Long-term community change through multiple rapid transitions in a desert rodent community

Beyond single-species models: leveraging multispecies forecasts to navigate the dynamics of ecological predictability

Background

Forecasting the responses of natural populations to environmental change is a key priority in the management of ecological systems. This is challenging because the dynamics of multi-species ecological communities are influenced by many factors. Populations can exhibit complex, nonlinear responses to environmental change, often over multiple temporal lags. In addition, biotic interactions, and other sources of multi-species dependence, are major contributors to patterns of population variation. Theory suggests that near-term ecological forecasts of population abundances can be improved by modelling these dependencies, but empirical support for this idea is lacking.

Methods

We test whether models that learn from multiple species, both to estimate nonlinear environmental effects and temporal interactions, improve ecological forecasts compared to simpler single species models for a semi-arid rodent community. Using dynamic generalized additive models, we analyze time series of monthly captures for nine rodent species over 25 years.

Results

Model comparisons provide strong evidence that multi-species dependencies improve both hindcast and forecast performance, as models that captured these effects gave superior predictions than models that ignored them. We show that changes in abundance for some species can have delayed, nonlinear effects on others, and that lagged, nonlinear effects of temperature and vegetation greenness are key drivers of changes in abundance for this system.

Conclusions

Our findings highlight that multivariate models are useful not only to improve near-term ecological forecasts but also to ask targeted questions about ecological interactions and drivers of change. This study emphasizes the importance of jointly modelling species' shared responses to the environment and their delayed temporal interactions when teasing apart community dynamics.

Transferability of ecological forecasting models to novel biotic conditions in a long-term experimental study

Ecological forecasting models play an increasingly important role for managing natural resources and assessing our fundamental knowledge of processes driving ecological dynamics. As global environmental change pushes ecosystems beyond their historical conditions, the utility of these models may depend on their transferability to novel conditions. Because species interactions can alter resource use, timing of reproduction, and other aspects of a species' realized niche, changes in biotic conditions, which can arise from community reorganization events in response to environmental change, have the potential to impact model transferability. Using a long-term experiment on desert rodents, we assessed model transferability under novel biotic conditions to better understand the limitations of ecological forecasting. We show that ecological forecasts can be less accurate when the models generating them are transferred to novel biotic conditions and that the extent of model transferability can depend on the species being forecast. We also demonstrate the importance of incorporating uncertainty into forecast evaluation with transferred models generating less accurate and more uncertain forecasts. These results suggest that how a species perceives its competitive landscape can influence model transferability and that when uncertainties are properly accounted for, transferred models may still be appropriate for decision making. Assessing the extent of the transferability of forecasting models is a crucial step to increase our understanding of the limitations of ecological forecasts.

Looking back on biodiversity change: lessons for the road ahead

Estimating biodiversity change across the planet in the context of widespread human modification is a critical challenge. Here, we review how biodiversity has changed in recent decades across scales and taxonomic groups, focusing on four diversity metrics: species richness, temporal turnover, spatial beta-diversity and abundance. At local scales, change across all metrics includes many examples of both increases and declines and tends to be centred around zero, but with higher prevalence of declining trends in beta-diversity (increasing similarity in composition across space or biotic homogenization) and abundance. The exception to this pattern is temporal turnover, with changes in species composition through time observed in most local assemblages. Less is known about change at regional scales, although several studies suggest that increases in richness are more prevalent than declines. Change at the global scale is the hardest to estimate accurately, but most studies suggest extinction rates are probably outpacing speciation rates, although both are elevated. Recognizing this variability is essential to accurately portray how biodiversity change is unfolding, and highlights how much remains unknown about the magnitude and direction of multiple biodiversity metrics at different scales. Reducing these blind spots is essential to allow appropriate management actions to be deployed. This article is part of the theme issue 'Detecting and attributing the causes of biodiversity change: needs, gaps and solutions'.

Continuous irregular dynamics with multiple neutral trajectories permit species coexistence in competitive communities

The colonization model formulates competition among propagules for habitable sites to colonize, which serves as a mechanism enabling coexistence of multiple species. This model traditionally assumes that encounters between propagules and sites occur as mass action events, under which species distribution can eventually reach an equilibrium state with multiple species in a constant environment. To investigate the effects of encounter mode on species diversity, we analyzed community dynamics in the colonization model by varying encounter processes. The analysis indicated that equilibrium is approximately neutrally-stable under perfect ratio-dependent encounter, resulting in temporally continuous variation of species' frequencies with irregular trajectories even under a constant environment. Although the trajectories significantly depend on initial conditions, they are considered to be "strange nonchaotic attractors" (SNAs) rather than chaos from the asymptotic growth rates of displacement. In addition, trajectories with different initial conditions remain different through time, indicating that the system involves an infinite number of SNAs. This analysis presents a novel mechanism for transient dynamics under competition.

Looking for compensation at multiple scales in a wetland bird community

Compensatory dynamics, during which community composition shifts despite a near-constant total community size, are usually rare: Synchronous dynamics prevail in natural communities. This is a puzzle for ecologists, because of the key role of compensation in explaining the relation between biodiversity and ecosystem functioning. However, most studies so far have considered compensation in either plants or planktonic organisms, so that evidence for the generality of such synchrony is limited. Here, we extend analyses of community-level synchrony to wetland birds. We analyze a 35-year monthly survey of a community where we suspected that compensation might occur due to potential competition and changes in water levels, favoring birds with different habitat preferences. We perform both year-to-year analyses by season, using a compensation/synchrony index, and multiscale analyses using a wavelet-based measure, which allows for both scale- and time-dependence. We analyze synchrony both within and between guilds, with guilds defined either as tightknit phylogenetic groups or as larger functional groups. We find that abundance and biomass compensation are rare, likely due to the synchronizing influence of climate (and other drivers) on birds, even after considering several temporal scales of covariation (during either cold or warm seasons, above or below the annual scale). Negative covariation in abundance at the guild or community level did only appear at the scale of a few months or several years. We also found that synchrony varies with taxonomic and functional scale: The rare cases where compensation appeared consistently in year-to-year analyses were between rather than within functional groups. Our results suggest that abundance compensation may have more potential to emerge between broad functional groups rather than between species, and at relatively long temporal scales (multiple years for vertebrates), above that of the dominant synchronizing driver.

Maintenance of community function through compensation breaks down over time in a desert rodent community

Understanding the ecological processes that maintain community function in systems experiencing species loss, and how these processes change over time, is key to understanding the relationship between community structure and function and predicting how communities may respond to perturbations in the Anthropocene. Using a 30‐year experiment on desert rodents, we show that the impact of species loss on community‐level energy use has changed repeatedly and dramatically over time, due to (1) the addition of new species to the community, and (2) a reduction in functional redundancy among the same set of species. Although strong compensation, initially driven by the dispersal of functionally redundant species to the local community, occurred in this system from 1997 to 2010, since 2010, compensation has broken down due to decreasing functional overlap within the same set of species. Simultaneously, long‐term changes in sitewide community composition due to niche complementarity have decoupled the dynamics of compensation from the overall impact of species loss on community‐level energy use. Shifting, context‐dependent compensatory dynamics, such as those demonstrated here, highlight the importance of explicitly long‐term, metacommunity, and eco‐evolutionary perspectives on the link between species‐level fluctuations and community function in a changing world.

The Latent Dirichlet Allocation model with covariates (LDAcov): A case study on the effect of fire on species composition in Amazonian forests

Understanding and predicting the effect of global change phenomena on biodiversity is challenging given that biodiversity data are highly multivariate, containing information from tens to hundreds of species in any given location and time. The Latent Dirichlet Allocation (LDA) model has been recently proposed to decompose biodiversity data into latent communities. While LDA is a very useful exploratory tool and overcomes several limitations of earlier methods, it has limited inferential and predictive skill given that covariates cannot be included in the model. We introduce a modified LDA model (called LDAcov) which allows the incorporation of covariates, enabling inference on the drivers of change of latent communities, spatial interpolation of results, and prediction based on future environmental change scenarios. We show with simulated data that our approach to fitting LDAcov is able to estimate well the number of groups and all model parameters. We illustrate LDAcov using data from two experimental studies on the long-term effects of fire on southeastern Amazonian forests in Brazil. Our results reveal that repeated fires can have a strong impact on plant assemblages, particularly if fuel is allowed to build up between consecutive fires. The effect of fire is exacerbated as distance to the edge of the forest decreases, with small-sized species and species with thin bark being impacted the most. These results highlight the compounding impacts of multiple fire events and fragmentation, a scenario commonly found across the southern edge of Amazon. We believe that LDAcov will be of wide interest to scientists studying the effect of global change phenomena on biodiversity using high-dimensional datasets. Thus, we developed the R package LDAcov to enable the straightforward use of this model.

Recovery from air pollution and subsequent acidification masks the effects of climate change on a freshwater macroinvertebrate community

Freshwater ecosystems are dynamic, complex systems with a multitude of physical and ecological processes and stressors which drive fluctuations on the community-level. Disentangling the effects of different processes and stressors is challenging due to their interconnected nature. However, as protected areas (i.e. national parks) are less anthropogenically impacted, they are ideal for investigating single stressors. We focus on the Bavarian Forest National Park, a Long-Term Ecological Research (LTER) site in Germany, where the major stressors are climate warming, air pollution (i.e. acidification) and bark beetle infestations. We investigated the effects of these stressors on freshwater macroinvertebrates using comprehensive long-term (1983-2014) datasets comprising high-resolution macroinvertebrate and physico-chemical data from a near-natural stream. Macroinvertebrate communities have undergone substantial changes over the past 32 years, highlighted by increases in overall community abundance (+173%) and richness (+51.6%) as well as taxonomic restructuring driven by a disproportional increase of dipterans. Prior to the year 2000, regression analyses revealed a decline in sulphate deposition and subsequent recovery from historical acidification as potential drivers of the increases in abundance and richness rather than to increases in water temperature (1.5 °C overall increase). Post 2000, however, alterations to nutrient cycling caused by bark beetle infestations coupled with warming temperatures were correlated to taxonomic restructuring and disproportional increases of dipterans at the expense of sensitive taxa such as plecopterans and trichopterans. Our results highlight the challenges when investigating the effects of climate change within a multi-stressor context. Even in conservation areas, recovery from previous disturbance might mask the effects of ongoing disturbances like climate change. Overall, we observed strong community restructuring, demonstrating that stenothermal headwater communities face additional stress due to emerging competition with tolerant taxa. Conservation efforts should consider the temporal variability of communities and their recovery from disturbances to adequately identify species vulnerable to local or widespread extinction.

Complex and nonlinear climate-driven changes in freshwater insect communities over 42 years

The ongoing biodiversity crisis becomes evident in the widely observed decline in abundance and diversity of species, profound changes in community structure, and shifts in species' phenology. Insects are among the most affected groups, with documented decreases in abundance up to 76% in the last 25-30 years in some terrestrial ecosystems. Identifying the underlying drivers is a major obstacle as most ecosystems are affected by multiple stressors simultaneously and in situ measurements of environmental variables are often missing. In our study, we investigated a headwater stream belonging to the most common stream type in Germany located in a nature reserve with no major anthropogenic impacts except climate change. We used the most comprehensive quantitative long-term data set on aquatic insects available, which includes weekly measurements of species-level insect abundance, daily water temperature and stream discharge as well as measurements of additional physicochemical variables for a 42-year period (1969-2010). Overall, water temperature increased by 1.88 °C and discharge patterns changed significantly. These changes were accompanied by an 81.6% decline in insect abundance, but an increase in richness (+8.5%), Shannon diversity (+22.7%), evenness (+22.4%), and interannual turnover (+34%). Moreover, the community's trophic structure and phenology changed: the duration of emergence increased by 15.2 days, whereas the peak of emergence moved 13.4 days earlier. Additionally, we observed short-term fluctuations (<5 years) in almost all metrics as well as complex and nonlinear responses of the community toward climate change that would have been missed by simply using snapshot data or shorter time series. Our results indicate that climate change has already altered biotic communities severely even in protected areas, where no other interacting stressors (pollution, habitat fragmentation, etc.) are present. This is a striking example of the scientific value of comprehensive long-term data in capturing the complex responses of communities toward climate change.

Unveiling causal interactions in complex systems

Throughout time, operational laws and concepts from complex systems have been employed to quantitatively model important aspects and interactions in nature and society. Nevertheless, it remains enigmatic and challenging, yet inspiring, to predict the actual interdependencies that comprise the structure of such systems, particularly when the causal interactions observed in real-world phenomena might be persistently hidden. In this article, we propose a robust methodology for detecting the latent and elusive structure of dynamic complex systems. Our treatment utilizes short-term predictions from information embedded in reconstructed state space. In this regard, using a broad class of real-world applications from ecology, neurology, and finance, we explore and are able to demonstrate our method's power and accuracy to reconstruct the fundamental structure of these complex systems, and simultaneously highlight their most fundamental operations.

Established rodent community delays recovery of dominant competitor following experimental disturbance

Human activities alter processes that control local biodiversity, causing changes in the abundance and identity of species in ecosystems. However, restoring biodiversity to a previous state is rarely as simple as reintroducing lost species or restoring processes to their pre-disturbance state. Theory suggests that established species can impede shifts in species composition via a variety of mechanisms, including direct interference, pre-empting resources or habitat alteration. These mechanisms can create transitory dynamics that delay convergence to an expected end state. We use an experimental manipulation of a desert rodent community to examine differences in recolonization dynamics of a dominant competitor (kangaroo rats of the genus Dipodomys) when patches were already occupied by an existing rodent community relative to when patches were empty. Recovery of kangaroo rat populations was slow on plots with an established community, taking approximately 2 years, in contrast with rapid recovery on empty plots with no established residents (approx. three months). These results demonstrate that the presence of an established alternate community inhibits recolonization by new species, even those that should be dominant in the community. This has important implications for understanding how biodiversity may change in the future, and what processes may slow or prevent this change.

Trends in Diatom Research Since 1991 Based on Topic Modeling

Diatoms are fundamental carbon sources in a wide range of aquatic food webs and have the potential for wide application in addressing environmental change. Understanding the evolution of topics in diatom research will provide a clear and needed guide to strengthen research on diatoms. However, such an overview remains unavailable. In this study, we used Latent Dirichlet Allocation (LDA), a generative model, to identify topics and determine their trends (i.e., cold and hot topics) by analyzing the abstracts of 19,000 publications from the Web of Science that were related to diatoms during 1991–2018. A total of 116 topics were identified from a Bayesian model selection. The hot topics (diversity, environmental indicator, climate change, land use, and water quality) that were identified by LDA indicated that diatoms are increasingly used as indicators to assess water quality and identify modern climate change impacts due to intensive anthropogenic activities. In terms of cold topics (growth rate, culture growth, cell life history, copepod feeding, grazing by microzooplankton, zooplankton predation, and primary productivity) and hot topics (spatial-temporal distribution, morphology, molecular identification, gene expression, and review), we determined that basic studies on diatoms have decreased and that studies tend to be more comprehensive. This study notes that future directions in diatom research will be closely associated with the application of diatoms in environmental management and climate change to cope with environmental challenges, and more comprehensive issues related to diatoms should be considered.

Extending the Latent Dirichlet Allocation model to presence/absence data: A case study on North American breeding birds and biogeographical shifts expected from climate change

Understanding how species composition varies across space and time is fundamental to ecology. While multiple methods having been created to characterize this variation through the identification of groups of species that tend to co-occur, most of these methods unfortunately are not able to represent gradual variation in species composition. The Latent Dirichlet Allocation (LDA) model is a mixed-membership method that can represent gradual changes in community structure by delineating overlapping groups of species, but its use has been limited because it requires abundance data and requires users to a priori set the number of groups. We substantially extend LDA to accommodate widely available presence/absence data and to simultaneously determine the optimal number of groups. Using simulated data, we show that this model is able to accurately determine the true number of groups, estimate the underlying parameters, and fit with the data. We illustrate this method with data from the North American Breeding Bird Survey (BBS). Overall, our model identified 18 main bird groups, revealing striking spatial patterns for each group, many of which were closely associated with temperature and precipitation gradients. Furthermore, by comparing the estimated proportion of each group for two time periods (1997-2002 and 2010-2015), our results indicate that nine (of 18) breeding bird groups exhibited an expansion northward and contraction southward of their ranges, revealing subtle but important community-level biodiversity changes at a continental scale that are consistent with those expected under climate change. Our proposed method is likely to find multiple uses in ecology, being a valuable addition to the toolkit of ecologists.