlight that coffee leaf rust and its hyperparasite are both affected by shade cover and temperature, but in different ways. On the one hand, these niche differences lead to the worrying prediction that levels of coffee leaf rust will increase, and its hyperparasite will decrease, with climate change. On the other hand, these niche differences between coffee leaf rust and its hyperparasite provide opportunities to develop strategies to manage the environment (such as shade cover and microclimate) in such a way that the rust is disfavoured and the hyperparasite is favoured. [ABSTRACT FROM AUTHOR]

Climate is assumed to strongly influence species distribution and abundance. Although the performance of many organisms is influenced by the climate in their immediate proximity, the climate data used to model their distributions often have a coarse spatial resolution. This is problematic because the local climate experienced by individuals might deviate substantially from the regional average. This problem is likely to be particularly important for sessile organisms like plants and in environments where small‐scale variation in climate is large. To quantify the effect of local temperature on vital rates and population growth rates, we used temperature values measured at the local scale (in situ logger measures) and integral projection models with demographic data from 37 populations of the forest herb Lathyrus vernus across a wide latitudinal gradient in Sweden. To assess how the spatial resolution of temperature data influences assessments of climate effects, we compared effects from models using local data with models using regionally aggregated temperature data at several spatial resolutions (≥1 km). Using local temperature data, we found that spring frost reduced the asymptotic population growth rate in the first of two annual transitions and influenced survival in both transitions. Only one of the four regional estimates showed a similar negative effect of spring frost on population growth rate. Our results for a perennial forest herb show that analyses using regionally aggregated data often fail to identify the effects of climate on population dynamics. This emphasizes the importance of using organism‐relevant estimates of climate when examining effects on individual performance and population dynamics, as well as when modeling species distributions. For sessile organisms that experience the environment over small spatial scales, this will require climate data at high spatial resolutions. [ABSTRACT FROM AUTHOR]