Seasonality in Environment and Population Processes Alters Population Spatial Synchrony.

AbstractPopulation spatial synchrony-the tendency for temporal population fluctuations to be correlated across locations-is common and important to metapopulation stability and persistence. One common cause of spatial synchrony, termed the Moran effect, occurs when populations respond to environmental fluctuations, such as weather, that are correlated over space. Although the degree of spatial synchrony in environmental fluctuations can differ between seasons and different population processes occur in different seasons, the impact on population spatial synchrony is uncertain because prior work has largely assumed that the spatial synchrony of environmental fluctuations and their effect on populations are consistent over annual sampling intervals. We used theoretical models to examine how seasonality in population processes and the spatial synchrony of environmental drivers affect population spatial synchrony. We found that population spatial synchrony can depend not only on the spatial synchrony of environmental drivers but also on the degree to which environmental fluctuations are correlated across seasons, locally, and across space. Moreover, measurements of synchrony from "snapshot" population censuses may not accurately reflect synchrony during other parts of the year. Together, these results show that neglecting seasonality in environmental conditions and population processes is consequential for understanding population spatial synchrony and its driving mechanisms.

Resistance and resilience to invasion is stronger in synchronous than compensatory communities.

While community synchrony is a key framework for predicting ecological constancy, the interplay between community synchrony and ecological invasions remains unclear. Yet the degree of synchrony in a resident community may influence its resistance and resilience to the introduction of an invasive species. Here we used a generalizable mathematical framework, constructed with a modified Lotka-Volterra competition model, to first simulate resident communities across a range of competitive strengths and species' responses to environmental fluctuations, which yielded communities that ranged from strongly synchronous to compensatory. We then invaded these communities at different timesteps with invaders of varying demographic traits, after which we quantified the resident community's susceptibility to initial invasion attempts (resistance) and the degree to which community synchrony was altered after invasion (resiliency of synchrony). We found that synchronous communities were not only more resistant but also more resilient to invasion than compensatory communities, likely due to stronger competition between resident species and thus lower cumulative abundances in compensatory communities, providing greater opportunities for invasion. The growth rate of the invader was most influenced by the resident and invader competition coefficients and the growth rate of the invader species. Our findings support prioritizing the conservation of compensatory and weakly synchronous communities which may be at increased risk of invasion.

Restoration ecology through the lens of coexistence theory.

Advances in restoration ecology are needed to guide ecological restoration in a variable and changing world. Coexistence theory provides a framework for how variability in environmental conditions and species interactions affects species success. Here, we conceptually link coexistence theory and restoration ecology. First, including low-density growth rates (LDGRs), a classic metric of coexistence, can improve abundance-based restoration goals, because abundances are sensitive to initial treatments and ongoing variability. Second, growth-rate partitioning, developed to identify coexistence mechanisms, can improve restoration practice by informing site selection and indicating necessary interventions (e.g., site amelioration or competitor removal). Finally, coexistence methods can improve restoration assessment, because initial growth rates indicate trajectories, average growth rates measure success, and growth partitioning highlights interventions needed in future.

Landscape-level variability and insect herbivore outbreak captured within modern forests provides a framework for interpreting the fossil record.

Temporal patterns of plant-insect interactions are readily observed within fossil datasets but spatial variability is harder to disentangle without comparable modern methods due to limitations in preservation. This is problematic as spatial variability influences community structure and interactions. To address this we replicated paleobotanical methods within three modern forests, creating an analogous dataset that rigorously tested inter- and intra-forest plant-insect variability. Random mixed effects models, non-metric multidimensional scaling (NMDS) ordinations, and bipartite network- and node-level metrics were used. Total damage frequency and diversity did not differ across forests but differences in functional feeding groups (FFGs) were observed across forests, correlating with plant diversity, evenness, and latitude. Overall, we found higher generalized herbivory within the temperate forests than the wet-tropical, a finding also supported by co-occurrence and network analyses at multiple spatial scales. Intra-forest analyses captured consistent damage type communities, supporting paleobotanical efforts. Bipartite networks captured the feeding outbreak of Lymantria dispar caterpillars; an exciting result as insect outbreaks have long been unidentifiable within fossil datasets. These results support paleobotanical assumptions about fossil insect herbivore communities, provide a comparative framework between paleobotanical and modern communities, and suggest a new analytical framework for targeting modern and fossil outbreaks of insect feeding.

Disturbance alters transience but nutrients determine equilibria during grassland succession with multiple global change drivers.

Disturbance and environmental change may cause communities to converge on a steady state, diverge towards multiple alternative states or remain in long-term transience. Yet, empirical investigations of successional trajectories are rare, especially in systems experiencing multiple concurrent anthropogenic drivers of change. We examined succession in old field grassland communities subjected to disturbance and nitrogen fertilization using data from a long-term (22-year) experiment. Regardless of initial disturbance, after a decade communities converged on steady states largely determined by resource availability, where species turnover declined as communities approached dynamic equilibria. Species favoured by the disturbance were those that eventually came to dominate the highly fertilized plots. Furthermore, disturbance made successional pathways more direct revealing an important interaction effect between nutrients and disturbance as drivers of community change. Our results underscore the dynamical nature of grassland and old field succession, demonstrating how community properties such as ß diversity change through transient and equilibrium states.

Quantifying mechanisms of coexistence in disease ecology.

Pathogen coexistence depends on ecological processes operating at both within and between-host scales, making it difficult to quantify which processes may promote or prevent coexistence. Here, we propose that adapting modern coexistence theory-traditionally applied in plant communities-to pathogen systems provides an exciting approach for examining mechanisms of coexistence operating across different spatial scales. We first overview modern coexistence theory and its mechanistic decomposition; we subsequently adapt the framework to quantify how spatial variation in pathogen density, host resources and immunity, and their interaction may promote pathogen coexistence. We apply this derivation to an example two pathogen, multiscale model comparing two scenarios with generalist and strain-specific immunity: one with demographic equivalency among pathogens and one with demographic trade-offs among pathogens. We then show how host-pathogen feedbacks generate spatial heterogeneity that promote pathogen coexistence and decompose those mechanisms to quantify how each spatial heterogeneity contributes to that coexistence. Specifically, coexistence of demographically equivalent pathogens occurs due to spatial variation in host resources, immune responses, and pathogen aggregation. With a competition-colonization trade-off, the superior colonizer requires spatial heterogeneity to coexist, whereas the superior competitor does not. Finally, we suggest ways forward for linking theory and empirical tests of coexistence in disease systems.

Positive effects of exotic species dampened by neighborhood heterogeneity.

It is well known that species interactions between exotic and native species are important for determining the success of biological invasions and how influential exotic species become in invaded communities. The strength and type of interactions between species can substantially vary, however, from negative and detrimental to minimal or even positive. Increasing evidence from the literature shows that exotic species have positive interactions with native species more often than originally thought. Gaps in our theory for how population growth is limited when interactions are positive, however, restrict our understanding of the mechanisms by which exotic "facilitators" contribute to diversity maintenance in invaded systems. Here, we quantified interactions between seven native and four exotic (established nonnative) common annual plant species in the highly diverse, York Gum woodlands of Western Australia. We used a Bayesian demographic modeling approach that allowed for interaction coefficients to be positive or negative, and explored key sources of variation in species responses to native and exotic neighbors at per capita (individual) and neighborhood levels. We observed positive per capita effects from exotic neighbors on exotic focal species as well as on several native focal species. However, all focal species were, on average, inhibited by their interaction neighborhood, when the variance in identity and abundance of observed neighbors was considered. At the neighborhood scale, exotic species were found to suppress all focal species, particularly those with high intrinsic fecundity. Our study demonstrates that within-neighborhood heterogeneity can regulate per capita positive effects of invaders, limiting runaway population growth of both natives and exotic invaders.

Global change re-structures alpine plant communities through interacting abiotic and biotic effects.

Global change is altering patterns of community assembly, with net outcomes dependent on species' responses to the abiotic environment, both directly and mediated through biotic interactions. Here, we assess alpine plant community responses in a 15-year factorial nitrogen addition, warming and snow manipulation experiment. We used a dynamic competition model to estimate the density-dependent and -independent processes underlying changes in species-group abundances over time. Density-dependent shifts in competitive interactions drove long-term changes in abundance of species-groups under global change while counteracting environmental drivers limited the growth response of the dominant species through density-independent mechanisms. Furthermore, competitive interactions shifted with the environment, primarily with nitrogen and drove non-linear abundance responses across environmental gradients. Our results highlight that global change can either reshuffle species hierarchies or further favour already-dominant species; predicting which outcome will occur requires incorporating both density-dependent and -independent mechanisms and how they interact across multiple global change factors.

Accounting for demographic uncertainty increases predictions for species coexistence: A case study with annual plants.

Natural systems contain more complexity than is accounted for in models of modern coexistence theory. Coexistence modelling often disregards variation arising from stochasticity in biological processes, heterogeneity among individuals and plasticity in trait values. However, these unaccounted-for sources of uncertainty are likely to be ecologically important and have the potential to impact estimates of coexistence. We applied a Bayesian modelling framework to data from an annual plant community in Western Australia to propagate uncertainty in coexistence outcomes using the invasion criterion and ratio of niche to fitness differences. We found accounting for this uncertainty altered predictions of coexistence versus competitive exclusion for 3 out of 14 species pairs and yielded a probability of priority effects for an additional species pair. The propagation of uncertainty arising from sources of biological complexity improves our ability to predict coexistence more accurately in natural systems.

Application of modern coexistence theory to rare plant restoration provides early indication of restoration trajectories.

Restoration ecology commonly seeks to re-establish species of interest in degraded habitats. Despite a rich understanding of how succession influences re-establishment, there are several outstanding questions that remain unaddressed: are short-term abundances sufficient to determine long-term re-establishment success, and what factors contribute to unpredictable restorations outcomes? In other words, when restoration fails, is it because the restored habitat is substandard, because of strong competition with invasive species, or alternatively due to changing environmental conditions that would equally impact established populations? Here, we re-purpose tools developed from modern coexistence theory to address these questions, and apply them to an effort to restore the endangered Contra Costa goldfields (Lasthenia conjugens) in constructed ("restored") California vernal pools. Using 16 years of data, we construct a population model of L. conjugens, a species of conservation concern due primarily to habitat loss and invasion of exotic grasses. We show that initial, short-term appearances of restoration success from population abundances is misleading, as year-to-year fluctuations cause long-term population growth rates to fall below zero. The failure of constructed pools is driven by lower maximum growth rates compared with reference ("natural") pools, coupled with a stronger negative sensitivity to annual fluctuations in abiotic conditions that yield decreased maximum growth rates. Nonetheless, our modeling shows that fluctuations in competition (mainly with exotic grasses) benefit L. conjugens through periods of competitive release, especially in constructed pools of intermediate pool depth. We therefore show how reductions in invasives and seed addition in pools of particular depths could change the outcome of restoration for L. conjugens. By applying a largely theoretical framework to the urgent goal of ecological restoration, our study provides a blueprint for predicting restoration success, and identifies future actions to reverse species loss.

The long and the short of it: Mechanisms of synchronous and compensatory dynamics across temporal scales.

Synchronous dynamics (fluctuations that occur in unison) are universal phenomena with widespread implications for ecological stability. Synchronous dynamics can amplify the destabilizing effect of environmental variability on ecosystem functions such as productivity, whereas the inverse, compensatory dynamics, can stabilize function. Here we combine simulation and empirical analyses to elucidate mechanisms that underlie patterns of synchronous versus compensatory dynamics. In both simulated and empirical communities, we show that synchronous and compensatory dynamics are not mutually exclusive but instead can vary by timescale. Our simulations identify multiple mechanisms that can generate timescale-specific patterns, including different environmental drivers, diverse life histories, dispersal, and non-stationary dynamics. We find that traditional metrics for quantifying synchronous dynamics are often biased toward long-term drivers and may miss the importance of short-term drivers. Our findings indicate key mechanisms to consider when assessing synchronous versus compensatory dynamics and our approach provides a pathway for disentangling these dynamics in natural systems.

Disentangling key species interactions in diverse and heterogeneous communities: A Bayesian sparse modelling approach.

Modelling species interactions in diverse communities traditionally requires a prohibitively large number of species-interaction coefficients, especially when considering environmental dependence of parameters. We implemented Bayesian variable selection via sparsity-inducing priors on non-linear species abundance models to determine which species interactions should be retained and which can be represented as an average heterospecific interaction term, reducing the number of model parameters. We evaluated model performance using simulated communities, computing out-of-sample predictive accuracy and parameter recovery across different input sample sizes. We applied our method to a diverse empirical community, allowing us to disentangle the direct role of environmental gradients on species' intrinsic growth rates from indirect effects via competitive interactions. We also identified a few neighbouring species from the diverse community that had non-generic interactions with our focal species. This sparse modelling approach facilitates exploration of species interactions in diverse communities while maintaining a manageable number of parameters.

Seed banks alter metacommunity diversity: The interactive effects of competition, dispersal and dormancy.

Dispersal and dormancy are two common strategies allowing for species persistence and the maintenance of biodiversity in variable environments. However, theory and empirical tests of spatial diversity patterns tend to examine either mechanism in isolation. Here, we developed a stochastic, spatially explicit metacommunity model incorporating seed banks with varying germination and survival rates. We found that dormancy and dispersal had interactive, nonlinear effects on the maintenance and distribution of metacommunity diversity. Seed banks promoted local diversity when seed survival was high and maintained regional diversity through interactions with dispersal. The benefits of seed banks for regional diversity were largest when dispersal was high or intermediate, depending on whether local competition was equal or stabilising. Our study shows that classic predictions for how dispersal affects metacommunity diversity can be strongly influenced by dormancy. Together, these results emphasise the need to consider both temporal and spatial processes when predicting multi-scale patterns of diversity.

Enhancing Racial/Ethnic Equity in College Student Mental Health Through Innovative Screening and Treatment.

Although college campuses are diversifying rapidly, students of color remain an underserved and understudied group. Online screening and subsequent allocation to treatment represents a pathway to enhancing equity in college student mental health. The purpose of the current study was to evaluate racial/ethnic differences in mental health problems and treatment enrollment within the context of a largescale screening and treatment research initiative on a diverse college campus. The sample was comprised of n = 2090 college students who completed an online mental health screening survey and were offered either free online or face-to-face treatment based on symptom severity as a part of a research study. A series of ordinal, binomial and multinomial logistic regression models were specified to examine racial/ethnic differences in mental health problems, prior treatment receipt, and enrollment in online and face-to-face treatment through the campus-wide research initiative. Racial/ethnic differences in depression, anxiety and suicidality endorsed in the screening survey were identified. Students of color were less likely to have received prior mental health treatment compared to non-Hispanic white students, but were equally likely to enroll in and initiate online and face-to-face treatment offered through the current research initiative. Rates of enrollment in online therapy were comparable to prior studies. Online screening and treatment may be an effective avenue to reaching underserved students of color with mental health needs on college campuses. Digital mental health tools hold significant promise for bridging gaps in care, but efforts to improve uptake and engagement are needed.

Climate sensitivity across latitude: scaling physiology to communities.

While we know climate change will impact individuals, populations, and communities, we lack a cross-scale synthesis for understanding global variation in climate change impacts and predicting their ecological effects. Studies of latitudinal variation in individuals' thermal responses have developed primarily in isolation from studies of natural populations' warming responses. Further, it is unclear whether latitudinal variation in temperature-dependent population responses will manifest into latitudinal patterns in community stability. Integrating across scales, we discuss the key drivers of latitudinal variation in climate change effects, with the goal of identifying key pieces of information necessary to predict warming effects in natural communities. We propose two experimental approaches synthesizing latitudinal variability in climate change impacts across scales of biological organization.

The spatial synchrony of species richness and its relationship to ecosystem stability.

Synchrony is broadly important to population and community dynamics due to its ubiquity and implications for extinction dynamics, system stability, and species diversity. Investigations of synchrony in community ecology have tended to focus on covariance in the abundances of multiple species in a single location. Yet, the importance of regional environmental variation and spatial processes in community dynamics suggests that community properties, such as species richness, could fluctuate synchronously across patches in a metacommunity, in an analog of population spatial synchrony. Here, we test the prevalence of this phenomenon and the conditions under which it may occur using theoretical simulations and empirical data from 20 marine and terrestrial metacommunities. Additionally, given the importance of biodiversity for stability of ecosystem function, we posit that spatial synchrony in species richness is strongly related to stability. Our findings show that metacommunities often exhibit spatial synchrony in species richness. We also found that richness synchrony can be driven by environmental stochasticity and dispersal, two mechanisms of population spatial synchrony. Richness synchrony also depended on community structure, including species evenness and beta diversity. Strikingly, ecosystem stability was more strongly related to richness synchrony than to species richness itself, likely because richness synchrony integrates information about community processes and environmental forcing. Our study highlights a new approach for studying spatiotemporal community dynamics and emphasizes the spatial dimensions of community dynamics and stability.

Temporal rarity is a better predictor of local extinction risk than spatial rarity.

Spatial rarity is often used to predict extinction risk, but rarity can also occur temporally. Perhaps more relevant in the context of global change is whether a species is core to a community (persistent) or transient (intermittently present), with transient species often susceptible to human activities that reduce niche space. Using 5-12 yr of data on 1,447 plant species from 49 grasslands on five continents, we show that local abundance and species persistence under ambient conditions are both effective predictors of local extinction risk following experimental exclusion of grazers or addition of nutrients; persistence was a more powerful predictor than local abundance. While perturbations increased the risk of exclusion for low persistence and abundance species, transient but abundant species were also highly likely to be excluded from a perturbed plot relative to ambient conditions. Moreover, low persistence and low abundance species that were not excluded from perturbed plots tended to have a modest increase in abundance following perturbance. Last, even core species with high abundances had large decreases in persistence and increased losses in perturbed plots, threatening the long-term stability of these grasslands. Our results demonstrate that expanding the concept of rarity to include temporal dynamics, in addition to local abundance, more effectively predicts extinction risk in response to environmental change than either rarity axis predicts alone.

General statistical scaling laws for stability in ecological systems.

Ecological stability refers to a family of concepts used to describe how systems of interacting species vary through time and respond to disturbances. Because observed ecological stability depends on sampling scales and environmental context, it is notoriously difficult to compare measurements across sites and systems. Here, we apply stochastic dynamical systems theory to derive general statistical scaling relationships across time, space, and ecological level of organisation for three fundamental stability aspects: resilience, resistance, and invariance. These relationships can be calibrated using random or representative samples measured at individual scales, and projected to predict average stability at other scales across a wide range of contexts. Moreover deviations between observed vs. extrapolated scaling relationships can reveal information about unobserved heterogeneity across time, space, or species. We anticipate that these methods will be useful for cross-study synthesis of stability data, extrapolating measurements to unobserved scales, and identifying underlying causes and consequences of heterogeneity.

Pliant pathogens: Estimating viral spread when confronted with new vector, host, and environmental conditions.

Pathogen spread rates are determined, in part, by the performance of pathogens under altered environmental conditions and their ability to persist while switching among hosts and vectors.To determine the effects of new conditions (host, vector, and nutrient) on pathogen spread rate, we introduced a vector-borne viral plant pathogen, Barley Yellow Dwarf Virus PAV (BYDV-PAV) into hosts, vectors, and host nutrient supplies that it had not encountered for thousands of viral generations. We quantified pathogen prevalence over the course of two serial inoculations under the new conditions. Using individual-level transmission rates from this experiment, we parameterized a dynamical model of disease spread and projected spread across host populations through a growing season.A change in nutrient conditions (increased supply of phosphorus) reduced viral transmission whereas shifting to a new vector or host species had no effect on infection prevalence. However, the reduction in the new nutrient environment was only temporary; infection prevalence recovered after the second inoculation. Synthesis. These results highlight how robust the pathogen, BYDV-PAV, is to changes in its biotic and abiotic environment. Our study also highlights the need to quantify longitudinal infection information beyond snapshot assessments to project disease risk for pathogens in new environments.