Thresholds and alternative states in a Neotropical dry forest in response to fire severity.

Neotropical xerophytic forest ecosystems evolved with fires that shaped their resilience to disturbance events. However, it is unknown whether forest resilience to fires persists under a new fire regime influenced by anthropogenic disturbance and climate change. We asked whether there was evidence for a fire severity threshold causing an abrupt transition from a forest to an alternative shrub thicket state in the presence of typical postfire management. We studied a heterogeneous wildfire event to assess medium-term effects (11 years) of varying fire severity in a xerophytic Caldén forest in central Argentina. We conducted vegetation surveys in patches that were exposed to low (LFS), medium (MFS), and high (HFS) fire severities but had similar prefire woody canopy cover. Satellite images were used to quantify fire severity using a delta Normalized Burning Ratio (dNBR) and to map prefire canopy cover. Postfire total woody canopy cover was higher in low and medium than high severity patches, but the understory woody component was highest in HFS patches. The density of woody plants was over three times higher under HFS than MFS and LFS due to the contribution of small woody plants to the total density. Unlike LFS and MFS patches, the small plants in HFS patches were persistent, multistem shrubs that resulted from the resprouting of top-killed Prosopis caldenia trees and, more importantly, from young shrubs that probably established after the wildfire. Our results suggest that the Caldén forest is resilient to fires of low to moderate severities but not to high-severity fires. Fire severities with dNBR values > ~600 triggered an abrupt transition to a shrub thicket state. Postfire grazing and controlled-fire treatments likely contributed to shrub dominance after high-severity wildfire. Forest to shrub thicket transitions enable recurring high-severity fire events. We propose that repeated fires combined with grazing can trap the system in a shrub thicket state. Further studies are needed to determine whether the relationships between fire and vegetation structure examined in this case study represent general mechanisms of irreversible state changes across the Caldenal forest region and whether analogous threshold relationships exist in other fire-prone woodland ecosystems.

Abrupt transitions in a southwest USA desert grassland related to the Pacific Decadal Oscillation.

Prediction of abrupt ecosystem transitions resulting from climatic change will be an essential element of adaptation strategies in the coming decades. In the arid southwest USA, the collapse and recovery of long-lived perennial grasses have important effects on ecosystem services, but the causes of these variations have been poorly understood. Here we use a quality-controlled vegetation monitoring dataset initiated in 1915 to show that grass cover dynamics during the 20th century were closely correlated to the Pacific decadal oscillation (PDO) index. The relationship out-performed models correlating grasses to yearly precipitation and drought indices, suggesting that ecosystem transitions attributed only to local disturbances were instead influenced by climate teleconnections. Shifts in PDO phase over time were associated with the persistent loss of core grass species and recovery of transient species, so recovery of grasses in aggregate concealed significant changes in species composition. However, the relationship between PDO and grass cover broke down after 1995; grass cover is consistently lower than PDO would predict. The decoupling of grass cover from the PDO suggests that a threshold had been crossed in which warming or land degradation overwhelmed the ability of any grass species to recover during favorable periods.

The consequences of climate change for dryland biogeochemistry.

Drylands, which cover > 40% of Earth's terrestrial surface, are dominant drivers of global biogeochemical cycling and home to more than one third of the global human population. Climate projections predict warming, drought frequency and severity, and evaporative demand will increase in drylands at faster rates than global means. As a consequence of extreme temperatures and high biological dependency on limited water availability, drylands are predicted to be exceptionally sensitive to climate change and, indeed, significant climate impacts are already being observed. However, our understanding and ability to forecast climate change effects on dryland biogeochemistry and ecosystem functions lag behind many mesic systems. To improve our capacity to forecast ecosystem change, we propose focusing on the controls and consequences of two key characteristics affecting dryland biogeochemistry: (1) high spatial and temporal heterogeneity in environmental conditions and (2) generalized resource scarcity. In addition to climate change, drylands are experiencing accelerating land-use change. Building our understanding of dryland biogeochemistry in both intact and disturbed systems will better equip us to address the interacting effects of climate change and landscape degradation. Responding to these challenges will require a diverse, globally distributed and interdisciplinary community of dryland experts united towards better understanding these vast and important ecosystems.

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.

Rapid genetic restoration of a keystone species exhibiting delayed demographic response.

Genetic founder effects are often expected when animals colonize restored habitat in fragmented landscapes, but empirical data on genetic responses to restoration are limited. We examined the genetic response of banner-tailed kangaroo rats (Dipodomys spectabilis) to landscape-scale grassland restoration in the Chihuahuan Desert of New Mexico, USA. Dipodomys spectabilis is a grassland specialist and keystone species. At sites treated with herbicide to remove shrubs, colonization by D. spectabilis is slow and populations persist at low density for ≥10 years (≥6 generations). Persistence at low density and low gene flow may cause strong founder effects. We compared genetic structure of D. spectabilis populations between treated sites and remnant grasslands, and we examined how the genetic response to restoration depended on treatment age, area, and connectivity to source populations. Allelic richness and heterozygosity were similar between treated sites and remnant grasslands. Allelic richness at treated sites was greatest early in the restoration trajectory, and genetic divergence did not differ between recently colonized and established populations. These results indicated that founder effects during colonization of treated sites were weak or absent. Moreover, our results suggested founder effects were not mitigated by treatment area or connectivity. Dispersal is negatively density-dependent in D. spectabilis, and we hypothesize that high gene flow may occur early in the restoration trajectory when density is low. Our study shows genetic diversity can be recovered more rapidly than demographic components of populations after habitat restoration and that founder effects are not inevitable for animals colonizing restored habitat in fragmented landscapes.

Restoration Practices Have Positive Effects on Breeding Bird Species of Concern in the Chihuahuan Desert.

Woody plant encroachment into grasslands is a global concern. Efforts to restore grasslands often assume that removal of woody plants benefits biodiversity but assumptions are rarely tested. In the Chihuahuan Desert of the Southwestern United States, we tested whether abundances of grassland specialist bird species would be greater in plant communities resulting from treatment with herbicides to remove encroaching shrubs compared with untreated shrub-dominated areas that represented pre-treatment conditions. In 2010, we surveyed breeding birds and vegetation at 16 treated-untreated pairs. In 2011, we expanded the survey effort to 21 treated-untreated pairs, seven unpaired treatment areas, and five reference grassland areas. Vegetation in treatment areas had higher perennial grass foliar and basal cover and lower shrub foliar cover compared with untreated areas. Several regionally declining grassland specialists exhibited higher occurrence and relative abundance in treated areas. A shrubland specialist, however, was associated with untreated areas and may be negatively impacted by shrub removal. Bird community composition differed between treated and untreated areas in both years. Our results indicate that shrub removal can have positive effects on grassland specialist bird species, but that a mosaic of treated and untreated areas might be most beneficial for regional biodiversity.

A test of critical thresholds and their indicators in a desertification-prone ecosystem: more resilience than we thought.

Theoretical models predict that drylands can cross critical thresholds, but experimental manipulations to evaluate them are non-existent. We used a long-term (13-year) pulse-perturbation experiment featuring heavy grazing and shrub removal to determine if critical thresholds and their determinants can be demonstrated in Chihuahuan Desert grasslands. We asked if cover values or patch-size metrics could predict vegetation recovery, supporting their use as early-warning indicators. We found that season of grazing, but not the presence of competing shrubs, mediated the severity of grazing impacts on dominant grasses. Recovery occurred at the same rate irrespective of grazing history, suggesting that critical thresholds were not crossed, even at low cover levels. Grass cover, but not patch size metrics, predicted variation in recovery rates. Some transition-prone ecosystems are surprisingly resilient; management of grazing impacts and simple cover measurements can be used to avert undesired transitions and initiate restoration.

Empirical assessment of state-and-transition models with a long-term vegetation record from the Sonoran Desert.

Resilience-based frameworks, including state-and-transition models (STM), are being increasingly called upon to inform policy and guide ecosystem management, particularly in rangelands. Yet, multiple challenges impede their effective implementation: (1) paucity of empirical tests of resilience concepts, such as alternative states and thresholds, and (2) heavy reliance on expert models, which are seldom tested against empirical data. We developed an analytical protocol to identify unique plant communities and their transitions, and applied it to a long-term vegetation record from the Sonoran Desert (1953-2009). We assessed whether empirical trends were consistent with resilience concepts, and evaluated how they may inform the construction and interpretation of expert STMs. Seven statistically distinct plant communities were identified based on the cover of 22 plant species in 68 permanent transects. We recorded 253 instances of community transitions, associated with changes in species composition between successive samplings. Expectedly, transitions were more frequent among proximate communities with similar species pools than among distant communities. But unexpectedly, communities and transitions were not strongly constrained by soil type and topography. Only 18 transitions featured disproportionately large compositional turnover (species dissimilarity ranged between 0.54 and 0.68), and these were closely associated with communities that were dominated by the common shrub (burroweed, Haplopappus tenuisecta); indicating that only some, and not all, communities may be prone to large compositional change. Temporal dynamics in individual transects illustrated four general trajectories: stability, nondirectional drift, reversibility, and directional shifts that were not reversed even after 2-3 decades. The frequency of transitions and the accompanying species dissimilarity were both positively correlated with fluctuation in precipitation, indicating that climatic drivers require more attention in STMs. Many features of the expert models, including the number of communities and participant species, were consistent with empirical trends, but expert models underrepresented recent increases in cacti while overemphasizing the introduced Lehmann's lovegrass (Eragrostis lehmanniana). Quantification of communities and transitions within long-term vegetation records presents several quantitative metrics such as transition frequency, magnitude of accompanying compositional change, presence of unidirectional trajectories, and lack of reversibility within various timescales, which can clarify resilience concepts and inform the construction and interpretation of STMs.

Soil-geomorphic heterogeneity governs patchy vegetation dynamics at an arid ecotone.

Soil properties are well known to affect vegetation, but the role of soil heterogeneity in the patterning of vegetation dynamics is poorly documented. We asked whether the location of an ecotone separating grass-dominated and sparsely vegetated areas reflected only historical variation in degradation or was related to variation in inherent soil properties. We then asked whether changes in the cover and spatial organization of vegetated and bare patches assessed using repeat aerial photography reflected self-organizing dynamics unrelated to soil variation or the stable patterning of soil variation. We found that the present-day ecotone was related to a shift from more weakly to more strongly developed soils. Parts of the ecotone were stable over a 60-year period, but shifts between bare and vegetated states, as well as persistently vegetated and bare states, occurred largely in small (<40 m2) patches throughout the study area. The probability that patches were presently vegetated or bare, as well as the probability that vegetation persisted and/or established over the 60-year period, was negatively related to surface calcium carbonate and positively related to subsurface clay content. Thus, only a fraction of the landscape was susceptible to vegetation change, and the sparsely vegetated area probably featured a higher frequency of susceptible soil patches. Patch dynamics and self-organizing processes can be constrained by subtle (and often unrecognized) soil heterogeneity.

Cross-scale interactions, nonlinearities, and forecasting catastrophic events.

Catastrophic events share characteristic nonlinear behaviors that are often generated by cross-scale interactions and feedbacks among system elements. These events result in surprises that cannot easily be predicted based on information obtained at a single scale. Progress on catastrophic events has focused on one of the following two areas: nonlinear dynamics through time without an explicit consideration of spatial connectivity [Holling, C. S. (1992) Ecol. Monogr. 62, 447-502] or spatial connectivity and the spread of contagious processes without a consideration of cross-scale interactions and feedbacks [Zeng, N., Neeling, J. D., Lau, L. M. & Tucker, C. J. (1999) Science 286, 1537-1540]. These approaches rarely have ventured beyond traditional disciplinary boundaries. We provide an interdisciplinary, conceptual, and general mathematical framework for understanding and forecasting nonlinear dynamics through time and across space. We illustrate the generality and usefulness of our approach by using new data and recasting published data from ecology (wildfires and desertification), epidemiology (infectious diseases), and engineering (structural failures). We show that decisions that minimize the likelihood of catastrophic events must be based on cross-scale interactions, and such decisions will often be counterintuitive. Given the continuing challenges associated with global change, approaches that cross disciplinary boundaries to include interactions and feedbacks at multiple scales are needed to increase our ability to predict catastrophic events and develop strategies for minimizing their occurrence and impacts. Our framework is an important step in developing predictive tools and designing experiments to examine cross-scale interactions.

Land management in the American southwest: a state-and-transition approach to ecosystem complexity.

State-and-transition models are increasingly being used to guide rangeland management. These models provide a relatively simple, management-oriented way to classify land condition (state) and to describe the factors that might cause a shift to another state (a transition). There are many formulations of state-and-transition models in the literature. The version we endorse does not adhere to any particular generalities about ecosystem dynamics, but it includes consideration of several kinds of dynamics and management response to them. In contrast to previous uses of state-and-transition models, we propose that models can, at present, be most effectively used to specify and qualitatively compare the relative benefits and potential risks of different management actions (e.g., fire and grazing) and other factors (e.g., invasive species and climate change) on specified areas of land. High spatial and temporal variability and complex interactions preclude the meaningful use of general quantitative models. Forecasts can be made on a case-by-case basis by interpreting qualitative and quantitative indicators, historical data, and spatially structured monitoring data based on conceptual models. We illustrate how science- based conceptual models are created using several rangeland examples that vary in complexity. In doing so, we illustrate the implications of designating plant communities and states in models, accounting for varying scales of pattern in vegetation and soils, interpreting the presence of plant communities on different soils and dealing with our uncertainty about how those communities were assembled and how they will change in the future. We conclude with observations about how models have helped to improve management decision-making.