Emergent global patterns of ecosystem structure and function from a mechanistic general ecosystem model

Assessing contemporary Arctic habitat availability for a woolly mammoth proxy

Interest continues to grow in Arctic megafaunal ecological engineering, but, since the mass extinction of megafauna ~ 12-15 ka, key physiographic variables and available forage continue to change. Here we sought to assess the extent to which contemporary Arctic ecosystems are conducive to the rewilding of megaherbivores, using a woolly mammoth (M. primigenius) proxy as a model species. We first perform a literature review on woolly mammoth dietary habits. We then leverage Oak Ridge National Laboratories Distributive Active Archive Center Global Aboveground and Belowground Biomass Carbon Density Maps to generate aboveground biomass carbon density estimates in plant functional types consumed by the woolly mammoth at 300 m resolution on Alaska's North Slope. We supplement these analyses with a NASA Arctic Boreal Vulnerability Experiment dataset to downgrade overall biomass estimates to digestible levels. We further downgrade available forage by using a conversion factor representing the relationship between total biomass and net primary productivity (NPP) for arctic vegetation types. Integrating these estimates with the forage needs of woolly mammoths, we conservatively estimate Alaska's North Slope could support densities of 0.0-0.38 woolly mammoth km-2 (mean 0.13) across a variety of habitats. These results may inform innovative rewilding strategies.

Decline of a distinct coral reef holobiont community under ocean acidification

BACKGROUND

Microbes play vital roles across coral reefs both in the environment and inside and upon macrobes (holobionts), where they support critical functions such as nutrition and immune system modulation. These roles highlight the potential ecosystem-level importance of microbes, yet most knowledge of microbial functions on reefs is derived from a small set of holobionts such as corals and sponges. Declining seawater pH - an important global coral reef stressor - can cause ecosystem-level change on coral reefs, providing an opportunity to study the role of microbes at this scale. We use an in situ experimental approach to test the hypothesis that under such ocean acidification (OA), known shifts among macrobe trophic and functional groups may drive a general ecosystem-level response extending across macrobes and microbes, leading to reduced distinctness between the benthic holobiont community microbiome and the environmental microbiome.

RESULTS

We test this hypothesis using genetic and chemical data from benthic coral reef community holobionts sampled across a pH gradient from CO2 seeps in Papua New Guinea. We find support for our hypothesis; under OA, the microbiome and metabolome of the benthic holobiont community become less compositionally distinct from the sediment microbiome and metabolome, suggesting that benthic macrobe communities are colonised by environmental microbes to a higher degree under OA conditions. We also find a simplification and homogenisation of the benthic photosynthetic community, and an increased abundance of fleshy macroalgae, consistent with previously observed reef microbialisation.

CONCLUSIONS

We demonstrate a novel structural shift in coral reefs involving macrobes and microbes: that the microbiome of the benthic holobiont community becomes less distinct from the sediment microbiome under OA. Our findings suggest that microbialisation and the disruption of macrobe trophic networks are interwoven general responses to environmental stress, pointing towards a universal, undesirable, and measurable form of ecosystem changed. Video Abstract.

The bii4africa dataset of faunal and floral population intactness estimates across Africa's major land uses

Sub-Saharan Africa is under-represented in global biodiversity datasets, particularly regarding the impact of land use on species' population abundances. Drawing on recent advances in expert elicitation to ensure data consistency, 200 experts were convened using a modified-Delphi process to estimate 'intactness scores': the remaining proportion of an 'intact' reference population of a species group in a particular land use, on a scale from 0 (no remaining individuals) to 1 (same abundance as the reference) and, in rare cases, to 2 (populations that thrive in human-modified landscapes). The resulting bii4africa dataset contains intactness scores representing terrestrial vertebrates (tetrapods: ±5,400 amphibians, reptiles, birds, mammals) and vascular plants (±45,000 forbs, graminoids, trees, shrubs) in sub-Saharan Africa across the region's major land uses (urban, cropland, rangeland, plantation, protected, etc.) and intensities (e.g., large-scale vs smallholder cropland). This dataset was co-produced as part of the Biodiversity Intactness Index for Africa Project. Additional uses include assessing ecosystem condition; rectifying geographic/taxonomic biases in global biodiversity indicators and maps; and informing the Red List of Ecosystems.

On the History of Ecosystem Dynamical Modeling: The Rise and Promises of Qualitative Models

Ecosystem modeling is a complex and multidisciplinary modeling problem which emerged in the 1950s. It takes advantage of the computational turn in sciences to better understand anthropogenic impacts and improve ecosystem management. For that purpose, ecosystem simulation models based on difference or differential equations were built. These models were relevant for studying dynamical phenomena and still are. However, they face important limitations in data-poor situations. As a response, several formal and non-formal qualitative dynamical modeling approaches were independently developed to overcome some limitations of the existing methods. Qualitative approaches allow studying qualitative dynamics as relevant abstractions of those provided by quantitative models (e.g., response to press perturbations). Each modeling framework can be viewed as a different assemblage of properties (e.g., determinism, stochasticity or synchronous update of variable values) designed to satisfy some scientific objectives. Based on four stated objectives commonly found in complex environmental sciences ((1) grasping qualitative dynamics, (2) making as few assumptions as possible about parameter values, (3) being explanatory and (4) being predictive), our objectives were guided by the wish to model complex and multidisciplinary issues commonly found in ecosystem modeling. We then discussed the relevance of existing modeling approaches and proposed the ecological discrete-event networks (EDEN) modeling framework for this purpose. The EDEN models propose a qualitative, discrete-event, partially synchronous and possibilistic view of ecosystem dynamics. We discussed each of these properties through ecological examples and existing analysis techniques for such models and showed how relevant they are for environmental science studies.

Impacts of large herbivores on terrestrial ecosystems

Large herbivores play unique ecological roles and are disproportionately imperiled by human activity. As many wild populations dwindle towards extinction, and as interest grows in restoring lost biodiversity, research on large herbivores and their ecological impacts has intensified. Yet, results are often conflicting or contingent on local conditions, and new findings have challenged conventional wisdom, making it hard to discern general principles. Here, we review what is known about the ecosystem impacts of large herbivores globally, identify key uncertainties, and suggest priorities to guide research. Many findings are generalizable across ecosystems: large herbivores consistently exert top-down control of plant demography, species composition, and biomass, thereby suppressing fires and the abundance of smaller animals. Other general patterns do not have clearly defined impacts: large herbivores respond to predation risk but the strength of trophic cascades is variable; large herbivores move vast quantities of seeds and nutrients but with poorly understood effects on vegetation and biogeochemistry. Questions of the greatest relevance for conservation and management are among the least certain, including effects on carbon storage and other ecosystem functions and the ability to predict outcomes of extinctions and reintroductions. A unifying theme is the role of body size in regulating ecological impact. Small herbivores cannot fully substitute for large ones, and large-herbivore species are not functionally redundant - losing any, especially the largest, will alter net impact, helping to explain why livestock are poor surrogates for wild species. We advocate leveraging a broad spectrum of techniques to mechanistically explain how large-herbivore traits and environmental context interactively govern the ecological impacts of these animals.

Predicting ecosystem productivity based on plant community traits

With the rapid accumulation of plant trait data, major opportunities have arisen for the integration of these data into predicting ecosystem primary productivity across a range of spatial extents. Traditionally, traits have been used to explain physiological productivity at cell, organ, or plant scales, but scaling up to the ecosystem scale has remained challenging. Here, we show the need to combine measures of community-level traits and environmental factors to predict ecosystem productivity at landscape or biogeographic scales. We show how theory can extend the production ecology equation to enormous potential for integrating traits into ecological models that estimate productivity-related ecosystem functions across ecological scales and to anticipate the response of terrestrial ecosystems to global change.

A Conceptual Framework to Integrate Biodiversity, Ecosystem Function, and Ecosystem Service Models

Global biodiversity and ecosystem service models typically operate independently. Ecosystem service projections may therefore be overly optimistic because they do not always account for the role of biodiversity in maintaining ecological functions. We review models used in recent global model intercomparison projects and develop a novel model integration framework to more fully account for the role of biodiversity in ecosystem function, a key gap for linking biodiversity changes to ecosystem services. We propose two integration pathways. The first uses empirical data on biodiversity-ecosystem function relationships to bridge biodiversity and ecosystem function models and could currently be implemented globally for systems and taxa with sufficient data. We also propose a trait-based approach involving greater incorporation of biodiversity into ecosystem function models. Pursuing both approaches will provide greater insight into biodiversity and ecosystem services projections. Integrating biodiversity, ecosystem function, and ecosystem service modeling will enhance policy development to meet global sustainability goals.

Process-explicit models reveal the structure and dynamics of biodiversity patterns

With ever-growing data availability and computational power at our disposal, we now have the capacity to use process-explicit models more widely to reveal the ecological and evolutionary mechanisms responsible for spatiotemporal patterns of biodiversity. Most research questions focused on the distribution of diversity cannot be answered experimentally, because many important environmental drivers and biological constraints operate at large spatiotemporal scales. However, we can encode proposed mechanisms into models, observe the patterns they produce in virtual environments, and validate these patterns against real-world data or theoretical expectations. This approach can advance understanding of generalizable mechanisms responsible for the distributions of organisms, communities, and ecosystems in space and time, advancing basic and applied science. We review recent developments in process-explicit models and how they have improved knowledge of the distribution and dynamics of life on Earth, enabling biodiversity to be better understood and managed through a deeper recognition of the processes that shape genetic, species, and ecosystem diversity.

Process-explicit models reveal the structure and dynamics of biodiversity patterns

With ever-growing data availability and computational power at our disposal, we now have the capacity to use process-explicit models more widely to reveal the ecological and evolutionary mechanisms responsible for spatiotemporal patterns of biodiversity. Most research questions focused on the distribution of diversity cannot be answered experimentally, because many important environmental drivers and biological constraints operate at large spatiotemporal scales. However, we can encode proposed mechanisms into models, observe the patterns they produce in virtual environments, and validate these patterns against real-world data or theoretical expectations. This approach can advance understanding of generalizable mechanisms responsible for the distributions of organisms, communities, and ecosystems in space and time, advancing basic and applied science. We review recent developments in process-explicit models and how they have improved knowledge of the distribution and dynamics of life on Earth, enabling biodiversity to be better understood and managed through a deeper recognition of the processes that shape genetic, species, and ecosystem diversity.

AVONET: morphological, ecological and geographical data for all birds

Functional traits offer a rich quantitative framework for developing and testing theories in evolutionary biology, ecology and ecosystem science. However, the potential of functional traits to drive theoretical advances and refine models of global change can only be fully realised when species-level information is complete. Here we present the AVONET dataset containing comprehensive functional trait data for all birds, including six ecological variables, 11 continuous morphological traits, and information on range size and location. Raw morphological measurements are presented from 90,020 individuals of 11,009 extant bird species sampled from 181 countries. These data are also summarised as species averages in three taxonomic formats, allowing integration with a global phylogeny, geographical range maps, IUCN Red List data and the eBird citizen science database. The AVONET dataset provides the most detailed picture of continuous trait variation for any major radiation of organisms, offering a global template for testing hypotheses and exploring the evolutionary origins, structure and functioning of biodiversity.

Coding for Life: Designing a Platform for Projecting and Protecting Global Biodiversity

Time is running out to limit further devastating losses of biodiversity and nature's contributions to humans. Addressing this crisis requires accurate predictions about which species and ecosystems are most at risk to ensure efficient use of limited conservation and management resources. We review existing biodiversity projection models and discover problematic gaps. Current models usually cannot easily be reconfigured for other species or systems, omit key biological processes, and cannot accommodate feedbacks with Earth system dynamics. To fill these gaps, we envision an adaptable, accessible, and universal biodiversity modeling platform that can project essential biodiversity variables, explore the implications of divergent socioeconomic scenarios, and compare conservation and management strategies. We design a roadmap for implementing this vision and demonstrate that building this biodiversity forecasting platform is possible and practical.

A New Approach to Evaluate and Reduce Uncertainty of Model-Based Biodiversity Projections for Conservation Policy Formulation

Biodiversity projections with uncertainty estimates under different climate, land-use, and policy scenarios are essential to setting and achieving international targets to mitigate biodiversity loss. Evaluating and improving biodiversity predictions to better inform policy decisions remains a central conservation goal and challenge. A comprehensive strategy to evaluate and reduce uncertainty of model outputs against observed measurements and multiple models would help to produce more robust biodiversity predictions. We propose an approach that integrates biodiversity models and emerging remote sensing and in-situ data streams to evaluate and reduce uncertainty with the goal of improving policy-relevant biodiversity predictions. In this article, we describe a multivariate approach to directly and indirectly evaluate and constrain model uncertainty, demonstrate a proof of concept of this approach, embed the concept within the broader context of model evaluation and scenario analysis for conservation policy, and highlight lessons from other modeling communities.

Estimating global biomass and biogeochemical cycling of marine fish with and without fishing

The biomass and biogeochemical roles of fish in the ocean are ecologically important but poorly known. Here, we use a data-constrained marine ecosystem model to provide a first-order estimate of the historical reduction of fish biomass due to fishing and the associated change in biogeochemical cycling rates. The pre-exploitation global biomass of exploited fish (10 g to 100 kg) was 3.3 ± 0.5 Gt, cycling roughly 2% of global primary production (9.4 ± 1.6 Gt year⁻¹) and producing 10% of surface biological export. Particulate organic matter produced by exploited fish drove roughly 10% of the oxygen consumption and biological carbon storage at depth. By the 1990s, biomass and cycling rates had been reduced by nearly half, suggesting that the biogeochemical impact of fisheries has been comparable to that of anthropogenic climate change. Our results highlight the importance of developing a better mechanistic understanding of how fish alter ocean biogeochemistry.

A unified model of species abundance, genetic diversity, and functional diversity reveals the mechanisms structuring ecological communities

Biodiversity accumulates hierarchically by means of ecological and evolutionary processes and feedbacks. Within ecological communities drift, dispersal, speciation, and selection operate simultaneously to shape patterns of biodiversity. Reconciling the relative importance of these is hindered by current models and inference methods, which tend to focus on a subset of processes and their resulting predictions. Here we introduce massive ecoevolutionary synthesis simulations (MESS), a unified mechanistic model of community assembly, rooted in classic island biogeography theory, which makes temporally explicit joint predictions across three biodiversity data axes: (i) species richness and abundances, (ii) population genetic diversities, and (iii) trait variation in a phylogenetic context. Using simulations we demonstrate that each data axis captures information at different timescales, and that integrating these axes enables discriminating among previously unidentifiable community assembly models. MESS is unique in generating predictions of community‐scale genetic diversity, and in characterizing joint patterns of genetic diversity, abundance, and trait values. MESS unlocks the full potential for investigation of biodiversity processes using multidimensional community data including a genetic component, such as might be produced by contemporary eDNA or metabarcoding studies. We combine MESS with supervised machine learning to fit the parameters of the model to real data and infer processes underlying how biodiversity accumulates, using communities of tropical trees, arthropods, and gastropods as case studies that span a range of data availability scenarios, and spatial and taxonomic scales.

Towards an ecosystem model of infectious disease

Increasingly intimate associations between human society and the natural environment are driving the emergence of novel pathogens, with devastating consequences for humans and animals alike. Prior to emergence, these pathogens exist within complex ecological systems that are characterized by trophic interactions between parasites, their hosts and the environment. Predicting how disturbance to these ecological systems places people and animals at risk from emerging pathogens-and the best ways to manage this-remains a significant challenge. Predictive systems ecology models are powerful tools for the reconstruction of ecosystem function but have yet to be considered for modelling infectious disease. Part of this stems from a mistaken tendency to forget about the role that pathogens play in structuring the abundance and interactions of the free-living species favoured by systems ecologists. Here, we explore how developing and applying these more complete systems ecology models at a landscape scale would greatly enhance our understanding of the reciprocal interactions between parasites, pathogens and the environment, placing zoonoses in an ecological context, while identifying key variables and simplifying assumptions that underly pathogen host switching and animal-to-human spillover risk. As well as transforming our understanding of disease ecology, this would also allow us to better direct resources in preparation for future pandemics.

Phylogeny and evolution of habitat preference in Goniurosaurus (Squamata: Eublepharidae) and their correlation with karst and granite-stream-adapted ecomorphologies in species groups from Vietnam

Abstract

Maximum likelihood (ML) and Bayesian inference (BI) analyses using two mitochondrial (16S and cyt b) and two nuclear (CMOS and RAG1) genes and 103 specimens recovered the first phylogenies of all 23 extant species of Goniurosaurus. The analyses strongly supported the recognition of four monophyletic species groups with identical inter-specific relationships within the kuroiwae, lichtenfelderi, and yingdeensis groups but discordant topologies at some nodes within the luii group. Both analyses recovered a polyphyletic G. luii with respect to G. kadoorieorum, and owing to the lack of diagnostic characters in the latter, it is considered a junior synonym of G. luii. A stochastic character mapping analysis of karst versus non-karst habitat preference suggested that karstic landscapes may have played a major role in the evolution and diversification of Goniurosaurus. A karst habitat preference is marginally supported as the most probable ancestral condition for Goniurosaurus as well as for the kuroiwae, luii, and yingdeensis groups. However, a non-karst habitat preference is marginally supported as the most probable ancestral condition for the lichtenfelderi group. Multivariate and univariate ecomorphological analyses of the karst-adapted G. catbaensis, G. huuliensis, and G. luii of the luii group and the granite-stream-adapted G. lichtenfelderii of the lichtenfelderi group demonstrated that their markedly statistically different body shapes may be an adaptive response that contributes to habitat partitioning in areas of northern Vietnam where they are nearly sympatric.

Rapid Anthropocene realignment of allometric scaling rules

The negative relationship between body size and population density in mammals is often interpreted as resulting from energetic constraints. In a global change scenario, however, this relationship might be expected to change, given the size-dependent nature of anthropogenic pressures and vulnerability to extinction. Here we test whether the size-density relationship (SDR) in mammals has changed over the last 50 years. We show that the relationship has shifted down and became shallower, corresponding to a decline in population density of 31-73%, for the largest and smallest mammals, respectively. However, the SDRs became steeper in some groups (e.g. carnivores) and shallower in others (e.g. herbivores). The Anthropocene reorganisation of biotic systems is apparent in macroecological relationships, reinforcing the notion that biodiversity pattens are contingent upon conditions at the time of investigation. We call for an increased attention to the role of global change on macroecological inferences.

On the robustness of an eastern boundary upwelling ecosystem exposed to multiple stressors

The resistance of an east border upwelling system was investigated using relative index of marine pelagic biomass estimates under a changing environment spanning 20-years in the strongly exploited southern Canary Current Large marine Ecosystem (sCCLME). We divided the sCCLME in two parts (north and south of Cap Blanc), based on oceanographic regimes. We delineated two size-based groups (“plankton” and “pelagic fish”) corresponding to lower and higher trophic levels, respectively. Over the 20-year period, all spatial remote sensing environmental variables increased significantly, except in the area south of Cap Blanc where sea surface Chlorophyll-a concentrations declined and the upwelling favorable wind was stable. Relative index of marine pelagic abundance was higher in the south area compared to the north area of Cap Blanc. No significant latitudinal shift to the mass center was detected, regardless of trophic level. Relative pelagic abundance did not change, suggesting sCCLME pelagic organisms were able to adapt to changing environmental conditions. Despite strong annual variability and the presence of major stressors (overfishing, climate change), the marine pelagic ressources, mainly fish and plankton remained relatively stable over the two decades, advancing our understanding on the resistance of this east border upwelling system.

Evolution of habitat preference in 243 species of Bent-toed geckos (Genus Cyrtodactylus Gray, 1827) with a discussion of karst habitat conservation

Understanding the processes that underpin adaptive evolutionary shifts within major taxonomic groups has long been a research directive among many evolutionary biologists. Such phenomena are best studied in large monophyletic groups that occupy a broad range of habitats where repeated exposure to novel ecological opportunities has happened independently over time in different lineages. The gekkonid genus Cyrtodactylus is just such a lineage with approximately 300 species that range from South Asia to Melanesia and occupy a vast array of habitats. Ancestral state reconstructions using a stochastic character mapping analysis of nine different habitat preferences were employed across a phylogeny composed of 76% of the known species of Cyrtodactylus. This was done in order to ascertain which habitat preference is the ancestral condition and from that condition, the transition frequency to more derived habitat preferences. The results indicate that a general habitat preference is the ancestral condition for Cyrtodactylus and the frequency of transitioning from a general habitat preference to anything more specialized occurs approximately four times more often than the reverse. Species showing extreme morphological and/or ecological specializations generally do not give rise to species bearing other habitat preferences. The evolution of different habitat preferences is generally restricted to clades that tend to occur in specific geographic regions. The largest radiations in the genus occur in rocky habitats (granite and karst), indicating that the transition from a general habitat preference to a granite or karst-dwelling life style may be ecologically uncomplicated. Two large, unrelated clades of karst-associated species are centered in northern Indochina and the largest clade of granite-associated species occurs on the Thai-Malay Peninsula. Smaller, independent radiations of clades bearing other habitat preferences occur throughout the tree and across the broad distribution of the genus. With the exception of a general habitat preference, the data show that karst-associated species far out-number all others (29.6% vs. 0.4%-10.2%, respectively) and the common reference to karstic regions as "imperiled arcs of biodiversity" is not only misleading but potentially dangerous. Karstic regions are not simply refugia harboring the remnants of local biodiversity but are foci of speciation that continue to generate the most speciose, independent, radiations across the genus. Unfortunately, karstic landscapes are some of the most imperiled and least protected habitats on the planet and these data continue to underscore the urgent need for their conservation.

Linking dimensions of data on global marine animal diversity

Recent decades have seen an explosion in the amount of data available on all aspects of biodiversity, which has led to data-driven approaches to understand how and why diversity varies in time and space. Global repositories facilitate access to various classes of species-level data including biogeography, genetics and conservation status, which are in turn required to study different dimensions of diversity. Ensuring that these different data sources are interoperable is a challenge as we aim to create synthetic data products to monitor the state of the world's biodiversity. One way to approach this is to link data of different classes, and to inventory the availability of data across multiple sources. Here, we use a comprehensive list of more than 200 000 marine animal species, and quantify the availability of data on geographical occurrences, genetic sequences, conservation assessments and DNA barcodes across all phyla and broad functional groups. This reveals a very uneven picture: 44% of species are represented by no record other than their taxonomy, but some species are rich in data. Although these data-rich species are concentrated into a few taxonomic and functional groups, especially vertebrates, data are spread widely across marine animals, with members of all 32 phyla represented in at least one database. By highlighting gaps in current knowledge, our census of marine diversity data helps to prioritize future data collection activities, as well as emphasizing the importance of ongoing sustained observations and archiving of existing data into global repositories. This article is part of the theme issue 'Integrative research perspectives on marine conservation'.

Food Webs and Ecosystems: Linking Species Interactions to the Carbon Cycle

All species within ecosystems contribute to regulating carbon cycling because of their functional integration into food webs. Yet carbon modeling and accounting still assumes that only plants, microbes, and invertebrate decomposer species are relevant to the carbon cycle. Our multifaceted review develops a case for considering a wider range of species, especially herbivorous and carnivorous wild animals. Animal control over carbon cycling is shaped by the animals’ stoichiometric needs and functional traits in relation to the stoichiometry and functional traits of their resources. Quantitative synthesis reveals that failing to consider these mechanisms can lead to serious inaccuracies in the carbon budget. Newer carbon-cycle models that consider food-web structure based on organismal functional traits and stoichiometry can offer mechanistically informed predictions about the magnitudes of animal effects that will help guide new empirical research aimed at developing a coherent understanding of the interactions and importance of all species within food webs.

Climate drives the geography of marine consumption by changing predator communities

The global distribution of primary production and consumption by humans (fisheries) is well-documented, but we have no map linking the central ecological process of consumption within food webs to temperature and other ecological drivers. Using standardized assays that span 105° of latitude on four continents, we show that rates of bait consumption by generalist predators in shallow marine ecosystems are tightly linked to both temperature and the composition of consumer assemblages. Unexpectedly, rates of consumption peaked at midlatitudes (25 to 35°) in both Northern and Southern Hemispheres across both seagrass and unvegetated sediment habitats. This pattern contrasts with terrestrial systems, where biotic interactions reportedly weaken away from the equator, but it parallels an emerging pattern of a subtropical peak in marine biodiversity. The higher consumption at midlatitudes was closely related to the type of consumers present, which explained rates of consumption better than consumer density, biomass, species diversity, or habitat. Indeed, the apparent effect of temperature on consumption was mostly driven by temperature-associated turnover in consumer community composition. Our findings reinforce the key influence of climate warming on altered species composition and highlight its implications for the functioning of Earth's ecosystems.

Ophidiomycosis, an emerging fungal disease of snakes: Targeted surveillance on military lands and detection in the western US and Puerto Rico

Wildlife disease surveillance and pathogen detection are fundamental for conservation, population sustainability, and public health. Detection of pathogens in snakes is often overlooked despite their essential roles as both predators and prey within their communities. Ophidiomycosis (formerly referred to as Snake Fungal Disease, SFD), an emergent disease on the North American landscape caused by the fungus Ophidiomyces ophiodiicola, poses a threat to snake population health and stability. We tested 657 individual snakes representing 58 species in 31 states from 56 military bases in the continental US and Puerto Rico for O. ophiodiicola. Ophidiomyces ophiodiicola DNA was detected in samples from 113 snakes for a prevalence of 17.2% (95% CI: 14.4–20.3%), representing 25 species from 19 states/territories, including the first reports of the pathogen in snakes in Idaho, Oklahoma, and Puerto Rico. Most animals were ophidiomycosis negative (n = 462), with Ophidiomyces detected by qPCR (n = 64), possible ophidiomycosis (n = 82), and apparent ophidiomycosis (n = 49) occurring less frequently. Adults had 2.38 times greater odds than juveniles of being diagnosed with ophidiomycosis. Snakes from Georgia, Massachusetts, Pennsylvania, and Virginia all had greater odds of ophidiomycosis diagnosis, while snakes from Idaho were less likely to be diagnosed with ophidiomycosis. The results of this survey indicate that this pathogen is endemic in the eastern US and identified new sites that could represent emergence or improved detection of endemic sites. The direct mortality of snakes with ophidiomycosis is unknown from this study, but the presence of numerous individuals with clinical disease warrants further investigation and possible conservation action.

A guide to ecosystem models and their environmental applications

Applied ecology has traditionally approached management problems through a simplified, single-species lens. Repeated failures of single-species management have led us to a new paradigm - managing at the ecosystem level. Ecosystem management involves a complex array of interacting organisms, processes and scientific disciplines. Accounting for interactions, feedback loops and dependencies between ecosystem components is therefore fundamental to understanding and managing ecosystems. We provide an overview of the main types of ecosystem models and their uses, and discuss challenges related to modelling complex ecological systems. Existing modelling approaches typically attempt to do one or more of the following: describe and disentangle ecosystem components and interactions; make predictions about future ecosystem states; and inform decision making by comparing alternative strategies and identifying important uncertainties. Modelling ecosystems is challenging, particularly when balancing the desire to represent many components of an ecosystem with the limitations of available data and the modelling objective. Explicitly considering different forms of uncertainty is therefore a primary concern. We provide some recommended strategies (such as ensemble ecosystem models and multi-model approaches) to aid the explicit consideration of uncertainty while also meeting the challenges of modelling ecosystems.

Non-linear changes in modelled terrestrial ecosystems subjected to perturbations

Perturbed ecosystems may undergo rapid and non-linear changes, resulting in 'regime shifts' to an entirely different ecological state. The need to understand the extent, nature, magnitude and reversibility of these changes is urgent given the profound effects that humans are having on the natural world. General ecosystem models, which simulate the dynamics of ecosystems based on a mechanistic representation of ecological processes, provide one novel way to project ecosystem changes across all scales and trophic levels, and to forecast impact thresholds beyond which irreversible changes may occur. We model ecosystem changes in four terrestrial biomes subjected to human removal of plant biomass, such as occurs through agricultural land-use change. We find that irreversible, non-linear responses commonly occur where removal of vegetation exceeds 80% (a level that occurs across nearly 10% of the Earth's land surface), especially for organisms at higher trophic levels and in less productive ecosystems. Very large, irreversible changes to ecosystem structure are expected at levels of vegetation removal akin to those in the most intensively used real-world ecosystems. Our results suggest that the projected twenty-first century rapid increases in agricultural land conversion may lead to widespread trophic cascades and in some cases irreversible changes to ecosystem structure.

Structural forecasting of species persistence under changing environments

The persistence of a species in a given place not only depends on its intrinsic capacity to consume and transform resources into offspring, but also on how changing environmental conditions affect its growth rate. However, the complexity of factors has typically taken us to choose between understanding and predicting the persistence of species. To tackle this limitation, we propose a probabilistic approach rooted on the statistical concepts of ensemble theory applied to statistical mechanics and on the mathematical concepts of structural stability applied to population dynamics models - what we call structural forecasting. We show how this new approach allows us to estimate a probability of persistence for single species in local communities; to understand and interpret this probability conditional on the information we have concerning a system; and to provide out-of-sample predictions of species persistence as good as the best experimental approaches without the need of extensive amounts of data.

Knight A, Leroi AM, Rosindell J. Relationship between conservation biology and ecology shown through machine reading of 32,000 articles

Conservation biology was founded on the idea that efforts to save nature depend on a scientific understanding of how it works. It sought to apply ecological principles to conservation problems. We investigated whether the relationship between these fields has changed over time through machine reading the full texts of 32,000 research articles published in 16 ecology and conservation biology journals. We examined changes in research topics in both fields and how the fields have evolved from 2000 to 2014. As conservation biology matured, its focus shifted from ecology to social and political aspects of conservation. The 2 fields diverged and now occupy distinct niches in modern science. We hypothesize this pattern resulted from increasing recognition that social, economic, and political factors are critical for successful conservation and possibly from rising skepticism about the relevance of contemporary ecological theory to practical conservation.

The megabiota are disproportionately important for biosphere functioning

A prominent signal of the Anthropocene is the extinction and population reduction of the megabiota—the largest animals and plants on the planet. However, we lack a predictive framework for the sensitivity of megabiota during times of rapid global change and how they impact the functioning of ecosystems and the biosphere. Here, we extend metabolic scaling theory and use global simulation models to demonstrate that (i) megabiota are more prone to extinction due to human land use, hunting, and climate change; (ii) loss of megabiota has a negative impact on ecosystem metabolism and functioning; and (iii) their reduction has and will continue to significantly decrease biosphere functioning. Global simulations show that continued loss of large animals alone could lead to a 44%, 18% and 92% reduction in terrestrial heterotrophic biomass, metabolism, and fertility respectively. Our findings suggest that policies that emphasize the promotion of large trees and animals will have disproportionate impact on biodiversity, ecosystem processes, and climate mitigation.

Human-driven losses of megafauna and megaflora may have disproportionate ecological consequences. Here, the authors combine metabolic scaling theory and global simulation models to show that past and continued reduction of megabiota have and will continue to decrease ecosystem and biosphere functioning.

Scaling-up biodiversity-ecosystem functioning research

A rich body of knowledge links biodiversity to ecosystem functioning (BEF), but it is primarily focused on small scales. We review the current theory and identify six expectations for scale dependence in the BEF relationship: (1) a nonlinear change in the slope of the BEF relationship with spatial scale; (2) a scale‐dependent relationship between ecosystem stability and spatial extent; (3) coexistence within and among sites will result in a positive BEF relationship at larger scales; (4) temporal autocorrelation in environmental variability affects species turnover and thus the change in BEF slope with scale; (5) connectivity in metacommunities generates nonlinear BEF and stability relationships by affecting population  synchrony at local and regional scales; (6) spatial scaling in food web structure and diversity will generate scale dependence in ecosystem functioning. We suggest directions for synthesis that combine approaches in metaecosystem and metacommunity ecology and integrate cross‐scale feedbacks. Tests of this theory may combine remote sensing with a generation of networked experiments that assess effects at multiple scales. We also show how anthropogenic land cover change may alter the scaling of the BEF relationship. New research on the role of scale in BEF will guide policy linking the goals of managing biodiversity and ecosystems.

Modelling regional futures at decadal scale: application to the Kimberley region

We address the question of how to provide meaningful scientific information to support environmental decision making at the regional scale and at the temporal scale of several decades in a network of marine parks in the Kimberley region of Western Australia. Where environmental sustainability is affected by slow-dynamics climate change processes and one-off investments in large infrastructure which can affect a region for decades to come, both strategic and reactive planning is necessary and prediction becomes as urgent as standard adaptive management. At the interface between future studies, socio-economic modelling and environmental modelling, we define 18 scenarios of economic development and climate change impacts and five management strategies. We explore these potential futures using coupled models of terrestrial and marine ecosystem dynamics. We obtain a projection of the Kimberley marine system to the year 2050, conditional on the chosen scenarios and management strategies. Our results suggest that climate change, not economic development, is the largest factor affecting the future of marine ecosystems in the Kimberley region, with site-attached species such as reef fish at greatest risk. These same species also benefit most from more stringent management strategies, especially expansion of sanctuary zones and Marine Protected Areas.

Ant-termite interactions: an important but under-explored ecological linkage

Animal interactions play an important role in understanding ecological processes. The nature and intensity of these interactions can shape the impacts of organisms on their environment. Because ants and termites, with their high biomass and range of ecological functions, have considerable effects on their environment, the interaction between them is important for ecosystem processes. Although the manner in which ants and termites interact is becoming increasingly well studied, there has been no synthesis to date of the available literature. Here we review and synthesise all existing literature on ant-termite interactions. We infer that ant predation on termites is the most important, most widespread, and most studied type of interaction. Predatory ant species can regulate termite populations and subsequently slow down the decomposition of wood, litter and soil organic matter. As a consequence they also affect plant growth and distribution, nutrient cycling and nutrient availability. Although some ant species are specialised termite predators, there is probably a high level of opportunistic predation by generalist ant species, and hence their impact on ecosystem processes that termites are known to provide varies at the species level. The most fruitful future research direction will be to evaluate the impact of ant-termite predation on broader ecosystem processes. To do this it will be necessary to quantify the efficacy both of particular ant species and of ant communities as a whole in regulating termite populations in different biomes. We envisage that this work will require a combination of methods, including DNA barcoding of ant gut contents along with field observations and exclusion experiments. Such a combined approach is necessary for assessing how this interaction influences entire ecosystems.

On the Ethics of Biodiversity Models, Forecasts and Scenarios

The development of numerical models to produce realistic prospective scenarios for the evolution of biological diversity is essential. Only integrative impact assessment models are able to take into account the diverse and complex interactions embedded in social-ecological systems. The knowledge used is objective, the procedure of their integration is rigorous and the data massive. Nevertheless, the technical choices (model ontology, treatment of scales and uncertainty, data choice and pre-processing, technique of representation, etc.) made at each stage of the development of models and scenarios are mostly circumstantial, depending on both the skills of modellers on a project and the means available to them. In the end, the scenarios selected and the way they are simulated limit the futures explored, and the options offered to decision makers and stakeholders to act. The ethical implications of these circumstantial choices are generally not documented, explained or even perceived by modellers. Applied ethics propose a coherent set of principles to guide a critical reflection on the social and environmental consequences of integrative modelling and simulation of biodiversity scenarios. Such reflection should be incorporated into the actual modelling process, in a broad participatory framework, and foster effective moral involvement of modellers, policy-makers and stakeholders, in preference to the application of fixed ethical rules.

Modelling landscape constraints on farmland bird species range shifts under climate change

Several studies estimating the effects of global environmental change on biodiversity are focused on climate change. Yet, non-climatic factors such as changes in land cover can also be of paramount importance. This may be particularly important for habitat specialists associated with human-dominated landscapes, where land cover and climate changes may be largely decoupled. Here, we tested this idea by modelling the influence of climate, landscape composition and pattern, on the predicted future (2021-2050) distributions of 21 farmland bird species in the Iberian Peninsula, using boosted regression trees and 10-km resolution presence/absence data. We also evaluated whether habitat specialist species were more affected by landscape factors than generalist species. Overall, this study showed that the contribution of current landscape composition and pattern to the performance of species distribution models (SDMs) was relatively low. However, SDMs built using either climate or climate plus landscape variables yielded very different predictions of future species range shifts and, hence, of the geographical patterns of change in species richness. Our results indicate that open habitat specialist species tend to expand their range, whereas habitat generalist species tend to retract under climate change scenarios. The effect of incorporating landscape factors were particularly marked on open habitat specialists of conservation concern, for which the expected expansion under climate change seems to be severely constrained by land cover change. Overall, results suggest that particular attention should be given to landscape change in addition to climate when modelling the impacts of environmental changes for both farmland specialist and generalist bird distributions.

Considering land-sea interactions and trade-offs for food and biodiversity

With the human population expected to near 10 billion by 2050, and diets shifting towards greater per-capita consumption of animal protein, meeting future food demands will place ever-growing burdens on natural resources and those dependent on them. Solutions proposed to increase the sustainability of agriculture, aquaculture, and capture fisheries have typically approached development from single sector perspectives. Recent work highlights the importance of recognising links among food sectors, and the challenge cross-sector dependencies create for sustainable food production. Yet without understanding the full suite of interactions between food systems on land and sea, development in one sector may result in unanticipated trade-offs in another. We review the interactions between terrestrial and aquatic food systems. We show that most of the studied land-sea interactions fall into at least one of four categories: ecosystem connectivity, feed interdependencies, livelihood interactions, and climate feedback. Critically, these interactions modify nutrient flows, and the partitioning of natural resource use between land and sea, amid a backdrop of climate variability and change that reaches across all sectors. Addressing counter-productive trade-offs resulting from land-sea links will require simultaneous improvements in food production and consumption efficiency, while creating more sustainable feed products for fish and livestock. Food security research and policy also needs to better integrate aquatic and terrestrial production to anticipate how cross-sector interactions could transmit change across ecosystem and governance boundaries into the future.

Spatial landscape model to characterize biological diversity using R statistical computing environment

Due to urbanization and population growth, the degradation of natural forests and associated biodiversity are now widely recognized as a global environmental concern. Hence, there is an urgent need for rapid assessment and monitoring of biodiversity on priority using state-of-art tools and technologies. The main purpose of this research article is to develop and implement a new methodological approach to characterize biological diversity using spatial model developed during the study viz. Spatial Biodiversity Model (SBM). The developed model is scale, resolution and location independent solution for spatial biodiversity richness modelling. The platform-independent computation model is based on parallel computation. The biodiversity model based on open-source software has been implemented on R statistical computing platform. It provides information on high disturbance and high biological richness areas through different landscape indices and site specific information (e.g. forest fragmentation (FR), disturbance index (DI) etc.). The model has been developed based on the case study of Indian landscape; however it can be implemented in any part of the world. As a case study, SBM has been tested for Uttarakhand state in India. Inputs for landscape ecology are derived through multi-criteria decision making (MCDM) techniques in an interactive command line environment. MCDM with sensitivity analysis in spatial domain has been carried out to illustrate the model stability and robustness. Furthermore, spatial regression analysis has been made for the validation of the output.