Long-term monitoring and experimental manipulation of a Chihuahuan desert ecosystem near Portal, Arizona (1977-2013)

Shifts in competitive structures can drive variation in species' phenology

For many species, a well documented response to anthropogenic climate change is a shift in various aspects of its life history, including its timing or phenology. Often, these phenological shifts are associated with changes in abiotic factors used as proxies for resource availability or other suitable conditions. Resource availability, however, can also be impacted by competition, but the impact of competition on phenology is less studied than abiotic drivers. We fit generalized additive models (GAMs) to a long-term experimental dataset on small mammals monitored in the southwestern United States and show that altered competitive landscapes can drive shifts in breeding timing and prevalence, and that, relative to a dominant competitor, other species exhibit less specific responses to environmental factors. These results suggest that plasticity of phenological responses, which is often described in the context of annual variation in abiotic factors, can occur in response to biotic context as well. Variation in phenological responses under different biotic conditions shown here further demonstrates that a more nuanced understanding of shifting biotic interactions is useful to better understand and predict biodiversity patterns in a changing world.

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.

Harmony on the prairie? Grassland plant and animal community responses to variation in climate across land-use gradients

Human induced climate and land-use change are severely impacting global biodiversity, but how community composition and richness of multiple taxonomic groups change in response to local drivers and whether these responses are synchronous remains unclear. We used long-term community-level data from an experimentally manipulated grassland to assess the relative influence of climate and land use as drivers of community structure of four taxonomic groups: birds, mammals, grasshoppers, and plants. We also quantified the synchrony of responses among taxonomic groups across land-use gradients and compared climatic drivers of community structure across groups. All four taxonomic groups responded strongly to land use (fire frequency and grazing), while responses to climate variability were more pronounced in grasshoppers and small mammals. Animal groups exhibited asynchronous responses across all land-use treatments, but plant and animal groups, especially birds, exhibited synchronous responses in composition. Asynchrony was attributed to taxonomic groups responding to different components of climate variability, including both current climate conditions and lagged effects from the previous year. Data-driven land management strategies are crucial for sustaining native biodiversity in grassland systems, but asynchronous responses of taxonomic groups to climate variability across land-use gradients highlight a need to incorporate response heterogeneity into management planning.

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.

Invasion intensity influences scale-dependent effects of an exotic species on native plant diversity

Invasive plant species reduce the diversity of natives by altering habitats or disturbance regimes, but it is less clear whether they do so via competitive exclusion. Here, we show that invader abundance alters scale-dependent competitive effects of invasion on native plant richness. Large-seeded exotic annual Erodium cicutarium invaded a site that manipulated rodent granivores. The invader became dominant on all plots but attained its highest abundance on plots that removed rodents. Invasion reduced plant abundance but not evenness; site-wide richness did not change over time on control plots but declined significantly on rodent removal plots. Species-area relationships within plots changed differently with invasion intensity: slopes increased and y-intercepts decreased on control plots relative to rodent removal plots. Changes in species-area slopes and y-intercepts following invasion suggest that common rather than rare species were most strongly impacted at small spatial scales on control plots, while common and rare species were both negatively impacted at all spatial scales on rodent removal plots. Small-seeded species declined in abundance following invasion more so than large-seeded species, indicative of competitive interactions mediated by seed size. These results reveal variation in scale-dependent competitive effects of invasion on native richness associated with invasion intensity.

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

While studies increasingly document long-term change in community composition, whether long-term change occurs gradually or via rapid reorganization events remains unclear. We used Latent Dirichlet Allocation (LDA) and a change-point model to examine the long-term dynamics of a desert rodent community undergoing compositional change over a 38-yr span. Our approach detected three rapid reorganization events, where changes in the relative abundances of dominant and rare species occurred, and a separate period of increased variance in the structure of the community. These events coincided with time periods, possibly related to climate events, where the total abundance of rodents was extremely low. There are a variety of processes that could link low abundance events with a higher probability of rapid ecological transitions, including higher importance of stochastic processes (i.e., competitive interactions or priority effects) and the removal of structuring effects of competitive dominants or incumbent species. Continued study of the dynamics of community change will provide important information not only on the processes structuring communities, but will also provide guidance for forecasting how communities will undergo change in the future.

Quantifying long-term plant community dynamics with movement models: implications for ecological resilience

Quantification of rates and patterns of community dynamics is central for understanding the organization and function of ecosystems. These insights may support a greater empirical understanding of ecological resilience, and the application of resilience concepts toward ecosystem management. Distinct types of dynamics in natural communities can be used to interpret and apply resilience concepts, but quantitative methods that can systematically distinguish among them are needed. We develop a quantitative method to analyze long-term records of plant community dynamics using principles of movement ecology. We analyzed dissimilarity of species composition through time with linear and nonlinear statistical models to assign community change to four classes of movement trajectories. Compositional change in each sampled plot through time was classified into four classes, stability, abrupt nonlinear change, transient reversible change, and gradual linear drift, each representing a different aspect of ecological resilience. These competing models were evaluated based on estimated coefficients, goodness of fit, and parsimony. We tested our method's accuracy and robustness through simulations, or the ability to distinguish among trajectories and classify them correctly. We simulated 16,000 trajectories of four types, of which 94-100% were correctly classified. Next, we analyzed 13 long-term vegetation records from North American grasslands (annual grasslands with warm-season and cool-season communities, shortgrass, mixedgrass, and tallgrass prairies, and sagebrush steppe), and a record of primary succession at Mt. St. Helens volcano. Collectively, we analyzed 14,647 observations from 775 plots, between 1915 and 2012. Dynamics could be reliably assigned for 705 plots (91%), and overall statistical fit was high (goodness of fit, 0.77 ± 0.15 SD). Among the perennial grasslands, stability was most common (44% of all plots), followed by gradual linear (22%), abrupt nonlinear (17%), and reversible (6%) change. Among annual grasslands, abrupt nonlinear shifts (33%) were more common in the warm-season community than in the cool-season (20%). As expected, abrupt nonlinear change was common during primary succession (51%) while reversible change was rare (3%). Generally, reversible dynamics often required 2-3 decades. Analysis of long-term community change, or trajectories, with principles of movement ecology provides a quantitative basis to compare and interpret ecological resilience within and among ecosystems.

The EcoData retriever: improving access to existing ecological data

Ecological research relies increasingly on the use of previously collected data. Use of existing datasets allows questions to be addressed more quickly, more generally, and at larger scales than would otherwise be possible. As a result of large-scale data collection efforts, and an increasing emphasis on data publication by journals and funding agencies, a large and ever-increasing amount of ecological data is now publicly available via the internet. Most ecological datasets do not adhere to any agreed-upon standards in format, data structure or method of access. Some may be broken up across multiple files, stored in compressed archives, and violate basic principles of data structure. As a result acquiring and utilizing available datasets can be a time consuming and error prone process. The EcoData Retriever is an extensible software framework which automates the tasks of discovering, downloading, and reformatting ecological data files for storage in a local data file or relational database. The automation of these tasks saves significant time for researchers and substantially reduces the likelihood of errors resulting from manual data manipulation and unfamiliarity with the complexities of individual datasets.

Quantifying temporal change in biodiversity: challenges and opportunities

Growing concern about biodiversity loss underscores the need to quantify and understand temporal change. Here, we review the opportunities presented by biodiversity time series, and address three related issues: (i) recognizing the characteristics of temporal data; (ii) selecting appropriate statistical procedures for analysing temporal data; and (iii) inferring and forecasting biodiversity change. With regard to the first issue, we draw attention to defining characteristics of biodiversity time series--lack of physical boundaries, uni-dimensionality, autocorrelation and directionality--that inform the choice of analytic methods. Second, we explore methods of quantifying change in biodiversity at different timescales, noting that autocorrelation can be viewed as a feature that sheds light on the underlying structure of temporal change. Finally, we address the transition from inferring to forecasting biodiversity change, highlighting potential pitfalls associated with phase-shifts and novel conditions.

Integrating spatial and temporal approaches to understanding species richness

Understanding species richness patterns represents one of the most fundamental problems in ecology. Most research in this area has focused on spatial gradients of species richness, with a smaller area of emphasis dedicated to understanding the temporal dynamics of richness. However, few attempts have been made to understand the linkages between the spatial and temporal patterns related to richness. Here, we argue that spatial and temporal richness patterns and the processes that drive them are inherently linked, and that our understanding of richness will be substantially improved by considering them simultaneously. The species-time-area relationship provides a case in point: successful description of the empirical spatio-temporal pattern led to a rapid development and testing of new theories. Other areas of research on species richness could also benefit from an explicitly spatio-temporal approach, and we suggest future directions for understanding the processes common to these two traditionally isolated fields of research.