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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↗

bioRxiv2019

Temporal switching of species roles in a plant-pollinator network

Mutualistic networks are highly dynamic, characterized by high temporal turnover of species and interactions. Yet, we have a limited understanding of how the internal structure of these networks and the roles species play in them vary through time. We used six years of observation data and a novel statistical method (dynamic stochastic block models) to assess how network structure and species roles change across time in a quantitative plant-pollinator network from a dryland ecosystem in Argentina. Our analyses revealed a core-periphery structure persistent through seasons and years. Yet, species roles as core or peripheral were highly dynamic: virtually all species that played a core role in some seasons were also peripheral in other seasons, while many other species remained always peripheral. Our results illuminate our understanding of the dynamics of ecological networks and have important implications for ecosystem management and conservation.

ecology↗

bioRxiv2019

Assessing different components of biodiversity across a river network using eDNA

O_LIAssessing individual components of biodiversity, such as local or regional taxon richness, and differences in community composition is a long-standing challenge in ecology. It is especially relevant in spatially structured and diverse ecosystems. Environmental DNA (eDNA) has been suggested as a novel technique to accurately measure biodiversity. However, we do not yet fully understand the comparability of eDNA-based assessments to previously used approaches. C_LIO_LIWe sampled may-, stone-, and caddisfly genera with contemporary eDNA and kicknet methods at 61 sites distributed over a large river network, allowing a comparison of various diversity measures from the catchment to site levels and providing insights into how these measures relate to network properties. We extended our survey data with historical records of total diversity at the catchment level. C_LIO_LIAt the catchment scale, eDNA and kicknet detected similar proportions of the overall and cumulative historically documented species richness (gamma diversity), namely 42% and 46%, respectively. We further found a good overlap (62%) between the two contemporary methods at the regional scale. C_LIO_LIAt the local scale, we found highly congruent values of local taxon richness (alpha diversity) between eDNA and kicknet. Richness of eDNA was positively related with discharge, a descriptor of network position, while kicknet was not. C_LIO_LIBeta diversity between sites was similar for the two contemporary methods. Contrary to our expectation, however, beta diversity was driven by species replacement and not by nestedness. C_LIO_LIAlthough optimization of eDNA approaches is still needed, our results indicate that this novel technique can capture extensive aspects of gamma diversity, proving its potential utility as a new tool for large sampling campaigns across hitherto understudied complete river catchments, requiring less time and becoming more cost-efficient than classical approaches. Overall, the richness estimated with the two contemporary methods is similar at both local and regional scale but community composition is differently assessed with the two methods at individual sites and becomes more similar with higher discharge. C_LI

ecology↗

bioRxiv2019

Evaluating the stoichiometric trait distributions of cultured bacterial populations and uncultured microbial communities

Originality StatementThe ecological stoichiometry of microbial biomass has most often focused on the ratio of the biologically-important elements carbon (C), nitrogen (N), and phosphorus (P) and has primarily been examined at a resolution where the contribution of the individual is masked by the reported population or community average. However, reporting population or community averages makes it difficult to assess phenotypic plasticity and stochasticity and mask important information required to understand both the drivers and implications of microbial biomass stoichiometry in nature. One way to assess the diversity of individual microbial phenotypes is through the use of single-cell techniques such as energy dispersive spectroscopy (EDS). EDS reports cellular quotas for the majority of elements composing microbial biomass including C, N, and P. In this study, by measuring individual cells within a microbial community or population, we describe for the first time the stoichiometry of microbial biomass as a distribution instead of an average. Exploration of stoichiometric trait distributions (as presented here) has the potential to improve our understanding of how nutrients interact with individual microorganisms to structure the elemental content of bacterial biomass and better describe how bacterial community biomass affects the ecosystems within which these organisms exist. SummaryTo assess the potential for EDS to describe the stoichiometric variance within populations and communities we measured the stoichiometric trait distribution of cultured freshwater bacterial populations under different resource conditions and compared them to natural microbial communities sampled from three lakes. Mean biomass C:N:P values obtained by EDS matched closely to those obtained by bulk measures using traditional analytical techniques for each freshwater isolate. However, we observed pronounced differences in the stoichiometric trait distributions of freshwater bacterial isolates compared to the stoichiometric trait distributions of natural communities. The stoichiometric trait distribution of the environmental isolates changed with P availability, growth phase, and genotype, with P availability having the strongest effect. The distribution of biomass ratios within each isolate growth experiment were the most constrained during stages of rapid growth and commonly had unimodal distributions. In contrast to the population distributions, the distribution of N:P and C:P for a similar number of cells from each of the mixed lake communities had narrower stoichiometric distributions and more commonly exhibited multiple modes.

ecology↗

bioRxiv2019

Rare Microbial Taxa Emerge When Communities Collide: Freshwater and Marine Microbiome Responses to Experimental Seawater Intrusion

Whole microbial communities regularly merge with one another, often in tandem with their environments, in a process called community coalescence. Such events allow us to address a central question in ecology - what processes shape community assembly. We used a reciprocal transplant and mixing experiment to directly and independently unravel the effects of environmental filtering and biotic interactions on microbiome success when freshwater and marine communities coalesce. The brackish treatment and community mixing resulted in strong convergence of microbiome structure and function toward the marine. Brackish exposure imposed a 96% taxa loss from freshwater and 66% loss from marine microbiomes, which was somewhat counterbalanced by the emergence of tolerant rare taxa. Community mixing further resulted in 29% and 49% loss from biotic interactions between freshwater and marine microbiomes, offset somewhat by mutualistically-assisted rare microbial taxa. Our study emphasizes the importance of the rare biosphere as a critical component of community resilience.

ecology↗

bioRxiv2019

Semi-quantitative characterisation of mixed pollen samples using MinION sequencing and Reverse Metagenomics (RevMet)

1. The ability to identify and quantify the constituent plant species that make up a mixed-species sample of pollen has important applications in ecology, conservation, and agriculture. Recently, metabarcoding protocols have been developed for pollen that can identify constituent plant species, but there are strong reasons to doubt that metabarcoding can accurately quantify their relative abundances. A PCR-free, shotgun metagenomics approach has greater potential for accurately quantifying species relative abundances, but applying metagenomics to eukaryotes is challenging due to low numbers of reference genomes. 2. We have developed a pipeline, RevMet (Reverse Metagenomics), that allows reliable and semi-quantitative characterization of the species composition of mixed-species eukaryote samples, such as bee-collected pollen, without requiring reference genomes. Instead, reference species are represented only by genome skims: low-cost, low-coverage, short-read sequence datasets. The skims are mapped to individual long reads sequenced from mixed-species samples using the MinION, a portable nanopore sequencing device, and each long read is uniquely assigned to a plant species. 3. We genome-skimmed 49 wild UK plant species, validated our pipeline with mock DNA mixtures of known composition, and then applied RevMet to pollen loads collected from wild bees. We demonstrate that RevMet can identify plant species present in mixed-species samples at proportions of DNA [≥]1%, with few false positives and false negatives, and reliably differentiate species represented by high versus low amounts of DNA in a sample. 4. The RevMet pipeline could readily be adapted to generate semi-quantitative datasets for a wide range of mixed eukaryote samples, which could include characterising diets, quantifying allergenic pollen from air samples, quantifying soil fauna, and identifying the compositions of algal and diatom communities. Our per-sample costs were {pound}90 per genome skim and {pound}60 per pollen sample, and new versions of sequencers available now will further reduce these costs.

ecology↗

bioRxiv2019

Future sea-level rise drives rocky intertidal habitat loss and benthic community change

Rocky intertidal ecosystems may be particularly susceptible to sea-level rise impacts but few studies have explored community scale response to future sea-level scenarios. Combining remote-sensing with large-area imaging, we quantify habitat extent and describe biological community structure at two rocky intertidal study locations in California. We then estimate changes in habitat area and community composition under a range of sea-level rise scenarios using a model-based approach. Our results suggest that future sea-level rise will significantly reduce rocky intertidal area at our study locations, leading to an overall decrease in benthic habitat and a reduction in overall invertebrate abundances, but increased densities of certain taxa. These results suggest that sea-level rise may fundamentally alter the structure and function of rocky intertidal systems. As large scale environmental changes such as sea-level rise accelerate in the next century, more extensive spatially-explicit monitoring at ecologically relevant scales will be needed to visualize and quantify the impacts to biological systems.

ecology↗