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.

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.

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.

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.

Quantifying the relative importance of variation in predation and the environment for species coexistence.

Coexistence and food web theory are two cornerstones of the long-standing effort to understand how species coexist. Although competition and predation are known to act simultaneously in communities, theory and empirical study of these processes continue to be developed largely independently. Here, we integrate modern coexistence theory and food web theory to simultaneously quantify the relative importance of predation and environmental fluctuations for species coexistence. We first examine coexistence in a theoretical, multitrophic model, adding complexity to the food web using machine learning approaches. We then apply our framework to a stochastic model of the rocky intertidal food web, partitioning empirical coexistence dynamics. We find the main effects of both environmental fluctuations and variation in predator abundances contribute substantially to species coexistence. Unexpectedly, their interaction tends to destabilise coexistence, leading to new insights about the role of bottom-up vs. top-down forces in both theory and the rocky intertidal ecosystem.

Integrating the underlying structure of stochasticity into community ecology.

Stochasticity is a core component of ecology, as it underlies key processes that structure and create variability in nature. Despite its fundamental importance in ecological systems, the concept is often treated as synonymous with unpredictability in community ecology, and studies tend to focus on single forms of stochasticity rather than taking a more holistic view. This has led to multiple narratives for how stochasticity mediates community dynamics. Here, we present a framework that describes how different forms of stochasticity (notably demographic and environmental stochasticity) combine to provide underlying and predictable structure in diverse communities. This framework builds on the deep ecological understanding of stochastic processes acting at individual and population levels and in modules of a few interacting species. We support our framework with a mathematical model that we use to synthesize key literature, demonstrating that stochasticity is more than simple uncertainty. Rather, stochasticity has profound and predictable effects on community dynamics that are critical for understanding how diversity is maintained. We propose next steps that ecologists might use to explore the role of stochasticity for structuring communities in theoretical and empirical systems, and thereby enhance our understanding of community dynamics.

Cross-scale dynamics in community and disease ecology: relative timescales shape the community ecology of pathogens.

Communities of free-living organisms are shaped by processes operating within and among patches of habitat, whereas pathogen communities are shaped by analogous processes operating within and among hosts. Resource competition (R*) theory can describe dynamics within patches or hosts, and metacommunity dynamics describe competition-colonization trade-offs, extinction debts, and superinfection. However, models at this broader scale often assume instantaneous competitive exclusion in co-inhabited patches or co-infected hosts. Impacts of more gradual competitive exclusion on the abundance, distribution, and diversity of species are less clear. Here, we nest a general resource competition model within a metacommunity framework and manipulate the relative timescales for processes operating within and among patches/hosts. We focus on superinfection in pathogen communities. We compare cases where transmission depends on infection prevalence vs. the abundance of pathogens within hosts. Surprisingly, slowing the relative pace of competitive exclusion within hosts can decrease infection prevalence of the inferior competitor and increase prevalence of the superior competitor, depending on transmission and virulence. Slower within-host dynamics reduce the abundance of both pathogens within hosts and promote diversity at multiple scales: co-infections within individual hosts and co-occurrence in the host population. These results highlight surprising feedbacks that can emerge across scales and reinforce the rich cross-scale connections between community and disease ecology.

Rainfall variability maintains grass-forb species coexistence.

Environmental variability can structure species coexistence by enhancing niche partitioning. Modern coexistence theory highlights two fluctuation-dependent temporal coexistence mechanisms -the storage effect and relative nonlinearity - but empirical tests are rare. Here, we experimentally test if environmental fluctuations enhance coexistence in a California annual grassland. We manipulate rainfall timing and relative densities of the grass Avena barbata and forb Erodium botrys, parameterise a demographic model, and partition coexistence mechanisms. Rainfall variability was integral to grass-forb coexistence. Variability enhanced growth rates of both species, and early-season drought was essential for Erodium persistence. While theoretical developments have focused on the storage effect, it was not critical for coexistence. In comparison, relative nonlinearity strongly stabilised coexistence, where Erodium experienced disproportionately high growth under early-season drought due to competitive release from Avena. Our results underscore the importance of environmental variability and suggest that relative nonlinearity is a critical if underappreciated coexistence mechanism.

Pathogens manipulate the preference of vectors, slowing disease spread in a multi-host system.

The spread of vector-borne pathogens depends on a complex set of interactions among pathogen, vector, and host. In single-host systems, pathogens can induce changes in vector preferences for infected vs. healthy hosts. Yet it is unclear if pathogens also induce changes in vector preference among host species, and how changes in vector behaviour alter the ecological dynamics of disease spread. Here, we couple multi-host preference experiments with a novel model of vector preference general to both single and multi-host communities. We show that viruliferous aphids exhibit strong preferences for healthy and long-lived hosts. Coupling experimental results with modelling to account for preference leads to a strong decrease in overall pathogen spread through multi-host communities due to non-random sorting of viruliferous vectors between preferred and non-preferred host species. Our results demonstrate the importance of the interplay between vector behaviour and host diversity as a key mechanism in the spread of vectored-diseases.