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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., Competing Interests: Competing interests statement:The authors declare no competing interest.

The ubiquitous use of computational work for data generation, processing, and modeling increased the importance of digital documentation in improving research quality and impact. Computational notebooks are files that contain descriptive text, as well as code and its outputs, in a single, dynamic, and visually appealing file that is easier to understand by nonspecialists. Traditionally used by data scientists when producing reports and informing decision-making, the use of this tool in research publication is not common, despite its potential to increase research impact and quality. For a single study, the content of such documentation partially overlaps with that of classical lab notebooks and that of the scientific manuscript reporting the study. Therefore, to minimize the amount of work required to manage all the files related to these contents and optimize their production, we present a starter kit to facilitate the implementation of computational notebooks in the research process, including publication. The kit contains the template of a computational notebook integrated into a research project that employs R, Python, or Julia. Using examples of ecological studies, we show how computational notebooks also foster the implementation of principles of Open Science, such as reproducibility and traceability. The kit is designed for beginners, but at the end we present practices that can be gradually implemented to develop a fully digital research workflow. Our hope is that such minimalist yet effective starter kit will encourage researchers to adopt this practice in their workflow, regardless of their computational background., Competing Interests: The authors have declared that no competing interests exist.

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'.

Background and Aims: The entangled relationship of chromosome number and genome size with species distribution has been the subject of study for almost a century, but remains an open question due to previous ecological and phylogenetic knowledge constraints. To better address this subject, we used the clade Maxillariinae, a widely distributed and karyotypically known orchid group, as a model system to infer such relationships in a robust methodological framework., Methods: Based on the literature and new data, we gathered the chromosome number and genome size for 93 and 64 species, respectively. We built a phylogenetic hypothesis and assessed the best macroevolutionary model for both genomic traits. Additionally, we collected together ecological data (preferences for bioclimatic variables, elevation and habit) used as explanatory variables in multivariate phylogenetic models explaining genomic traits. Finally, the impact of polyploidy was estimated by running the analyses with and without polyploids in the sample., Key Results: The association between genomic and ecological data varied depending on whether polyploids were considered or not. Without polyploids, chromosome number failed to present consistent associations with ecological variables. With polyploids, there was a tendency to waive epiphytism and colonize new habitats outside humid forests. The genome size showed association with ecological variables: without polyploids, genome increase was associated with flexible habits, with higher elevation and with drier summers; with polyploids, genome size increase was associated with colonizing drier environments., Conclusions: The chromosome number and genome size variations, essential but neglected traits in the ecological niche, are shaped in the Maxillariinae by both neutral and adaptive evolution. Both genomic traits are partially correlated to bioclimatic variables and elevation, even when controlling for phylogenetic constraints. While polyploidy was associated with shifts in the environmental niche, the genome size emerges as a central trait in orchid evolution by the association between small genome size and epiphytism, a key innovation to Neotropical orchid diversification., (© The Author(s) 2022. Published by Oxford University Press on behalf of the Annals of Botany Company. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

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, β, 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., Competing Interests: The authors have declared that no competing interests exist.

Forest canopies play a crucial role in structuring communities of vascular epiphytes by providing substrate for colonization, by locally varying microclimate, and by causing epiphyte mortality due to branch or tree fall. However, as field studies in the three-dimensional habitat of epiphytes are generally challenging, our understanding of how forest structure and dynamics influence the structure and dynamics of epiphyte communities is scarce.Mechanistic models can improve our understanding of epiphyte community dynamics. We present such a model that couples dispersal, growth, and mortality of individual epiphytes with substrate dynamics, obtained from a three-dimensional functional-structural forest model, allowing the study of forest-epiphyte interactions. After validating the epiphyte model with independent field data, we performed several theoretical simulation experiments to assess how (a) differences in natural forest dynamics, (b) selective logging, and (c) forest fragmentation could influence the long-term dynamics of epiphyte communities.The proportion of arboreal substrate occupied by epiphytes (i.e., saturation level) was tightly linked with forest dynamics and increased with decreasing forest turnover rates. While species richness was, in general, negatively correlated with forest turnover rates, low species numbers in forests with very-low-turnover rates were due to competitive exclusion when epiphyte communities became saturated. Logging had a negative impact on epiphyte communities, potentially leading to a near-complete extirpation of epiphytes when the simulated target diameters fell below a threshold. Fragment size had no effect on epiphyte abundance and saturation level but correlated positively with species numbers.Synthesis: The presented model is a first step toward studying the dynamic forest-epiphyte interactions in an agent-based modeling framework. Our study suggests forest dynamics as key factor in controlling epiphyte communities. Thus, both natural and human-induced changes in forest dynamics, for example, increased mortality rates or the loss of large trees, pose challenges for epiphyte conservation., Competing Interests: The authors have no conflict of interest to declare., (© 2021 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.)

Laser-induced breakdown spectroscopy (LIBS) for atomic multi-elementary analyses, and Fourier transform infrared spectroscopy (FTIR) for molecular identification, are often suggested as the most versatile spectroscopic techniques. The present work aimed to evaluate the performance of both techniques, LIBS and FTIR, combined with principal component analysis (PCA) and machine learning (ML) algorithms in the detection of the composition analysis and differentiation of four different types of rice, white, brown, black, and red. The two techniques were primarily used to obtain the elemental and molecular qualitative characterization of rice samples. Then, LIBS and FTIR data sets were subjected to PCA and supervised ML analysis to investigate which main chemical features were responsible for nutritional differences for the white (milled) and colored rice samples. In particular, PCA data analysis suggested that protein, fatty acids, and magnesium were the highest contributors to the sample's differentiation. The ML analysis based on this information yielded a 100% level of accuracy, sensitivity, and specificity on sample classification. In conclusion, LIBS and FTIR coupled with multivariate analysis were confirmed as promising tools alternative to traditional analytical techniques for composition analysis and differentiation when subtle chemical variations were observed.

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., (© 2019 The Authors. Ecology published by Wiley Periodicals, Inc. on behalf of Ecological Society of America.)

The research of a generation of ecologists was catalysed by the recognition that the number and identity of species in communities influences the functioning of ecosystems. The relationship between biodiversity and ecosystem functioning (BEF) is most often examined by controlling species richness and randomising community composition. In natural systems, biodiversity changes are often part of a bigger community assembly dynamic. Therefore, focusing on community assembly and the functioning of ecosystems (CAFE), by integrating both species richness and composition through species gains, losses and changes in abundance, will better reveal how community changes affect ecosystem function. We synthesise the BEF and CAFE perspectives using an ecological application of the Price equation, which partitions the contributions of richness and composition to function. Using empirical examples, we show how the CAFE approach reveals important contributions of composition to function. These examples show how changes in species richness and composition driven by environmental perturbations can work in concert or antagonistically to influence ecosystem function. Considering how communities change in an integrative fashion, rather than focusing on one axis of community structure at a time, will improve our ability to anticipate and predict changes in ecosystem function., (© 2017 The Authors. Ecology Letters published by CNRS and John Wiley & Sons Ltd.)

Island biogeographical models consider islands either as geologically static with biodiversity resulting from ecologically neutral immigration-extinction dynamics, or as geologically dynamic with biodiversity resulting from immigration-speciation-extinction dynamics influenced by changes in island characteristics over millions of years. Present climate and spatial arrangement of islands, however, are rather exceptional compared to most of the Late Quaternary, which is characterized by recurrent cooler and drier glacial periods. These climatic oscillations over short geological timescales strongly affected sea levels and caused massive changes in island area, isolation and connectivity, orders of magnitude faster than the geological processes of island formation, subsidence and erosion considered in island theory. Consequences of these oscillations for present biodiversity remain unassessed. Here we analyse the effects of present and Last Glacial Maximum (LGM) island area, isolation, elevation and climate on key components of angiosperm diversity on islands worldwide. We find that post-LGM changes in island characteristics, especially in area, have left a strong imprint on present diversity of endemic species. Specifically, the number and proportion of endemic species today is significantly higher on islands that were larger during the LGM. Native species richness, in turn, is mostly determined by present island characteristics. We conclude that an appreciation of Late Quaternary environmental change is essential to understand patterns of island endemism and its underlying evolutionary dynamics.

Temperature and water status profiles during the growing season are the most important factors influencing the ripening of wine grapes. To model weather influences on the quality and productivity of the vintages, it is necessary to partition the growing season into smaller growth intervals in which weather variables are evaluated. A significant part of past and ongoing research on the relationships between weather and wine quality uses calendar-defined intervals to partition the growing season. The phenology of grapevines is not determined by calendar dates but by several factors such as accumulated heat. To examine the accuracy of different approaches, this work analyzed the difference in average temperature and accumulated precipitation using growth intervals with boundaries defined by means of estimated historical phenological dates and intervals defined by means of accumulated heat or average calendar dates of the Douro Valley of Portugal. The results show that in situations where there is an absence of historical phenological dates and/or no available data that makes the estimation of those dates possible, it is more accurate to use grapevine heat requirements than calendar dates to define growth interval boundaries. Additionally, we analyzed the ability of the length of growth intervals with boundaries based on grapevine heat requirements to differentiate the best from the worst vintage years with the results showing that vintage quality is strongly related to the phenological events. Finally, we analyzed the variability of growth interval lengths in the Douro Valley during 1980-2009 with the results showing a tendency for earlier grapevine physiology.

Island biogeographic studies traditionally treat single islands as units of analysis. This ignores the fact that most islands are spatially nested within archipelagos. Here, we took a fundamentally different approach and focused on entire archipelagos using species richness of vascular plants on 23 archipelagos worldwide and their 174 constituent islands. We assessed differential effects of biogeographic factors (area, isolation, age, elevation), current and past climate (temperature, precipitation, seasonality, climate change velocity) and intra-archipelagic spatial structure (archipelago area, number of islands, area range, connectivity, environmental volume, inter-island distance) on plant diversity. Species diversity of each archipelago (γ) was additively partitioned into α, β, nestedness and replacement β-components to investigate the relative importance of environmental and spatial drivers. Multiple regressions revealed strong effects of biogeography and climate on α and γ, whereas spatial factors, particularly number of islands, inter-island distance and area range, were key to explain β. Structural equation models additionally suggested that γ is predominantly determined by indirect abiotic effects via its components, particularly β. This highlights that β and the spatial arrangement of islands are essential to understand insular ecology and evolution. Our methodological framework can be applied more widely to other taxa and archipelago-like systems, allowing new insights into biodiversity origin and maintenance.

The C cycle in the Brazilian forests is very important, mainly for issues addressed to climate changes and soil management. Assessing and understanding C dynamics in Amazonian soils can help scientists to improve models and anticipate scenarios. New methods that allow soil C measurements in situ are a crucial approach for this kind of region, due to the costs for collecting and sending soil samples from the rainforest to the laboratory. Laser-induced breakdown spectroscopy (LIBS) is a multielemental atomic emission spectroscopy technique that employs a highly energetic laser pulse for plasma production and requires neither sample preparation nor the use of reagents. As LIBS takes less than 10 s per sample measurement, it is considered a promising technique for in situ soil analyses. One of the limitations of portable LIBS systems, however, is the common overlap of the emission lines that cannot be spectrally resolved. In this study a method was developed capable of separating the Al interference from the C emission line in LIBS measurements. Two typical forest Brazilian soils rich in Al were investigated: a spodosol (Amazon Forest) and an oxisol (Atlantic Forest). Fifty-three samples were collected and analyzed using a low-resolution LIBS apparatus to measure the intensities of C lines. In particular, two C lines were evaluated, at 193.03 and 247.86 nm. The line at 247.86 nm showed very strong interference with Fe and Si lines, which made quantitative analysis difficult. The line at 193.03 nm showed interference with atomic and ionic Al emission lines, but this problem could be solved by applying a correction method that was proposed and tested in this work. The line at 247.86 was used to assess the proposed model. The strong correlation (Pearson's coefficient R=0.91) found between the LIBS values and those obtained by a reference technique (dry combustion by an elemental analyzer) supported the validity of the proposed method.

Wildflower harvesting is an economically important activity of which the ecological effects are poorly understood. We assessed how harvesting of flowers affects shrub persistence and abundance at multiple spatial extents. To this end, we built a process-based model to examine the mean persistence and abundance of wild shrubs whose flowers are subject to harvest (serotinous Proteaceae in the South African Cape Floristic Region). First, we conducted a general sensitivity analysis of how harvesting affects persistence and abundance at nested spatial extents. For most spatial extents and combinations of demographic parameters, persistence and abundance of flowering shrubs decreased abruptly once harvesting rate exceeded a certain threshold. At larger extents, metapopulations supported higher harvesting rates before their persistence and abundance decreased, but persistence and abundance also decreased more abruptly due to harvesting than at smaller extents. This threshold rate of harvest varied with species' dispersal ability, maximum reproductive rate, adult mortality, probability of extirpation or local extinction, strength of Allee effects, and carrying capacity. Moreover, spatial extent interacted with Allee effects and probability of extirpation because both these demographic properties affected the response of local populations to harvesting more strongly than they affected the response of metapopulations. Subsequently, we simulated the effects of harvesting on three Cape Floristic Region Proteaceae species and found that these species reacted differently to harvesting, but their persistence and abundance decreased at low rates of harvest. Our estimates of harvesting rates at maximum sustainable yield differed from those of previous investigations, perhaps because researchers used different estimates of demographic parameters, models of population dynamics, and spatial extent than we did. Good demographic knowledge and careful identification of the spatial extent of interest increases confidence in assessments and monitoring of the effects of harvesting. Our general sensitivity analysis improved understanding of harvesting effects on metapopulation dynamics and allowed qualitative assessment of the probability of extirpation of poorly studied species., (©2010 Society for Conservation Biology.)

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.