Self-organization as a mechanism of resilience in dryland ecosystems.

Self-organized spatial patterns are a common feature of complex systems, ranging from microbial communities to mussel beds and drylands. While the theoretical implications of these patterns for ecosystem-level processes, such as functioning and resilience, have been extensively studied, empirical evidence remains scarce. To address this gap, we analyzed global drylands along an aridity gradient using remote sensing, field data, and modeling. We found that the spatial structure of the vegetation strengthens as aridity increases, which is associated with the maintenance of a high level of soil multifunctionality, even as aridity levels rise up to a certain threshold. The combination of these results with those of two individual-based models indicate that self-organized vegetation patterns not only form in response to stressful environmental conditions but also provide drylands with the ability to adapt to changing conditions while maintaining their functioning, an adaptive capacity which is lost in degraded ecosystems. Self-organization thereby plays a vital role in enhancing the resilience of drylands. Overall, our findings contribute to a deeper understanding of the relationship between spatial vegetation patterns and dryland resilience. They also represent a significant step forward in the development of indicators for ecosystem resilience, which are critical tools for managing and preserving these valuable ecosystems in a warmer and more arid world.

Scaling up our understanding of tipping points.

Anthropogenic activities are increasingly affecting ecosystems across the globe. Meanwhile, empirical and theoretical evidence suggest that natural systems can exhibit abrupt collapses in response to incremental increases in the stressors, sometimes with dramatic ecological and economic consequences. These catastrophic shifts are faster and larger than expected from the changes in the stressors and happen once a tipping point is crossed. The primary mechanisms that drive ecosystem responses to perturbations lie in their architecture of relationships, i.e. how species interact with each other and with the physical environment and the spatial structure of the environment. Nonetheless, existing theoretical work on catastrophic shifts has so far largely focused on relatively simple systems that have either few species and/or no spatial structure. This work has laid a critical foundation for understanding how abrupt responses to incremental stressors are possible, but it remains difficult to predict (let alone manage) where or when they are most likely to occur in more complex real-world settings. Here, we discuss how scaling up our investigations of catastrophic shifts from simple to more complex-species rich and spatially structured-systems could contribute to expanding our understanding of how nature works and improve our ability to anticipate the effects of global change on ecological systems. This article is part of the theme issue 'Ecological complexity and the biosphere: the next 30 years'.

gen3sis: A general engine for eco-evolutionary simulations of the processes that shape Earth's biodiversity.

Understanding the origins of biodiversity has been an aspiration since the days of early naturalists. The immense complexity of ecological, evolutionary, and spatial processes, however, has made this goal elusive to this day. Computer models serve progress in many scientific fields, but in the fields of macroecology and macroevolution, eco-evolutionary models are comparatively less developed. We present a general, spatially explicit, eco-evolutionary engine with a modular implementation that enables the modeling of multiple macroecological and macroevolutionary processes and feedbacks across representative spatiotemporally dynamic landscapes. Modeled processes can include species' abiotic tolerances, biotic interactions, dispersal, speciation, and evolution of ecological traits. Commonly observed biodiversity patterns, such as α, ß, and γ diversity, species ranges, ecological traits, and phylogenies, emerge as simulations proceed. As an illustration, we examine alternative hypotheses expected to have shaped the latitudinal diversity gradient (LDG) during the Earth's Cenozoic era. Our exploratory simulations simultaneously produce multiple realistic biodiversity patterns, such as the LDG, current species richness, and range size frequencies, as well as phylogenetic metrics. The model engine is open source and available as an R package, enabling future exploration of various landscapes and biological processes, while outputs can be linked with a variety of empirical biodiversity patterns. This work represents a key toward a numeric, interdisciplinary, and mechanistic understanding of the physical and biological processes that shape Earth's biodiversity.

Integrating the underlying structure of stochasticity into community ecology.

Stochasticity is a core component of ecology, as it underlies key processes that structure and create variability in nature. Despite its fundamental importance in ecological systems, the concept is often treated as synonymous with unpredictability in community ecology, and studies tend to focus on single forms of stochasticity rather than taking a more holistic view. This has led to multiple narratives for how stochasticity mediates community dynamics. Here, we present a framework that describes how different forms of stochasticity (notably demographic and environmental stochasticity) combine to provide underlying and predictable structure in diverse communities. This framework builds on the deep ecological understanding of stochastic processes acting at individual and population levels and in modules of a few interacting species. We support our framework with a mathematical model that we use to synthesize key literature, demonstrating that stochasticity is more than simple uncertainty. Rather, stochasticity has profound and predictable effects on community dynamics that are critical for understanding how diversity is maintained. We propose next steps that ecologists might use to explore the role of stochasticity for structuring communities in theoretical and empirical systems, and thereby enhance our understanding of community dynamics.

Oceanic island biogeography through the lens of the general dynamic model: assessment and prospect.

The general dynamic model of oceanic island biogeography (GDM) has added a new dimension to theoretical island biogeography in recognizing that geological processes are key drivers of the evolutionary processes of diversification and extinction within remote islands. It provides a dynamic and essentially non-equilibrium framework generating novel predictions for emergent diversity properties of oceanic islands and archipelagos. Its publication in 2008 coincided with, and spurred on, renewed attention to the dynamics of remote islands. We review progress, both in testing the GDM's predictions and in developing and enhancing ecological-evolutionary understanding of oceanic island systems through the lens of the GDM. In particular, we focus on four main themes: (i) macroecological tests using a space-for-time rationale; (ii) extensions of theory to islands following different patterns of ontogeny; (iii) the implications of GDM dynamics for lineage diversification and trait evolution; and (iv) the potential for downscaling GDM dynamics to local-scale ecological patterns and processes within islands. We also consider the implications of the GDM for understanding patterns of non-native species diversity. We demonstrate the vitality of the field of island biogeography by identifying a range of potentially productive lines for future research.

Benchmarking novel approaches for modelling species range dynamics.

Increasing biodiversity loss due to climate change is one of the most vital challenges of the 21st century. To anticipate and mitigate biodiversity loss, models are needed that reliably project species' range dynamics and extinction risks. Recently, several new approaches to model range dynamics have been developed to supplement correlative species distribution models (SDMs), but applications clearly lag behind model development. Indeed, no comparative analysis has been performed to evaluate their performance. Here, we build on process-based, simulated data for benchmarking five range (dynamic) models of varying complexity including classical SDMs, SDMs coupled with simple dispersal or more complex population dynamic models (SDM hybrids), and a hierarchical Bayesian process-based dynamic range model (DRM). We specifically test the effects of demographic and community processes on model predictive performance. Under current climate, DRMs performed best, although only marginally. Under climate change, predictive performance varied considerably, with no clear winners. Yet, all range dynamic models improved predictions under climate change substantially compared to purely correlative SDMs, and the population dynamic models also predicted reasonable extinction risks for most scenarios. When benchmarking data were simulated with more complex demographic and community processes, simple SDM hybrids including only dispersal often proved most reliable. Finally, we found that structural decisions during model building can have great impact on model accuracy, but prior system knowledge on important processes can reduce these uncertainties considerably. Our results reassure the clear merit in using dynamic approaches for modelling species' response to climate change but also emphasize several needs for further model and data improvement. We propose and discuss perspectives for improving range projections through combination of multiple models and for making these approaches operational for large numbers of species.

Molecular phylogeny of the neotropical genus Christensonella (Orchidaceae, Maxillariinae): species delimitation and insights into chromosome evolution.

BACKGROUND AND AIMS: Species' boundaries applied within Christensonella have varied due to the continuous pattern of variation and mosaic distribution of diagnostic characters. The main goals of this study were to revise the species' delimitation and propose a more stable classification for this genus. In order to achieve these aims phylogenetic relationships were inferred using DNA sequence data and cytological diversity within Christensonella was examined based on chromosome counts and heterochromatin patterns. The results presented describe sets of diagnostic morphological characters that can be used for species' identification. METHODS: Phylogenetic studies were based on sequence data of nuclear and plastid regions, analysed using maximum parsimony and maximum likelihood criteria. Cytogenetic observations of mitotic cells were conducted using CMA and DAPI fluorochromes. KEY RESULTS: Six of 21 currently accepted species were recovered. The results also support recognition of the 'C. pumila' clade as a single species. Molecular phylogenetic relationships within the 'C. acicularis-C. madida' and 'C. ferdinandiana-C. neowiedii' species' complexes were not resolved and require further study. Deeper relationships were incongruent between plastid and nuclear trees, but with no strong bootstrap support for either, except for the position of C. vernicosa. Cytogenetic data indicated chromosome numbers of 2n = 36, 38 and 76, and with substantial variation in the presence and location of CMA/DAPI heterochromatin bands. CONCLUSIONS: The recognition of ten species of Christensonella is proposed according to the molecular and cytogenetic patterns observed. In addition, diagnostic morphological characters are presented for each recognized species. Banding patterns and chromosome counts suggest the occurrence of centric fusion/fission events, especially for C. ferdinandiana. The results suggest that 2n = 36 karyotypes evolved from 2n = 38 through descendent dysploidy. Patterns of heterochromatin distribution and other karyotypic data proved to be a valuable source of information to understand evolutionary patterns within Maxillariinae orchids.

Heterochromatin diversity and its co-localization with 5S and 45S rDNA sites in chromosomes of four Maxillaria species (Orchidaceae)

We investigated four orchids of the genus Maxillaria (M. discolor, M. acicularis, M. notylioglossa and M. desvauxiana) in regard to the position of heterochromatin blocks as revealed using chromomycin A3 (CMA) and 4'-6-diamidino-2-phenylindole (DAPI) fluorochrome staining and 5S and 45S rDNA sites using fluorescence in situ hybridization (FISH). The species showed differences in chromosome number and a diversified pattern of CMA+ and DAPI+ bands, including heteromorphism for CMA+ bands. The 5S and 45S rDNA sites also varied in number and most of them were co-localized with CMA+ bands. The relationship between 5S rDNA sites and CMA+ bands was more evident in M. notylioglossa, in which the brighter CMA+ bands were associated with large 5S rDNA sites. However, not all 5S and 45S rDNA sites were co-localized with CMA+ bands, probably due to technical constraints. We compare these results to banding data from other species and suggest that not all blocks of tandemly repetitive sequences, such as 5S rDNA sites, can be observed as heterochromatin blocks.