Structural asymmetry in biotic interactions as a tool to understand and predict ecological persistence.

A universal feature of ecological systems is that species do not interact with others with the same sign and strength. Yet, the consequences of this asymmetry in biotic interactions for the short- and long-term persistence of individual species and entire communities remains unclear. Here, we develop a set of metrics to evaluate how asymmetric interactions among species translate to asymmetries in their individual vulnerability to extinction under changing environmental conditions. These metrics, which solve previous limitations of how to independently quantify the size from the shape of the so-called feasibility domain, provide rigorous advances to understand simultaneously why some species and communities present more opportunities to persist than others. We further demonstrate that our shape-related metrics are useful to predict short-term changes in species' relative abundances during 7 years in a Mediterranean grassland. Our approach is designed to be applied to any ecological system regardless of the number of species and type of interactions. With it, we show that is possible to obtain both mechanistic and predictive information on ecological persistence for individual species and entire communities, paving the way for a stronger integration of theoretical and empirical research.

Non-random interactions within and across guilds shape the potential to coexist in multi-trophic ecological communities.

Theory posits that the persistence of species in ecological communities is shaped by their interactions within and across trophic guilds. However, we lack empirical evaluations of how the structure, strength and sign of biotic interactions drive the potential to coexist in diverse multi-trophic communities. Here, we model community feasibility domains, a theoretically informed measure of multi-species coexistence probability, from grassland communities comprising more than 45 species on average from three trophic guilds (plants, pollinators and herbivores). Contrary to our hypothesis, increasing community complexity, measured either as the number of guilds or community richness, did not decrease community feasibility. Rather, we observed that high degrees of species self-regulation and niche partitioning allow for maintaining larger levels of community feasibility and higher species persistence in more diverse communities. Our results show that biotic interactions within and across guilds are not random in nature and both structures significantly contribute to maintaining multi-trophic diversity.

Pathways to global-change effects on biodiversity: new opportunities for dynamically forecasting demography and species interactions.

In structured populations, persistence under environmental change may be particularly threatened when abiotic factors simultaneously negatively affect survival and reproduction of several life cycle stages, as opposed to a single stage. Such effects can then be exacerbated when species interactions generate reciprocal feedbacks between the demographic rates of the different species. Despite the importance of such demographic feedbacks, forecasts that account for them are limited as individual-based data on interacting species are perceived to be essential for such mechanistic forecasting-but are rarely available. Here, we first review the current shortcomings in assessing demographic feedbacks in population and community dynamics. We then present an overview of advances in statistical tools that provide an opportunity to leverage population-level data on abundances of multiple species to infer stage-specific demography. Lastly, we showcase a state-of-the-art Bayesian method to infer and project stage-specific survival and reproduction for several interacting species in a Mediterranean shrub community. This case study shows that climate change threatens populations most strongly by changing the interaction effects of conspecific and heterospecific neighbours on both juvenile and adult survival. Thus, the repurposing of multi-species abundance data for mechanistic forecasting can substantially improve our understanding of emerging threats on biodiversity.

Fine scale prediction of ecological community composition using a two-step sequential Machine Learning ensemble.

Prediction is one of the last frontiers in ecology. Indeed, predicting fine-scale species composition in natural systems is a complex challenge as multiple abiotic and biotic processes operate simultaneously to determine local species abundances. On the one hand, species intrinsic performance and their tolerance limits to different abiotic pressures modulate species abundances. On the other hand, there is growing recognition that species interactions play an equally important role in limiting or promoting such abundances within ecological communities. Here, we present a joint effort between ecologists and data scientists to use data-driven models to predict species abundances using reasonably easy to obtain data. We propose a sequential data-driven modeling approach that in a first step predicts the potential species abundances based on abiotic variables, and in a second step uses these predictions to model the realized abundances once accounting for species competition. Using a curated data set over five years we predict fine-scale species abundances in a highly diverse annual plant community. Our models show a remarkable spatial predictive accuracy using only easy-to-measure variables in the field, yet such predictive power is lost when temporal dynamics are taken into account. This result suggests that predicting future abundances requires longer time series analysis to capture enough variability. In addition, we show that these data-driven models can also suggest how to improve mechanistic models by adding missing variables that affect species performance such as particular soil conditions (e.g. carbonate availability in our case). Robust models for predicting fine-scale species composition informed by the mechanistic understanding of the underlying abiotic and biotic processes can be a pivotal tool for conservation, especially given the human-induced rapid environmental changes we are experiencing. This objective can be achieved by promoting the knowledge gained with classic modelling approaches in ecology and recently developed data-driven models.

Discriminating climate, land-cover and random effects on species range dynamics.

Species are reportedly shifting their distributions poleward and upward in several parts of the world in response to climate change. The extent to which other factors might play a role driving these changes is still unclear. Land-cover change is a major cause of distributional changes, but it cannot be discarded that distributional dynamics might be at times caused by other mechanisms (e.g. dispersal, ecological drift). Using observed changes in the distribution of 82 breeding birds in Great Britain between three time periods 1968-72 (t1 ), 1988-91 (t2 ) and 2007-2011 (t3 ), we examine whether observed bird range shifts between t1 -t2 and t1 -t3 are best explained by climate change or land-cover change, or whether they are not distinguishable from what would be expected by chance. We found that range shifts across the rear edge of northerly distributed species in Great Britain are best explained by climate change, while shifts across the leading edge of southerly distributed species are best explained by changes in land-cover. In contrast, at the northern and southern edges of Great Britain, range dynamics could not be distinguished from that expected by chance. The latter observation could be a consequence of boundary effects limiting the direction and magnitude of range changes, stochastic demographic mechanisms neither associated with climate nor land-cover change or with complex interactions among factors. Our results reinforce the view that comprehensive assessments of climate change effects on species range shifts need to examine alternative drivers of change on equal footing and that null models can help assess whether observed patterns could have arisen by chance alone.

The effect of multiple biotic interaction types on species persistence.

No species can persist in isolation from other species, but how biotic interactions affect species persistence is still a matter of inquiry. Is persistence more likely in communities with higher proportion of competing species, or in communities with more positive interactions? How do different components of community structure mediate this relationship? We address these questions using a novel simulation framework that generates realistic communities with varying numbers of species and different proportions of biotic interaction types within and across trophic levels. We show that when communities have fewer species, persistence is more likely if positive interactions-such as mutualism and commensalism-are prevalent. In species-rich communities, the disproportionate effect of positive interactions on persistence is diluted and different combinations of biotic interaction types can coexist without affecting persistence significantly. We present the first theoretical examination of how multiple-interaction networks with varying architectures relate to local species persistence, and provide insight about the underlying causes of stability in communities.

The propagation of disturbances in ecological networks.

Despite the development of network science, we lack clear heuristics for how far different disturbance types propagate within and across species interaction networks. We discuss the mechanisms of disturbance propagation in ecological networks, and propose that disturbances can be categorized into structural, functional, and transmission types according to their spread and effect on network structure and functioning. We describe the properties of species and their interaction networks and metanetworks that determine the indirect, spatial, and temporal extent of propagation. We argue that the sampling scale of ecological studies may have impeded predictions regarding the rate and extent that a disturbance spreads, and discuss directions to help ecologists to move towards a predictive understanding of the propagation of impacts across interacting communities and ecosystems.

Addressing Burnout in Urology: A Qualitative Assessment of Interventions.

INTRODUCTION: We characterized physician burnout among urologists to determine the prevalence and efficacy of specific burnout interventions utilized and to determine involvement of workplaces in effective burnout interventions. METHODS: The Western Section of the American Urological Association created an electronic, 29 question workforce survey. Several questions focused on assessing the level of urologist burnout, prevalence of work sponsored burnout interventions and efficacy of specific interventions. RESULTS: A total of 440 responses were received (25.9% response rate); 82.2% of responders were male. The majority of urologists noted some level of burnout (79.5%) with no significant difference between those who reported no burnout vs some level of burnout (p=0.30). The most commonly tried interventions to reduce burnout were participating in regular physical exercise (76.6%), reading nonmedical literature (67.1%) and decreasing or modifying work hours (52.3%). The interventions most frequently cited as "very effective" were hiring a scribe (62.5%), regular exercise (56.1%) and participating in 1-on-1 gatherings with colleagues outside of work (44.6%). There were no significant differences noted when comparing "very effective" interventions by gender. The interventions most frequently cited as not effective were stress or burnout seminars (26.9%) and meditation/mindfulness training (11.5%); 42.5% reported workplace interventions to help prevent or reduce burnout. CONCLUSIONS: Certain practice-changing and personal burnout interventions were noted to be "very effective" in decreasing burnout. Fewer than half of responders noted workplace sponsorship of interventions. Organizational support may lead to increased participation and effectiveness of burnout interventions.

The spatial configuration of biotic interactions shapes coexistence-area relationships in an annual plant community.

The increase of species richness with area is a universal phenomenon on Earth. However, this observation contrasts with our poor understanding of how these species-area relationships (SARs) emerge from the collective effects of area, spatial heterogeneity, and local interactions. By combining a structuralist approach with five years of empirical observations in a highly-diverse Mediterranean grassland, we show that spatial heterogeneity plays a little role in the accumulation of species richness with area in our system. Instead, as we increase the sampled area more species combinations are realized, and they coexist mainly due to direct pairwise interactions rather than by changes in single-species dominance or by indirect interactions. We also identify a small set of transient species with small population sizes that are consistently found across spatial scales. These findings empirically support the importance of the architecture of species interactions together with stochastic events for driving coexistence- and species-area relationships.

Spatial trophic cascades in communities connected by dispersal and foraging.

Pairwise interactions between species have both direct and indirect consequences that reverberate throughout the whole ecosystem. In particular, interaction effects may propagate in a spatial dimension, to localities connected by organismal movement. Here we study the propagation of interaction effects with a spatially explicit metacommunity model, where local sites are connected by dispersal, foraging, or by both types of movement. We show that indirect pairwise effects are, in most cases, of the same sign as direct effects if localities are connected by dispersing species. However, if foraging is prevalent, this correspondence is broken, and indirect effects between species often have a different sign than direct effects. This highlights the importance of indirect interactions across space and their inherent unpredictability in complex settings with species foraging across local patches. Further, the effect of a species over another in a local patch does not necessarily correspond to its effect at the metacommunity scale; this correspondence is again mediated by the type of movement across localities. Every species, despite their trophic position or spatial range, displays a non-zero net effect over every other species in our model metacommunities. Thus we show that local dynamics and local interactions between species can trigger indirect effects all across the set of connected patches, and these effects have a distinct signature depending on whether the prevalent connection between patches is via dispersal or via foraging. However, the magnitude of this effect between any two species strongly decays with the distance between them. These theoretical results strengthen the importance of considering indirect effects across species at both the community and metacommunity levels, highlight the differences between types of movement across locations, and thus open novel avenues for the study of interaction effects in spatially explicit settings.

Small-Molecule Activators of Protein Phosphatase 2A for the Treatment of Castration-Resistant Prostate Cancer.

Primary prostate cancer is generally treatable by androgen deprivation therapy, however, later recurrences of castrate-resistant prostate cancer (CRPC) that are more difficult to treat nearly always occur due to aberrant reactivation of the androgen receptor (AR). In this study, we report that CRPC cells are particularly sensitive to the growth-inhibitory effects of reengineered tricyclic sulfonamides, a class of molecules that activate the protein phosphatase PP2A, which inhibits multiple oncogenic signaling pathways. Treatment of CRPC cells with small-molecule activators of PP2A (SMAP) in vitro decreased cellular viability and clonogenicity and induced apoptosis. SMAP treatment also induced an array of significant changes in the phosphoproteome, including most notably dephosphorylation of full-length and truncated isoforms of the AR and downregulation of its regulatory kinases in a dose-dependent and time-dependent manner. In murine xenograft models of human CRPC, the potent compound SMAP-2 exhibited efficacy comparable with enzalutamide in inhibiting tumor formation. Overall, our results provide a preclinical proof of concept for the efficacy of SMAP in AR degradation and CRPC treatment.Significance: A novel class of small-molecule activators of the tumor suppressor PP2A, a serine/threonine phosphatase that inhibits many oncogenic signaling pathways, is shown to deregulate the phosphoproteome and to destabilize the androgen receptor in advanced prostate cancer. Cancer Res; 78(8); 2065-80. ©2018 AACR.

Long-Term Efficacy and Safety of Insulin and Glucokinase Gene Therapy for Diabetes: 8-Year Follow-Up in Dogs.

Diabetes is a complex metabolic disease that exposes patients to the deleterious effects of hyperglycemia on various organs. Achievement of normoglycemia with exogenous insulin treatment requires the use of high doses of hormone, which increases the risk of life-threatening hypoglycemic episodes. We developed a gene therapy approach to control diabetic hyperglycemia based on co-expression of the insulin and glucokinase genes in skeletal muscle. Previous studies proved the feasibility of gene delivery to large diabetic animals with adeno-associated viral (AAV) vectors. Here, we report the long-term (∼8 years) follow-up after a single administration of therapeutic vectors to diabetic dogs. Successful, multi-year control of glycemia was achieved without the need of supplementation with exogenous insulin. Metabolic correction was demonstrated through normalization of serum levels of fructosamine, triglycerides, and cholesterol and remarkable improvement in the response to an oral glucose challenge. The persistence of vector genomes and therapeutic transgene expression years after vector delivery was documented in multiple samples from treated muscles, which showed normal morphology. Thus, this study demonstrates the long-term efficacy and safety of insulin and glucokinase gene transfer in large animals and especially the ability of the system to respond to the changes in metabolic needs as animals grow older.

Nonviral-mediated hepatic expression of IGF-I increases Treg levels and suppresses autoimmune diabetes in mice.

In type 1 diabetes, loss of tolerance to ß-cell antigens results in T-cell-dependent autoimmune destruction of ß cells. The abrogation of autoreactive T-cell responses is a prerequisite to achieve long-lasting correction of the disease. The liver has unique immunomodulatory properties and hepatic gene transfer results in tolerance induction and suppression of autoimmune diseases, in part by regulatory T-cell (Treg) activation. Hence, the liver could be manipulated to treat or prevent diabetes onset through expression of key genes. IGF-I may be an immunomodulatory candidate because it prevents autoimmune diabetes when expressed in ß cells or subcutaneously injected. Here, we demonstrate that transient, plasmid-derived IGF-I expression in mouse liver suppressed autoimmune diabetes progression. Suppression was associated with decreased islet inflammation and ß-cell apoptosis, increased ß-cell replication, and normalized ß-cell mass. Permanent protection depended on exogenous IGF-I expression in liver nonparenchymal cells and was associated with increased percentage of intrapancreatic Tregs. Importantly, Treg depletion completely abolished IGF-I-mediated protection confirming the therapeutic potential of these cells in autoimmune diabetes. This study demonstrates that a nonviral gene therapy combining the immunological properties of the liver and IGF-I could be beneficial in the treatment of the disease.

Treatment of diabetes and long-term survival after insulin and glucokinase gene therapy.

Diabetes is associated with severe secondary complications, largely caused by poor glycemic control. Treatment with exogenous insulin fails to prevent these complications completely, leading to significant morbidity and mortality. We previously demonstrated that it is possible to generate a "glucose sensor" in skeletal muscle through coexpression of glucokinase and insulin, increasing glucose uptake and correcting hyperglycemia in diabetic mice. Here, we demonstrate long-term efficacy of this approach in a large animal model of diabetes. A one-time intramuscular administration of adeno-associated viral vectors of serotype 1 encoding for glucokinase and insulin in diabetic dogs resulted in normalization of fasting glycemia, accelerated disposal of glucose after oral challenge, and no episodes of hypoglycemia during exercise for >4 years after gene transfer. This was associated with recovery of body weight, reduced glycosylated plasma proteins levels, and long-term survival without secondary complications. Conversely, exogenous insulin or gene transfer for insulin or glucokinase alone failed to achieve complete correction of diabetes, indicating that the synergistic action of insulin and glucokinase is needed for full therapeutic effect. This study provides the first proof-of-concept in a large animal model for a gene transfer approach to treat diabetes.