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To make more informed predictions of host–pathogen interactions under climate change, studies have incorporated the thermal performance of host, vector and pathogen traits into disease models to quantify effects on average transmission rates. However, this body of work has omitted the fact that variation in susceptibility among individual hosts affects disease spread and long-term patterns of host population dynamics. Furthermore, and especially for ectothermic host species, variation in susceptibility is likely to be plastic, influenced by variables such as environmental temperature. For example, as host individuals respond idiosyncratically to temperature, this could affect the population-level variation in susceptibility, such that there may be predictable functional relationships between variation in susceptibility and temperature. Quantifying the relationship between temperature and among-host trait variation will therefore be critical for predicting how climate change and disease will interact to influence host–pathogen population dynamics. Here, we use a model to demonstrate how short-term effects of temperature on the distribution of host susceptibility can drive epidemic characteristics, fluctuations in host population sizes and probabilities of host extinction. Our results emphasize that more research is needed in disease ecology and climate biology to understand the mechanisms that shape individual trait variation, not just trait averages. 

Abstract Structure‐forming foundation species facilitate consumers by providing habitat and refugia. In return, consumers can benefit foundation species by reducing top‐down pressures and increasing the supply of nutrients. Consumer‐mediated nutrient dynamics (CND) fuel the growth of autotrophic foundation species and generate more habitat for consumers, forming reciprocal feedbacks. Such feedbacks are threatened when foundation species are lost to disturbances, yet testing these interactions requires long‐term studies, which are rare. Here, we experimentally evaluated how disturbance to giant kelp, a marine foundation species, affects (1) CND of the forest animal community and (2) nutrient feedbacks that help sustain forest primary production during extended periods of low nitrate. Our experiment involved removing giant kelp annually during the winter for 10 years at four sites to mimic frequent wave disturbance. We paired temporal changes in the forest community in kelp removal and control plots with estimates of taxon‐specific ammonium excretion rates (reef fishes and macroinvertebrates) and nitrogen (N) demand (giant kelp and understory macroalgae) to determine the effects of disturbance on CND as measured by ammonium excretion, N demand by kelp forest macroalgae, and the percentage of nitrogen demand met by ammonium excretion. We found that disturbance to giant kelp decreased ammonium excretion by 66% over the study, mostly due to declines in fishes. Apart from a few fish species that dominated CND, most reef‐associated consumers were unaffected by disturbance. Disturbance to giant kelp reduced its N demand by 56% but increased that of the understory by 147% due to its increased abundance in the absence of a kelp canopy. Overall, disturbance had little effect on the fraction of N demand of macroalgae met by consumer excretion due to the offsetting responses of giant kelp, understory macroalgae, and consumers to disturbance. Across both disturbance regimes, on average, consumers supported 11%–12% of the N required by all kelp forest macroalgae and 48% of N demand by the understory macroalgae (which are confined to the benthos where most reef‐associated consumers reside). Our findings suggest that CND constitutes a considerable contribution of N required in kelp forests, yet nutrient inputs decrease following reductions in essential habitat perpetuated by frequent disturbances. 

Off-road vehicle mobility assessments rely on fine-resolution (~10 m) estimates of soil moisture and strength across the region of interest. Such estimates are often produced by downscaling soil moisture from a microwave satellite like SMAP, then using the soil moisture in a soil strength model. Soil moisture downscaling methods typically assume consistent relationships between the moisture and topographic, vegetation, and soil composition characteristics within the microwave satellite grid cells. The objective of this study is to examine whether soil moisture and strength exhibit heterogenous dependencies on topography, vegetation, and soil composition characteristics within a SMAP grid cell. Soil moisture and strength data were collected at four geographically separated regions within a 9 km SMAP grid cell in the Front Range foothills of northern Colorado. Laboratory methods and pedotransfer functions were used to characterize soil attributes, and remote sensing data were used to determine topographic and vegetation attributes. Pearson correlation analyses were used to quantify the direction, strength, and significance of the relationships of both soil moisture and strength with topography, vegetation, and soil composition. Contrary to the common assumption, spatial variations in the slope and correlation of the relationships are observed for both soil moisture and strength. The findings indicate that improved predictions of soil moisture and soil strength may be achievable by soil moisture downscaling procedures that use spatially variable parameters across the downscaling extent. 

In this study, we consider three different machine‐learning methods—a three‐hidden‐layer neural network, support vector regression, and Gaussian process regression—and compare how well they can learn from a synthetic data set for proton acceleration in the Target Normal Sheath Acceleration regime. The synthetic data set was generated from a previously published theoretical model by Fuchs et al. 2005 that we modified. Once trained, these machine‐learning methods can assist with efforts to maximize the peak proton energy, or with the more general problem of configuring the laser system to produce a proton energy spectrum with desired characteristics. In our study, we focus on both the accuracy of the machine‐learning methods and the performance on one GPU including memory consumption. Although it is arguably the least sophisticated machine‐learning model we considered, support vector regression performed very well in our tests.