Losses and gains in species diversity affect ecological stability 1-7 and the sustainability of ecosystem functions and services 8-13. Experiments and models reveal positive, negative, and no effects of diversity on individual components of stability such as temporal variability, resistance, and resilience 2,3,6,11,12,14. How these stability components covary is poorly appreciated 15 , as are diversity effects on overall ecosystem stability 16 , conceptually akin to ecosystem multifunctionality 17,18. We observed how temporal variability, resistance, and overall ecosystem stability responded to diversity (i.e. species richness) in a large experiment involving 690 micro-ecosystems sampled 19 times over 40 days, resulting in 12939 samplings. Species richness increased temporal stability but decreased resistance to warming. Thus, two stability components negatively covaried along the diversity gradient. Previous biodiversity manipulation studies rarely reported such negative covariation despite general predictions of negative effects of diversity on individual stability components 3. Integrating our findings with the ecosystem multifunctionality concept revealed hump-and U-shaped effects of diversity on overall ecosystem stability. That is, biodiversity can increase overall ecosystem stability when biodiversity is low, and decrease it when biodiversity is high, or the opposite with a Ushaped relationship. Effects of diversity on ecosystem multifunctionality would also be hump-or U-shaped if diversity has positive effects on some functions and negative effects on others. Linking the ecosystem multifunctionality concept and ecosystem stability can transform perceived effects of diversity on ecological stability and may assist translation of this science into policy-relevant information. Ecological stability consists of numerous components including temporal variability, resistance to environmental change, and rate of recovery from disturbance 1,2,16. Effects of species losses and gains on these components are of considerable interest, not least due to potential effects on ecosystem functioning and hence the sustainable delivery of ecosystem services 1-13. A growing number of experimental studies reveal stabilising effects of diversity on individual stability components. In particular, higher diversity often, but not always, reduces temporal variability of biomass production 13. Positive effects of diversity on resistance are common, though neutral and negative effects on resistance and resilience also occur 9,13,19,20. While assessment of individual stability components is essential, a more integrative approach to ecological stability could lead to clearer conceptual understanding 15 and might improve policy guidance concerning ecological stability 16. Analogous to ecosystem multifunctionality 17,18 , a more integrative approach considers variation in multiple stability components, and the often-ignored covariation among stability components. The nature of this covariation is of paramount importance, as it defines whe...

The recent description of potentially generic early warning signals is a promising development that may help conservationists to anticipate a population's collapse prior to its occurrence. So far, the majority of such warning signals documented have been in highly controlled laboratory systems or theoretical models. Data from wild populations, however, are typically restricted both temporally and spatially due to limited monitoring resources and intrinsic ecological heterogeneity -limitations that may affect the detectability of generic early warning signals, as they add additional stochasticity to population abundance estimates. Consequently, spatial and temporal subsampling may serve either to muffle or magnify early warning signals. Using a combination of theoretical models and analysis of experimental data, we evaluate the extent to which statistical warning signs are robust to data corruption. Online enhancements: appendixes, zip file.abstract: The recent description of potentially generic early warning signals is a promising development that may help conservationists to anticipate a population's collapse prior to its occurrence. So far, the majority of such warning signals documented have been in highly controlled laboratory systems or in theoretical models. Data from wild populations, however, are typically restricted both temporally and spatially due to limited monitoring resources and intrinsic ecological heterogeneity-limitations that may affect the detectability of generic early warning signals, as they add additional stochasticity to population abundance estimates. Consequently, spatial and temporal subsampling may serve to either muffle or magnify early warning signals. Using a combination of theoretical models and analysis of experimental data, we evaluate the extent to which statistical warning signs are robust to data corruption.

The extent to which immune cell phenotypes in the peripheral blood reflect within-tumor immune activity prior to and early in cancer therapy is unclear. To address this question, we studied the population dynamics of tumor and immune cells, and immune phenotypic changes, using clinical tumor and immune cell measurements and single-cell genomic analyses. These samples were serially obtained from a cohort of advanced gastrointestinal cancer patients enrolled in a trial with chemotherapy and immunotherapy. Using an ecological population model, fitted to clinical tumor burden and immune cell abundance data from each patient, we find evidence of a strong tumor-circulating immune cell interaction in responder patients but not in those patients that progress on treatment. Upon initiation of therapy, immune cell abundance increased rapidly in responsive patients, and once the peak level is reached tumor burden decreases, similar to models of predator–prey interactions; these dynamic patterns were absent in nonresponder patients. To interrogate phenotype dynamics of circulating immune cells, we performed single-cell RNA sequencing at serial time points during treatment. These data show that peripheral immune cell phenotypes were linked to the increased strength of patients’ tumor–immune cell interaction, including increased cytotoxic differentiation and strong activation of interferon signaling in peripheral T cells in responder patients. Joint modeling of clinical and genomic data highlights the interactions between tumor and immune cell populations and reveals how variation in patient responsiveness can be explained by differences in peripheral immune cell signaling and differentiation soon after the initiation of immunotherapy.

Combining cyclin-dependent kinase (CDK) inhibitors with endocrine therapy improves outcomes for metastatic estrogen receptor positive (ER+) breast cancer patients but its value in earlier stage patients is unclear. We examined evolutionary trajectories of early-stage breast cancer tumors, using single cell RNA sequencing (scRNAseq) of serial biopsies from the FELINE clinical trial ( #NCT02712723 ) of endocrine therapy (letrozole) alone or combined with the CDK inhibitor ribociclib. Despite differences in subclonal diversity evolution across patients and treatments, common resistance phenotypes emerged. Resistant tumors treated with combination therapy showed accelerated loss of estrogen signaling with convergent up-regulation of JNK signaling through growth factor receptors. In contrast, cancer cells maintaining estrogen signaling during mono- or combination therapy showed potentiation of CDK4/6 activation and ERK upregulation through ERBB4 signaling. These results indicate that combination therapy in early-stage ER+ breast cancer leads to emergence of resistance through a shift from estrogen to alternative growth signal-mediated proliferation.

Biotic interactions are central to both ecological and evolutionary dynamics. In the vast majority of empirical studies, the strength of intraspecific interactions is estimated by using simple measures of population size. Biologists have long known that these are crude metrics, with experiments and theory suggesting that interactions between individuals should depend on traits, such as body size. Despite this, it has been difficult to estimate the impact of traits on competitive ability from ecological field data, and this explains why the strength of biotic interactions has empirically been treated in a simplistic manner. Using long-term observational data from four different populations, we show that large Trinidadian guppies impose a significantly larger competitive pressure on conspecifics than individuals that are smaller; in other words, competition is asymmetric. When we incorporate this asymmetry into integral projection models, the predicted size structure is much closer to what we see in the field compared with models where competition is independent of body size. This difference in size structure translates into a twofold difference in reproductive output. This demonstrates how the nature of ecological interactions drives the size structure, which, in turn, will have important implications for both the ecological and evolutionary dynamics.

The development of video-based monitoring methods allows for rapid, dynamic and accurate monitoring of individuals or communities, compared to slower traditional methods, with far reaching ecological and evolutionary applications. Large amounts of data are generated using video-based methods, which can be effectively processed using machine learning (ML) algorithms into meaningful ecological information. ML uses user defined classes (e.g. species), derived from a subset (i.e. training data) of video-observed quantitative features (e.g. phenotypic variation), to infer classes in subsequent observations. However, phenotypic variation often changes due to environmental conditions, which may lead to poor classification, if environmentally induced variation in phenotypes is not accounted for. Here we describe a framework for classifying species under changing environmental conditions based on the random forest classification. A sliding window approach was developed that restricts temporal and environmentally conditions to improve the classification. We tested our approach by applying the classification framework to experimental data. The experiment used a set of six ciliate species to monitor changes in community structure and behavior over hundreds of generations, in dozens of species combinations and across a temperature gradient. Differences in biotic and abiotic conditions caused simplistic classification approaches to be unsuccessful. In contrast, the sliding window approach allowed classification to be highly successful, as phenotypic differences driven by environmental change, could be captured by the classifier. Importantly, classification using the random forest algorithm showed comparable success when validated against traditional, slower, manual identification. Our framework allows for reliable classification in dynamic environments, and may help to improve strategies for long-term monitoring of species in changing environments. Our classification pipeline can be applied in fields assessing species community dynamics, such as eco-toxicology, ecology and evolutionary ecology.

42 43One sentence summary: Peripheral immune cell differentiation and signaling, upon initiation of 44 immunotherapy, reflects tumor attacking ability and patient response. 45 46 47 Circulating immune cell phenotype dynamics Page 2 of 18 Significance statement 48 The evolution of peripheral immune cell abundance and signaling over time, as well as 49 how these immune cells interact with the tumor, may impact a cancer patient's response to 50 therapy. By developing an ecological population model, we provide evidence of a dynamic 51 predator-prey like relationship between circulating immune cell abundance and tumor size in 52 patients that respond to immunotherapy. This relationship is not found either in patients that are 53 non-responsive to immunotherapy or during chemotherapy. Single cell RNA-sequencing 54 (scRNAseq) of serial peripheral blood samples from patients show that the strength of tumor-55 immune cell interactions is reflected in T-cells interferon activation and differentiation early in 56 treatment. Thus, circulating immune cell dynamics reflect a tumor's response to immunotherapy. 57 58 Abstract 59 The extent that immune cell phenotypes in the peripheral blood reflect within-tumor 60 immune activity prior to and early in cancer therapy is unclear. To address this question, we 61 studied the population dynamics of tumor and immune cells, and immune phenotypic changes, 62 using clinical tumor and immune cell measurements and single cell genomic analyses. These 63 samples were serially obtained from a cohort of advanced gastrointestinal cancer patients enrolled 64 on a trial with chemotherapy and immunotherapy. Using an ecological population model, fitted to 65 clinical tumor burden and immune cell abundance data from each patient, we find evidence of a 66 strong tumor-circulating immune cell interaction in responder patients, but not those patients that 67 progress on treatment. Upon initiation of therapy, immune cell abundance increased rapidly in 68 responsive patients, and once the peak level is reached, tumor burden decreases, similar to models 69 of predator-prey interactions; these dynamic patterns were absent in non-responder patients. To 70 interrogate phenotype dynamics of circulating immune cells, we performed single cell RNA 71 sequencing at serial time points during treatment. These data show that peripheral immune cell 72 phenotypes were linked to the increased strength of patients' tumor-immune cell interaction, 73 including increased cytotoxic differentiation and strong activation of interferon signaling in 74 peripheral T-cells in responder patients. Joint modeling of clinical and genomic data highlights 75 the interactions between tumor and immune cell populations and reveals how variation in patient 76 responsiveness can be explained by differences in peripheral immune cell signaling and 77 differentiation soon after the initiation of immunotherapy. 78 79

Abstract1. Intraspecific trait change, including altered behaviour or morphology, can drive temporal variation in interspecific interactions and population dynamics. In turn, variation in species' interactions and densities can alter the strength and direction of trait change. The resulting feedback between species' traits and abundance permits a wide range of community dynamics that would not be expected from ecological theories purely based on species abundances. Despite the theoretical importance of these interrelated processes, unambiguous experimental evidence of how intraspecific trait variation modifies species interactions and population dynamics and how this feeds back to influence trait variation is currently required.2. We investigate the role of trait-mediated demography in determining community dynamics and examine how ecological interactions influence trait change. We concurrently monitored the dynamics of community abundances and individual traits in an experimental microbial predator-prey-resource system. Using this data, we parameterised a trait-dependent community model to identify key ecologically relevant traits and to link trait dynamics with those of species abundances.3. Our results provide clear evidence of a feedback between trait change, demographic rates and species dynamics. The inclusion of trait-abundance feedbacks into our population model improved the predictability of ecological dynamics from r 2 of 34% to 57% and confirmed theoretical expectations of density-dependent population growth and species interactions in the system. 4. Additionally, our model revealed that the feedbacks were underpinned by a tradeoff between population growth and anti-predatory defence. High predator abundance was linked to a reduction in prey body size. This prey size decrease was associated with a reduction in its rate of consumption by predators and a decrease in its resource consumption.5. Modelling trait-abundance feedbacks allowed us to pinpoint the underlying life history trade-off which links trait and abundance dynamics. These results show that accounting for trait-abundance feedbacks has the potential to improve understanding and predictability of ecological dynamics. | 497Functional Ecology GRIFFITHS eT al. | INTRODUCTIONTrait variation within species is increasingly recognised as having important impacts on the population dynamics of natural communities (Berg & Ellers, 2010;Schoener, 2011). Such variation can be driven by evolutionary selection pressures favouring certain heritable traits (Kasada, Yamamichi, & Yoshida, 2014;Thompson, 1998;Yoshida, Hairston, & Ellner, 2004). Alternatively, trait variation can be caused by phenotypic plasticity, when a single genotype produces different phenotypes under differing environments (Agrawal, 2001;Cortez, 2011;Fordyce, 2006;Tollrian & Harvell, 1999). For example, the timing of life history events or the allocation of resources to growth and defence may depend on the density of predators and resources and on environmental conditions (Finlay, 1977;Lam...