Critical slowing down of the Amazon forest after increased drought occurrence.

Dynamic ecosystems, such as the Amazon forest, are expected to show critical slowing down behavior, or slower recovery from recurrent small perturbations, as they approach an ecological threshold to a different ecosystem state. Drought occurrences are becoming more prevalent across the Amazon, with known negative effects on forest health and functioning, but their actual role in the critical slowing down patterns still remains elusive. In this study, we evaluate the effect of trends in extreme drought occurrences on temporal autocorrelation (TAC) patterns of satellite-derived indices of vegetation activity, an indicator of slowing down, between 2001 and 2019. Differentiating between extreme drought frequency, intensity, and duration, we investigate their respective effects on the slowing down response. Our results indicate that the intensity of extreme droughts is a more important driver of slowing down than their duration, although their impacts vary across the different Amazon regions. In addition, areas with more variable precipitation are already less ecologically stable and need fewer droughts to induce slowing down. We present findings indicating that most of the Amazon region does not show an increasing trend in TAC. However, the predicted increase in extreme drought intensity and frequency could potentially transition significant portions of this ecosystem into a state with altered functionality.

Reassessment of the risks of climate change for terrestrial ecosystems.

Forecasting the risks of climate change for species and ecosystems is necessary for developing targeted conservation strategies. Previous risk assessments mapped the exposure of the global land surface to changes in climate. However, this procedure is unlikely to robustly identify priority areas for conservation actions because nonlinear physiological responses and colimitation processes ensure that ecological changes will not map perfectly to the forecast climatic changes. Here, we combine ecophysiological growth models of 135,153 vascular plant species and plant growth-form information to transform ambient and future climatologies into phytoclimates, which describe the ability of climates to support the plant growth forms that characterize terrestrial ecosystems. We forecast that 33% to 68% of the global land surface will experience a significant change in phytoclimate by 2070 under representative concentration pathways RCP 2.6 and RCP 8.5, respectively. Phytoclimates without present-day analogue are forecast to emerge on 0.3-2.2% of the land surface and 0.1-1.3% of currently realized phytoclimates are forecast to disappear. Notably, the geographic pattern of change, disappearance and novelty of phytoclimates differs markedly from the pattern of analogous trends in climates detected by previous studies, thereby defining new priorities for conservation actions and highlighting the limits of using untransformed climate change exposure indices in ecological risk assessments. Our findings suggest that a profound transformation of the biosphere is underway and emphasize the need for a timely adaptation of biodiversity management practices.

Limited climatic space for alternative ecosystem states in Africa.

One of the foundational premises of ecology is that climate determines ecosystems. This has been challenged by alternative ecosystem state models, which illustrate that internal ecosystem dynamics acting on the initial ecosystem state can overwhelm the influence of climate, and by observations suggesting that climate cannot reliably discriminate forest and savanna ecosystem types. Using a novel phytoclimatic transform, which estimates the ability of climate to support different types of plants, we show that climatic suitability for evergreen trees and C4 grasses are sufficient to discriminate between forest and savanna in Africa. Our findings reassert the dominant influence of climate on ecosystems and suggest that the role of feedbacks causing alternative ecosystem states is less prevalent than has been suggested.

Transferability of correlative and process-based species distribution models revisited: A response to Booth.

Here, we respond to Booth's criticism of our paper, "Predictive ability of a process-based versus a correlative species distribution model." Booth argues that our usage of the MaxEnt model was flawed and that the conclusions of our paper are by implication flawed. We respond by clarifying that the error Booth implies we made was not made in our analysis, and we repeat statements from the original manuscript which anticipated such criticisms. In addition, we illustrate that using BIOCLIM variables in a MaxEnt analysis as recommended by Booth does not change the conclusions of the original analysis. That is, high performance in the training data domain did not equate to reliable predictions in novel data domains, and the process model transferred into novel data domains better than the correlative model did. We conclude by discussing a hidden implication of our study, namely, that process-based SDMs negate the need for BIOCLIM-type variables and therefore reframe the variable selection problem in species distribution modeling.

Predictive ability of a process-based versus a correlative species distribution model.

Species distribution modeling is a widely used tool in many branches of ecology and evolution. Evaluations of the transferability of species distribution models-their ability to predict the distribution of species in independent data domains-are, however, rare. In this study, we contrast the transferability of a process-based and a correlative species distribution model. Our case study uses 664 Australian eucalypt and acacia species. We estimate models for these species using data from their native Australia and then assess whether these models can predict the adventive range of these species. We find that the correlative model-MaxEnt-has a superior ability to describe the data in the training data domain (Australia) and that the process-based model-TTR-SDM-has a superior ability to predict the distribution of the study species outside of Australia. The implication of this analysis, that process-based models may be more appropriate than correlative models when making projections outside of the domain of the training data, needs to be tested in other case studies.

An operational definition of the biome for global change research.

Biomes are constructs for organising knowledge on the structure and functioning of the world's ecosystems, and serve as useful units for monitoring how the biosphere responds to anthropogenic drivers, including climate change. The current practice of delimiting biomes relies on expert knowledge. Recent studies have questioned the value of such biome maps for comparative ecology and global-change research, partly due to their subjective origin. Here we propose a flexible method for developing biome maps objectively. The method uses range modelling of several thousands of plant species to reveal spatial attractors for different growth-form assemblages that define biomes. The workflow is illustrated using distribution data from 23 500 African plant species. In an example application, we create a biome map for Africa and use the fitted species models to project biome shifts. In a second example, we map gradients of growth-form suitability that can be used to identify sites for comparative ecology. This method provides a flexible framework that (1) allows a range of biome types to be defined according to user needs and (2) enables projections of biome changes that emerge purely from the individualistic responses of plant species to environmental changes.

Biogeographic historical legacies in the net primary productivity of Northern Hemisphere forests.

It has been suggested that biogeographic historical legacies in plant diversity may influence ecosystem functioning. This is expected because of known diversity effects on ecosystem functions, and impacts of historical events such as past climatic changes on plant diversity. However, empirical evidence for a link between biogeographic history and present-day ecosystem functioning is still limited. Here, we explored the relationships between Late-Quaternary climate instability, species-pool size, local species and functional diversity, and the net primary productivity (NPP) of Northern Hemisphere forests using structural equation modelling. Our study confirms that past climate instability has negative effects on plant functional diversity and through that on NPP, after controlling for present-day climate, soil conditions, stand biomass and age. We conclude that global models of terrestrial plant productivity need to consider the biogeographical context to improve predictions of plant productivity and feedbacks with the climate system.

The importance of ecological memory for trophic rewilding as an ecosystem restoration approach.

Increasing human pressure on strongly defaunated ecosystems is characteristic of the Anthropocene and calls for proactive restoration approaches that promote self-sustaining, functioning ecosystems. However, the suitability of novel restoration concepts such as trophic rewilding is still under discussion given fragmentary empirical data and limited theory development. Here, we develop a theoretical framework that integrates the concept of 'ecological memory' into trophic rewilding. The ecological memory of an ecosystem is defined as an ecosystem's accumulated abiotic and biotic material and information legacies from past dynamics. By summarising existing knowledge about the ecological effects of megafauna extinction and rewilding across a large range of spatial and temporal scales, we identify two key drivers of ecosystem responses to trophic rewilding: (i) impact potential of (re)introduced megafauna, and (ii) ecological memory characterising the focal ecosystem. The impact potential of (re)introduced megafauna species can be estimated from species properties such as lifetime per capita engineering capacity, population density, home range size and niche overlap with resident species. The importance of ecological memory characterising the focal ecosystem depends on (i) the absolute time since megafauna loss, (ii) the speed of abiotic and biotic turnover, (iii) the strength of species interactions characterising the focal ecosystem, and (iv) the compensatory capacity of surrounding source ecosystems. These properties related to the focal and surrounding ecosystems mediate material and information legacies (its ecological memory) and modulate the net ecosystem impact of (re)introduced megafauna species. We provide practical advice about how to quantify all these properties while highlighting the strong link between ecological memory and historically contingent ecosystem trajectories. With this newly established ecological memory-rewilding framework, we hope to guide future empirical studies that investigate the ecological effects of trophic rewilding and other ecosystem-restoration approaches. The proposed integrated conceptual framework should also assist managers and decision makers to anticipate the possible trajectories of ecosystem dynamics after restoration actions and to weigh plausible alternatives. This will help practitioners to develop adaptive management strategies for trophic rewilding that could facilitate sustainable management of functioning ecosystems in an increasingly human-dominated world.