Andrew T. Tredennick

Analysis of stable states in global savannas: is the CART pulling the horse?

Multiple stable states, bifurcations and thresholds are fashionable concepts in the ecological literature, a recognition that complex ecosystems may at times exhibit the interesting dynamic behaviours predicted by relatively simple biomathematical models. Recently, several papers in Global Ecology and Biogeography, Proceedings of the National Academy of Sciences USA, Science and elsewhere have attempted to quantify the prevalence of alternate stable states in the savannas of Africa, Australia and South America, and the tundra–taiga–grassland transitions of the circum-boreal region using satellite-derived woody canopy cover. While we agree with the logic that basins of attraction can be inferred from the relative frequencies of ecosystem states observed in space and time, we caution that the statistical methodologies underlying the satellite product used in these studies may confound our ability to infer the presence of multiple stable states. We demonstrate this point using a uniformly distributed ‘pseudo-tree cover’ database for Africa that we use to retrace the steps involved in creation of the satellite tree-cover product and subsequent analysis. We show how classification and regression tree (CART)-based products may impose discontinuities in satellite tree-cover estimates even when such discontinuities are not present in reality. As regional and global remote sensing and geospatial data become more easily accessible for ecological studies, we recommend careful consideration of how error distributions in remote sensing products may interact with the data needs and theoretical expectations of the ecological process under study.

Does warming increase the risk of civil war in Africa

The potential relationship between climate change and conflict is intriguing and warrants rigorous study. However, the proposition by Burke et al. (1) that warming may be a directly causative factor in the risk of civil war in Sub-Saharan Africa seems unlikely. The analysis of Burke et al. (1) suggests instead a tenuous historical association between warming and increased conflict. Regrettably, the authors did not elucidate further with either (i) specific case studies that demonstrate warming as a causative factor above economic, political, and sociocultural precipitants of conflict or (ii) a more thorough investigation of how climate-induced problems in agricultural sectors may result in increased conflict.

Iterative near-term ecological forecasting: Needs, opportunities, and challenges

Two foundational questions about sustainability are “How are ecosystems and the services they provide going to change in the future?” and “How do human decisions affect these trajectories?” Answering these questions requires an ability to forecast ecological processes. Unfortunately, most ecological forecasts focus on centennial-scale climate responses, therefore neither meeting the needs of near-term (daily to decadal) environmental decision-making nor allowing comparison of specific, quantitative predictions to new observational data, one of the strongest tests of scientific theory. Near-term forecasts provide the opportunity to iteratively cycle between performing analyses and updating predictions in light of new evidence. This iterative process of gaining feedback, building experience, and correcting models and methods is critical for improving forecasts. Iterative, near-term forecasting will accelerate ecological research, make it more relevant to society, and inform sustainable decision-making under high uncertainty and adaptive management. Here, we identify the immediate scientific and societal needs, opportunities, and challenges for iterative near-term ecological forecasting. Over the past decade, data volume, variety, and accessibility have greatly increased, but challenges remain in interoperability, latency, and uncertainty quantification. Similarly, ecologists have made considerable advances in applying computational, informatic, and statistical methods, but opportunities exist for improving forecast-specific theory, methods, and cyberinfrastructure. Effective forecasting will also require changes in scientific training, culture, and institutions. The need to start forecasting is now; the time for making ecology more predictive is here, and learning by doing is the fastest route to drive the science forward.

Effects of tree harvest on the stable-state dynamics of savanna and forest.

Contemporary theory on the maintenance and stability of the savanna biome has focused extensively on how climate and disturbances interact to affect tree growth and demography. In particular, the role of fire in reducing tree cover from climatic maxima is now well appreciated, and in certain cases, herbivory also strongly affects tree cover. However, in African savannas and forests, harvest of trees by humans for cooking and heating is an oft overlooked disturbance. Thus, we incorporate tree harvest into a population dynamic model of grasses, savanna saplings, savanna trees, and forest trees. We use assumptions about the differential demographic responses of savanna trees and forest trees to harvest to show how tree harvest influences tree cover, demography, and community composition. Tree harvest can erode the intrinsic basin of attraction for forest and make a state transition via fire to savanna more likely. The savanna state is generally resilient to all but high levels of tree harvest because of the resprouting abilities of savanna trees. In the absence of active fire suppression, our analysis suggests that we can expect to see large and potentially irreversible shifts from forest to savanna as demand increases for charcoal in sub-Saharan Africa. On the other hand, savanna tree species’ traits promote savanna stability in the face of low to moderate harvest pressure.

Comment on "Worldwide evidence of a unimodal relationship between productivity and plant species richness"

Fraser et al. (Reports, 17 July 2015, p. 302) report a unimodal relationship between productivity and species richness at regional and global scales, which they contrast with the results of Adler et al. (Reports, 23 September 2011, p. 1750). However, both data sets, when analyzed correctly, show clearly and consistently that productivity is a poor predictor of local species richness.

Climate change on the Shoshone National Forest, Wyoming: a synthesis of past climate, climate projections, and ecosystem implications

The Shoshone National Forest (Shoshone) covers 2.4 million acres of mountainous topography in northwest Wyoming and is a vital ecosystem that provides clean water, wildlife habitat, timber, grazing, recreational opportunities, and aesthetic value. The Shoshone has experienced and adapted to changes in climate for many millennia, and is currently experiencing a warming trend that is expected to accelerate in the next century. Climate change directly and indirectly affects the Shoshones high-elevation, mountainous terrain that supports unique and sometimes rare ecological components. Several vulnerable and very responsive resources and processes on the Shoshone could interact to produce unforeseeable or undesirable ecosystem changes, highlighting the need to identify potential resource vulnerabilities and develop adaptation pathways and flexibility in resource management options. The objective of this report is to synthesize the current understanding of the paleo and historical climate of the Shoshone as a reference point, determine what future climates may look like, and what the effects of future climate may be on natural resources. This information allows for the identification of vulnerabilities and information gaps, thereby aiding the development of adaptation tools and strategies.

Allometric convergence in savanna trees and implications for the use of plant scaling models in variable ecosystems.

Theoretical models of allometric scaling provide frameworks for understanding and predicting how and why the morphology and function of organisms vary with scale. It remains unclear, however, if the predictions of ‘universal’ scaling models for vascular plants hold across diverse species in variable environments. Phenomena such as competition and disturbance may drive allometric scaling relationships away from theoretical predictions based on an optimized tree. Here, we use a hierarchical Bayesian approach to calculate tree-specific, species-specific, and ‘global’ (i.e. interspecific) scaling exponents for several allometric relationships using tree- and branch-level data harvested from three savanna sites across a rainfall gradient in Mali, West Africa. We use these exponents to provide a rigorous test of three plant scaling models (Metabolic Scaling Theory (MST), Geometric Similarity, and Stress Similarity) in savanna systems. For the allometric relationships we evaluated (diameter vs. length, aboveground mass, stem mass, and leaf mass) the empirically calculated exponents broadly overlapped among species from diverse environments, except for the scaling exponents for length, which increased with tree cover and density. When we compare empirical scaling exponents to the theoretical predictions from the three models we find MST predictions are most consistent with our observed allometries. In those situations where observations are inconsistent with MST we find that departure from theory corresponds with expected tradeoffs related to disturbance and competitive interactions. We hypothesize savanna trees have greater length-scaling exponents than predicted by MST due to an evolutionary tradeoff between fire escape and optimization of mechanical stability and internal resource transport. Future research on the drivers of systematic allometric variation could reconcile the differences between observed scaling relationships in variable ecosystems and those predicted by ideal models such as MST.

Asynchrony among local communities stabilises ecosystem function of metacommunities

Abstract Temporal stability of ecosystem functioning increases the predictability and reliability of ecosystem services, and understanding the drivers of stability across spatial scales is important for land management and policy decisions. We used species‐level abundance data from 62 plant communities across five continents to assess mechanisms of temporal stability across spatial scales. We assessed how asynchrony (i.e. different units responding dissimilarly through time) of species and local communities stabilised metacommunity ecosystem function. Asynchrony of species increased stability of local communities, and asynchrony among local communities enhanced metacommunity stability by a wide range of magnitudes (1–315%); this range was positively correlated with the size of the metacommunity. Additionally, asynchronous responses among local communities were linked with species’ populations fluctuating asynchronously across space, perhaps stemming from physical and/or competitive differences among local communities. Accordingly, we suggest spatial heterogeneity should be a major focus for maintaining the stability of ecosystem services at larger spatial scales.

Competition and coexistence in plant communities: intraspecific competition is stronger than interspecific competition

Theory predicts that intraspecific competition should be stronger than interspecific competition for any pair of stably coexisting species, yet previous literature reviews found little support for this pattern. We screened over 5400 publications and identified 39 studies that quantified phenomenological intraspecific and interspecific interactions in terrestrial plant communities. Of the 67% of species pairs in which both intra- and interspecific effects were negative (competitive), intraspecific competition was, on average, four to five-fold stronger than interspecific competition. Of the remaining pairs, 93% featured intraspecific competition and interspecific facilitation, a situation that stabilises coexistence. The difference between intra- and interspecific effects tended to be larger in observational than experimental data sets, in field than greenhouse studies, and in studies that quantified population growth over the full life cycle rather than single fitness components. Our results imply that processes promoting stable coexistence at local scales are common and consequential across terrestrial plant communities.

Do we need demographic data to forecast plant population dynamics

Summary Rapid environmental change has generated growing interest in forecasts of future population trajectories. Traditional population models built with detailed demographic observations from one study site can address the impacts of environmental change at particular locations, but are difficult to scale up to the landscape and regional scales relevant to management decisions. An alternative is to build models using population-level data that are much easier to collect over broad spatial scales than individual-level data. However, it is unknown whether models built using population-level data adequately capture the effects of density-dependence and environmental forcing that are necessary to generate skillful forecasts. Here, we test the consequences of aggregating individual responses when forecasting the population states (percent cover) and trajectories of four perennial grass species in a semi-arid grassland in Montana, USA. We parameterized two population models for each species, one based on individual-level data (survival, growth and recruitment) and one on population-level data (percent cover), and compared their forecasting accuracy and forecast horizons with and without the inclusion of climate covariates. For both models, we used Bayesian ridge regression to weight the influence of climate covariates for optimal prediction. In the absence of climate effects, we found no significant difference between the forecast accuracy of models based on individual-level data and models based on population-level data. Climate effects were weak, but increased forecast accuracy for two species. Increases in accuracy with climate covariates were similar between model types. In our case study, percent cover models generated forecasts as accurate as those from a demographic model. For the goal of forecasting, models based on aggregated individual-level data may offer a practical alternative to data-intensive demographic models. Long time series of percent cover data already exist for many plant species. Modelers should exploit these data to predict the impacts of environmental change.