1. Organisms have evolved diverse strategies to manage parasite infections. Broadly, hosts may avoid infection by altering behaviour, resist infection by targeting parasites, or tolerate infection by repairing associated damage. Effectiveness of a strategy depends on interactions between, e.g., resource availability, parasite traits (virulence, life-history) and the host itself (nutritional status, immunopathology). 2. To understand how these factors shape host parasite-mitigation strategies, we developed a mathematical model of within-host, parasite-immune dynamics in the context of helminth infections. The model incorporated host nutrition and resource allocation to different mechanisms of immune response: larval parasite prevention; adult parasite clearance; damage repair (tolerance). We also considered a non-immune strategy: avoidance via anorexia, reducing intake of infective stages. Resources not allocated to immune processes promoted host condition, whereas harm due to parasites and immunopathology diminished it. Maximising condition (a proxy for fitness), we determined optimal host investment for each parasite-mitigation strategy, singly and combined, across different environmental resource levels and parasite trait values. 3. Which strategy was optimal varied with scenario. Tolerance generally performed well, especially with high resources. Success of the different resistance strategies (larval prevention or adult clearance) tracked relative virulence of larval and adult parasites: slowly maturing, highly damaging larvae favoured prevention; rapidly maturing, less harmful larvae favoured clearance. Anorexia was viable only in the short-term, due to reduced host nutrition. Combined strategies always outperformed any lone strategy: these were dominated by tolerance, with some investment in resistance. 4. Choice of parasite mitigation strategy has profound consequences for hosts, impacting their condition, survival and reproductive success. We show the efficacy of different strategies is highly dependent on timescale, parasite traits and resource availability. Models that integrate such factors can inform the collection and interpretation of empirical data, to understand how those drivers interact to shape host immune responses in natural systems.

Contemporary rates of biodiversity decline emphasize the need for reliable ecological forecasting, but cur-rent methods vary in their ability to predict the declines of real-world populations. Acknowledging that stress acts at the individual level, and that it is the sum of these individual-level effects which drives popu-lations to collapse, shifts the focus of predictive ecology away from using predominantly abundance data. Doing so opens new opportunities to develop predictive frameworks which utilize increasingly available multi-dimensional data which have previously been overlooked for ecological forecasting. Using this ra-tional, we propose that stressed populations will exhibit a predictable sequence of detectable changes through time: (i) changes in individuals’ behaviour will occur as the first sign of increasing stress, followed by (ii) changes in fitness related morphological traits, (iii) shifts in the dynamics (e.g. birth rates) of popu-lations, and finally (iv) abundance declines. We discuss how monitoring the sequential appearance of these signals supplies information to discern whether a population becoming increasingly stressed risks collapse or is adapting in the face of environmental change. Such a timeline of signals provides a new framework to implement forecasting methods combining multidimensional data (e.g. behaviour, morphology, abun-dance) that may increase the ability to predict population collapse.

Phenotypic plasticity can mask population genetic differentiation, reducing the predictability of trait-environment relationships. In short-lived plants, reproductive traits may be more genetically determined due to their direct impact on fitness, whereas vegetative traits may show higher plasticity to buffer short-term perturbations. Combining a multi-treatment greenhouse experiment with global field observations for the short-lived Plantago lanceolata, we 1) disentangled the genetic and plastic responses of functional traits to a set of environmental drivers and 2) assessed the utility of trait-environment relationshisps inferred from observational data for predicting genetic differentiation. Reproductive traits showed distinct genetic differentiation that was highly predictable from observational data, but only when correcting traits for differences in their (labile) biomass component. Vegetative traits showed higher plasticity and contrasting genetic and plastic responses, leading to unpredictable trait patterns. Our study suggests that genetic differentiation may be inferred from observational data only for the traits most closely related with fitness.