Search

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.

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.

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

Brief introduction: What are microclimates and why are they important? Microclimate science has developed into a global discipline. Microclimate science is increasingly used to understand and mitigate climate and biodiversity shifts. Here, we provide an overview of the current status of microclimate ecology and biogeography in terrestrial ecosystems, and where this field is heading next. Microclimate investigations in ecology and biogeography: We highlight the latest research on interactions between microclimates and organisms, including how microclimates influence individuals, and through them populations, communities and entire ecosystems and their processes. We also briefly discuss recent research on how organisms shape microclimates from the tropics to the poles. Microclimate applications in ecosystem management: Microclimates are also important in ecosystem management under climate change. We showcase new research in microclimate management with examples from biodiversity conservation, forestry and urban ecology. We discuss the importance of microrefugia in conservation and how to promote microclimate heterogeneity. Methods for microclimate science: We showcase the recent advances in data acquisition, such as novel field sensors and remote sensing methods. We discuss microclimate modelling, mapping and data processing, including accessibility of modelling tools, advantages of mechanistic and statistical modelling and solutions for computational challenges that have pushed the state-of-the-art of the field. What's next? We identify major knowledge gaps that need to be filled for further advancing microclimate investigations, applications and methods. These gaps include spatiotemporal scaling of microclimate data, mismatches between macroclimate and microclimate in predicting responses of organisms to climate change, and the need for more evidence on the outcomes of microclimate management.

Conventional wisdom states that genetic variation reduces disease levels in plant populations. Nevertheless, crop species have been subject to a gradual loss of genetic variation through selection for specific traits during breeding, thereby increasing their vulnerability to biotic stresses such as pathogens. We explored how genetic variation in Arabica coffee sites in southwestern Ethiopia was related to the incidence of four major fungal diseases. Sixty sites were selected along a gradient of management intensity, ranging from nearly wild to intensively managed coffee stands. We used genotyping-by-sequencing of pooled leaf samples (pool-GBS) derived from 16 individual coffee shrubs in each of the 60 sites to assess the variation in genetic composition (multivariate: reference allele frequency) and genetic diversity (univariate: mean expected heterozygosity) between sites. We found that genetic composition had a clear spatial pattern and that genetic diversity was higher in less managed sites. The incidence of the four fungal diseases was related to the genetic composition of the coffee stands, but in a specific way for each disease. In contrast, genetic diversity was only related to the within-site variation of coffee berry disease, but not to the mean incidence of any of the four diseases across sites. Given that fungal diseases are major challenges of Arabica coffee in its native range, our findings that genetic composition of coffee sites impacted the major fungal diseases may serve as baseline information to study the molecular basis of disease resistance in coffee. Overall, our study illustrates the need to consider both host genetic composition and genetic diversity when investigating the genetic basis for variation in disease levels.