SEARCH · bioRxiv Science

Results for “Ecology”

Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,315 records · Page 73Linked to original sources

bioRxiv2019

Landscape-scale simulations as a tool in multi-criteria decision making for agri-environment schemes

Increasing concerns over the environmental impacts of agriculture in Europe has led to the introduction of agri-environment schemes (AES) to help mitigate biodiversity loss. However, effectiveness of AES has been mixed and only partially successful in achieving desired outcomes. To improve effectiveness and reduce high costs, multi-criteria decision analysis (MCDA) can help support decision-making and determine the most effective management action. Although MCDA has great potential for evaluating policy measures, it rarely considers the context-dependency of species responses to management practices across different landscapes. Landscape simulations can, therefore, be valuable for reducing the uncertainties when predicting the consequences of management actions. A potential suitable simulation system is the Animal, Landscape, and Man Simulation System (ALMaSS), a mechanistic simulation with can improve MCDA with the automatic integration of a species ecology and behaviour and landscape context. The aim of this study was to demonstrate the effectiveness of ALMaSS in evaluating AES management practices across different landscapes and estimate their ability to achieve the proposed conservation outcomes of three typical species of conservation interest. In this study, the effect of a particular management strategy on a species was dependent on the landscape context, in our case, a combination of landscape structure and the type and distribution of farms, and varied depending on the metrics being measured. Although we did not aim to make recommendations of particular management strategies, we demonstrate how simulations can be used for MCDA to select between management strategies with different costs. Despite the complexity of ALMaSS models, the simulation results provided are easy to interpret. Landscape simulations, such as ALMaSS, can be an important tool in multi-criteria decision making by simulating a wide range of managements and contexts and provide supporting information for filtering management options based on specific conservation goals.

ecology↗

bioRxiv2019

Metabolic rates of prokaryotic microbes may inevitably rise with global warming

Understanding how the metabolic rates of prokaryotes respond to temperature is fun-damental to our understanding of how ecosystem functioning will be altered by climate change, as these micro-organisms are major contributors to global carbon efflux. Ecological metabolic theory suggests that species living at higher temperatures evolve higher growth rates than those in cooler niches due to thermodynamic constraints. Here, using a global prokaryotic dataset, we find that maximal growth rate at thermal optimum increases with temperature for mesophiles (temperature optima [lsim] 45{degrees}C), but not thermophiles ({gtrsim} 45{degrees}C). Furthermore, short-term (within-day) thermal responses of prokaryotic metabolic rates are typically more sensitive to warming than those of eukaryotes. Given that climatic warming will mostly impact ecosystems in the mesophilic temperature range, we conclude that as microbial communities adapt to higher temperatures, their metabolic rates and therefore, carbon efflux, will inevitably rise. Using a mathematical model, we illustrate the potential global impacts of these findings.

ecology↗

bioRxiv2019

Risks to pollinators from different land-use transitions: bee species’ responses to agricultural expansion show strong phylogenetic signal

Bee species worldwide are facing a future of further land-use change and intensification. Populations of closely-related species with similar ecological characteristics are likely to respond similarly to such pressures. Such phylogenetic signal in species responses could undermine the stability of pollination services in agricultural and natural systems. We use abundance data from a global compilation of bee assemblages in different land uses to assess the sensitivity of 573 bee species to agricultural expansion, intensification and urbanization; and combine the results with the Bee Tree of Life to assess phylogenetic signal. In addition, we assess whether variation in species sensitivity to land-use change is better explained by phylogenetic or available functional trait differences. Bee species show strong phylogenetic signal in sensitivity to agricultural land expansion but only a weak signal in sensitivity to agricultural intensification and urbanisation. Sensitivities were usually best explained by a combination of functional and phylogenetic distances. This finding suggests that the commonly-recorded traits, despite being meaningful as functional response traits, do not capture all important determinants of bee species vulnerability or resistance. However, it also suggests that model-based predictions of the sensitivity of poorly known species may be sufficient to help guide conservation efforts.

ecology↗

bioRxiv2019

Maximum entropy models elucidate the contribution of metabolic traits to patterns of community assembly

Aim: Maximum entropy (MaxEnt) models promise a novel approach for understanding community assembly and species abundance patterns. One of these models, the \"Maximum Entropy Theory of Ecology\" (METE) reproduces many observed species abundance patterns, but is based on an aggregated representation of community structure that does not resolve species identity or explicitly represent species-specific functional traits. In this paper, METE is compared to \"Very Entropic Growth\" (VEG), a MaxEnt model with a less aggregated representation of community structure that represents species (more correctly, functional types) in terms of their per capita metabolic rates. We examine the contribution of metabolic traits to the patterns of community assembly predicted by VEG and, through aggregation, compare the results with METE predictions in order to gain insight into the biological factors underlying observed patterns of community assembly. Innovation: We formally compare two MaxEnt-based community models, METE and VEG, that differ as to whether or not they represent species-specific functional traits. We empirically test and compare the metabolic predictions of both models, thereby elucidating the role of metabolic traits in patterns of community assembly. Main Conclusions: Our analysis reveals that a key determinant of community metabolic patterns is the \"density of species\" distribution, defined as the intrinsic number of species with metabolic rates in a given range that are available to a community prior to filtering by environmental constraints. Our analysis suggests that appropriate choice of of the density of species in VEG may lead to more realistic predictions than METE, for which this distribution is not defined, and thus opens up new ways to understanding the link between functional traits and patterns of community assembly.

ecology↗

bioRxiv2019

Transmission bias's fundamental role in biodiversity change

There is a great need to understand how and why biodiversity, which we define as the variety of organisms found in a given place, changes over time. Current estimates suggest strikingly slow change in traditional measures of biodiversity. These estimates seem to contradict rapid shifts in the abundance of individual species and have led to a rethinking of the mechanisms shaping biodiversity. Conceptual models emphasize the role of competition among species or, more recently, selection on species identity (i.e. selection that favors some species at the expense of others). However, it is difficult to quantify how these mechanisms contribute to biodiversity change. To illustrate this point we present cases where strong competition or selection on species identity leads to no biodiversity change. In view of this disconnect we develop a new approach to studying biodiversity change using the Price equation. We show that biodiversity change responds to selection on species rarity, rather than to either competition or selection on species identity. We then show how this insight can be used to quantify the effects of the mechanisms previously thought to influence biodiversity: 1) selection, 2) (ecological) drift, 3) immigration and 4) speciation. Our results suggest the connection between species fates and their rarity is fundamental to understanding biodiversity change.

ecology↗

bioRxiv2019

Virome heterogeneity and connectivity in waterfowl and shorebird communities

Models of host-microbe dynamics typically assume a single-host population infected by a single pathogen. In reality, many hosts form multi-species aggregations and may be infected with an assemblage of pathogens. We used a meta-transcriptomic approach to characterize the viromes of nine avian species in the Anseriformes (ducks) and Charadriiformes (shorebirds). This revealed the presence of 27 viral species, of which 24 were novel, including double-stranded RNA viruses (Picobirnaviridae and Reoviridae), single-stranded RNA viruses (Astroviridae, Caliciviridae, Picornaviridae), a retro-transcribing DNA virus (Hepadnaviridae), and a single-stranded DNA virus (Parvoviridae). These viruses comprise multi-host generalist viruses and those that are host-specific, indicative of both virome connectivity and heterogeneity. Virome connectivity was apparent in two well described multi-host virus species (avian coronavirus and influenza A virus) and a novel Rotavirus species that were shared among some Anseriform species, while heterogeneity was reflected in the absence of viruses shared between Anseriformes and Charadriiformes. Notably, within avian host families there was no significant relationship between either host taxonomy or foraging ecology and virome composition, although Anseriform species positive for influenza A virus harboured more additional viruses than those negative for influenza virus. Overall, we demonstrate complex virome structures across host species that co-exist in multi-species aggregations.

ecology↗

bioRxiv2019

Scale dependent patterns in interaction diversity maintain resiliency in a frequently disturbed ecosystem

Frequently disturbed ecosystems are characterized by resilience to ecological disturbances. For example, longleaf pine ecosystems are exposed to frequent fire disturbance, and this feature sustains biodiversity. We examined how fire frequency maintains beta diversity of multi-trophic interactions, as this community parameter provides a measure of functional redundancy of an ecosystem. We found that turnover in interaction diversity at small local scales is highest in the most frequently burned stands, conferring immediate resiliency to disturbance by fire. Interactions become more specialized and less resilient as fire frequency decreases. Local scale patterns of interaction diversity contribute to broader scale patterns and confer long-term ecosystem resiliency. Such natural disturbances are likely to be important for maintaining regional diversity of interactions for a broad range of ecosystems.

ecology↗

bioRxiv2019

Behavior influences range limits and patterns of coexistence across an elevational gradient in tropical bird diversity

Does competition influence patterns of coexistence between closely related taxa? Here we address this basic question in ecology by analyzing patterns of range overlap between related bird species ("sister pairs") distributed along a Neotropical elevational gradient. We explicitly contrast the behavioral dimension of interspecific competition (interference competition) with similarity in resource acquisition traits (exploitative competition). We find that behavioral interactions are generally important in setting elevational range limits and preventing coexistence of closely related species. Specifically, close relatives that defend year-round territories tend to live in non-overlapping elevational distributions, while close relatives that do not defend territories tend to broadly overlap in distribution. In contrast, neither similarity in beak morphology nor evolutionary relatedness was associated with patterns of range limitation. Our main result is that interference competition can be an important driver of species ranges at the scale of entire diverse assemblages. Consequently, we suggest that behavioral dimensions of the niche should be more broadly incorporated in macroecological studies.

ecology↗

bioRxiv2019

Effects of variations in adaptation potential on invasion speeds and species ranges

Confronted with global changes and their potential impacts on biodiversity, an important question is to understand the ecological and evolutionary determinants of species geographical distributions. In order to understand how adaptation in heterogeneous environments constrains such distributions, we analyze how the potential of adaptation along an environmental cline affects the geographical distribution and propagation dynamics (invasion or extinction) of a single species. We re-analyse a model initially proposed by Kirkpatrick and Barton using propagation speed to assess whether species distribution is spatially limited or not. We found that for big adaptation potentials, the species invades space following Fishers model, whereas for small adaptation potentials the propagation depends on the evolutionary challenge to overcome. We have explicit approximations for the propagation speeds in both cases. We discuss the utility of these propagation speeds as an eco-evolutionary index based on empirical studies.

ecology↗

bioRxiv2019

Extinctions in marine plankton preceded by stabilizing selection

Unless they adapt, populations facing persistent stress are threatened by extinction. Theoretically, populations facing stress can react by either disruption, increasing trait variation, or stabilisation, decreasing trait variation. In the short term, the more economical response is stabilisation, because it quickly transfers a large part of the population closer to a new ecological optimum. However, canalisation is deleterious in the face of persistently increasing stress because it reduces variability and thus decreases the ability to react to further change in stress. Understanding how natural populations react to intensifying stress reaching terminal levels is key to assessing their resilience to environmental change such as that caused by global warming. Because extinctions are hard to predict, observational data on the adaptive reaction of populations facing extinction are rare. In this study, we make use of the glacial salinity rise in the Red Sea as a natural experiment allowing us to analyse the reaction of planktonic Foraminifera to stress escalation in the geological past. We analyse morphological trait state and variance in two species across a salinity rise leading to their local extinction. One species reacted by stabilisation in shape and size, detectable several thousand years prior to extinction. The second species reacted by trait divergence, but each of the two divergent populations remains stable or reacted by further stabilisation. These observations indicate that the default reaction of the studied Foraminifera is stabilisation and that stress escalation did not lead to the local emergence of adapted forms. Inability to breach the global adaptive threshold would explain why communities of Foraminifera, and many other groups of marine plankton, reacted to Quaternary climate change by faithfully tracking their zonally shifting environments. It also means that populations of marine species adapted to response by migration, when exposed to stress outside of the adaptive range, will be at risk of extinction.

ecology↗

bioRxiv2019

Environmental selection and spatiotemporal structure of a major group of soil protists (Rhizaria: Cercozoa) in a temperate grassland

Soil protists are increasingly appreciated as essential components of soil foodwebs; however, there is a dearth of information on the factors structuring their communities. Here we investigate the importance of different biotic and abiotic factors as key drivers of spatial and seasonal distribution of protistan communities. We conducted an intensive survey of a 10m2 grassland plot in Germany, focusing on a major group of protists, the Cercozoa. From 177 soil samples, collected from April to November, we obtained 694 Operational Taxonomy Units representing >6 million Illumina reads. All major cercozoan groups were present, dominated by the small flagellates of the Glissomonadida. We found evidence of environmental filtering structuring the cercozoan communities both spatially and seasonally. Spatial analyses indicated that communities were correlated within a range of four meters. Seasonal variations of bactevirores and bacteria, and that of omnivores after a time-lapse, suggested a dynamic prey-predator succession. The most influential edaphic properties were moisture and clay content, which differentially affected each functional group. Our study is based on an intense sampling of protists at a small scale, thus providing a detailed description of the niches occupied by different taxa/functional groups and the ecological processes involved.

ecology↗

bioRxiv2019

Individual tree-crown detection in RGB imagery using self-supervised deep learning neural networks

Remote sensing can transform the speed, scale, and cost of biodiversity and forestry surveys. Data acquisition currently outpaces the ability to identify individual organisms in high resolution imagery. We outline an approach for identifying tree-crowns in RGB imagery while using a semi-supervised deep learning detection network. Individual crown delineation has been a long-standing challenge in remote sensing and available algorithms produce mixed results. We show that deep learning models can leverage existing Light Detection and Ranging (LIDAR)-based unsupervised delineation to generate trees that are used for training an initial RGB crown detection model. Despite limitations in the original unsupervised detection approach, this noisy training data may contain information from which the neural network can learn initial tree features. We then refine the initial model using a small number of higher-quality hand-annotated RGB images. We validate our proposed approach while using an open-canopy site in the National Ecological Observation Network. Our results show that a model using 434,551 self-generated trees with the addition of 2848 hand-annotated trees yields accurate predictions in natural landscapes. Using an intersection-over-union threshold of 0.5, the full model had an average tree crown recall of 0.69, with a precision of 0.61 for the visually-annotated data. The model had an average tree detection rate of 0.82 for the field collected stems. The addition of a small number of hand-annotated trees improved the performance over the initial self-supervised model. This semi-supervised deep learning approach demonstrates that remote sensing can overcome a lack of labeled training data by generating noisy data for initial training using unsupervised methods and retraining the resulting models with high quality labeled data.

ecology↗

bioRxiv2019

Variation in space and time: a long-term examination of density-dependent dispersal in a woodland rodent

Dispersal is a fundamental ecological process that can be affected by population density, yet studies report contrasting effects of density on propensity to disperse. Additionally, the relationship between dispersal and density is seldom examined using densities measured at different spatial scales or over extensive time-series. We used 51-years of trapping data to examine how dispersal by wild deer mice (Peromyscus maniculatus) was affected by changes in both local and regional population densities. We examined these patterns over both the entire time-series and also in ten-year shifting windows to determine whether the nature and strength of the relationship changed through time. Probability of dispersal decreased with increased local and regional population density, and the negative effect of local density on dispersal was more pronounced in years with low regional densities. Additionally, the strength of negative density-dependent dispersal changed through time, ranging from very strong in some decades to absent in other periods of the study. Finally, while females were less likely to disperse, female dispersal was more density-dependent than male dispersal. Our study shows that the relationship between density and dispersal is not temporally static and that investigations of density-dependent dispersal should consider both local and regional population densities.

ecology↗

bioRxiv2019

Chemosymbiotic bivalves contribute to the nitrogen budget of seagrass ecosystems

In many seagrass sediments, lucinid bivalves and their sulfur-oxidizing symbionts are thought to underpin key ecosystem functions, but little is known about their role in nutrient cycles, particularly nitrogen. We used natural stable isotopes, elemental analyses, and stable isotope probing to study the ecological stoichiometry of a lucinid symbiosis in spring and fall. Chemoautotrophy appeared to dominate in fall, when chemoautotrophic carbon fixation rates were up to one order of magnitude higher as compared to the spring, suggesting a flexible nutritional mutualism. In fall, an isotope pool dilution experiment revealed carbon limitation of the symbiosis and ammonium excretion rates up to 10-fold higher compared to fluxes reported for non-symbiotic marine bivalves. These results provide evidence that lucinid bivalves can contribute substantial amounts of ammonium to the ecosystem. Given the preference of seagrasses for this nitrogen source, lucinid bivalves contribution may boost productivity of these important blue carbon ecosystems.

ecology↗

bioRxiv2019

A practive faeces collection protocol for multidisciplinary research in wildlife science

Faecal samples have become important non-invasive source of information in wildlife biology and ecological research. Despite regular use of faeces, there is no universal protocol available for faeces collection and storage to answer various questions in wildlife biology. We collected 1408 faeces from ten different species using a dry sampling approach, and achieved 94.87% and 86.02% success rate in mitochondrial and nuclear marker amplifications. We also suggest a universal framework to use the same samples for different use. This protocol provides an easy, quick and cheap option to collect non-invasive samples from species living at different environmental conditions to answer multidisciplinary questions in wildlife biology.

ecology↗

bioRxiv2019

Optimal sample size calculation for null hypothesis significance tests

Traditional study design tools for estimating appropriate sample sizes are not consistently used in ecology and can lead to low statistical power to detect biologically relevant effects. We have developed a new approach to estimating optimal sample sizes, requiring only three parameters; a maximum acceptable average of and {beta}, a critical effect size of minimum biological relevance, and an estimate of the relative costs of Type I vs. Type II errors.This approach can be used to show the general circumstances under which different combinations of critical effect sizes and maximum acceptable combinations of and {beta} are attainable for different statistical tests. The optimal sample size estimation approach can require fewer samples than traditional sample size estimation methods when costs of Type I and II errors are assumed to be equal but recommends comparatively more samples for increasingly unequal Type I vs. Type II errors costs. When sampling costs and absolute costs of Type I and II errors are known, optimal sample size estimation can be used to determine the smallest sample size at which the cost of an additional sample outweighs its associated reduction in errors. Optimal sample size estimation constitutes a more flexible and intuitive tool than traditional sample size estimation approaches, given the constraints and unknowns commonly faced by ecologists during study.

ecology↗

bioRxiv2019

Neutral processes and high inter-annual turnover shape the assembly of soil bacterial communities in a Mediterranean watershed.

There is a renewed interest in recent years on the ecological processes (stochastic vs selective) driving the assembly of microbial communities. Such information could potentially improve our understanding on ecosystem functioning and resilience to disturbances, ecosystem response to environmental shifts, and adoption of sustainable soil management practices. Herein, employing a suite of existing methodologies, we show that stochastic processes have an important role on the assembly of soil bacterial communities at a Mediterranean watershed. Moreover, we document that the relative contribution of assembly processes varies over the years. The observed intensification of stochastic processes was accompanied by a decrease in the contribution of variable selection in favor of homogeneous selection and dispersal and this trend was only marginally affected by land use (natural vs agricultural lands) or soil depth. Our study also revealed a high inter-annual turnover of soil microbial communities that was likely stimulated by the weak environmental selection and the prevailing environmental conditions (drying-wetting cycles) in Mediterranean landscapes, implying potential impacts on ecosystem functioning and our ability to predict soil response to environmental shifts. Using nitrogen mineralization rate (NMR) as a representative function we document highly variable NMR over the sampling years, land uses and soil depths and lack of significant associations with the monitored environmental variables and individual taxa. In summary, our study provides novel insights on the organization and functioning of microbial communities at Mediterranean ecosystems and sets directions towards a more advanced understanding of the relationships among environmental factors, microbial community structure, and ecosystem functioning that could contribute to sustainable management of these severely degraded ecosystems.

ecology↗

bioRxiv2019

Using extreme gradient boosting (XGBoost) to evaluate the importance of a suite of environmental variables and to predict recruitment of young-of-the-year spotted seatrout in Florida

Environmental factors strongly influence the success of juvenile fish recruitment and productivity, but species-specific environment-recruitment relationships have eluded researchers for decades. Most likely, this is because the environment-recruitment relationship is nonlinear, there are multi-level interactions between factors, and environmental variability may differentially affect recruitment among populations due to spatial heterogeneity. Identifying the most influential environmental variables may result in more accurate predictions of future recruitment and productivity of managed species. Here, gradient tree boosting was implemented using XGBoost to identify the most important predictors of recruitment for six estuary populations of spotted seatrout (Cynoscion nebulosus), an economically valuable marine resource in Florida. XGBoost, a machine learning method for regression and classification, was employed because it inherently models variable interactions and seamlessly deals with multi-collinearity, both of which are common features of ecological datasets. Additionally, XGBoost operates at a speed faster than many other gradient boosting algorithms due to a regularization factor and parallel computing functionality. In this application of XGBoost, the results indicate that the abundance of pre-recruit, juvenile spotted seatrout in spatially distinct estuaries is influenced by nearly the same set of environmental predictors. But perhaps of greater importance is that the results of this study show that this algorithm is highly effective at predicting species abundance and identifying important environmental factors (i.e. predictors of recruitment). It is strongly encouraged that future research explore the applicability of the XGBoost algorithm to other topics in marine and fisheries science and compare its performance to that of other statistical methods.

ecology↗