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bioRxiv2018

Stability Criteria for Complex Microbial Communities

Competition and mutualism are inevitable processes in microbial ecology, and a central question is which and how many taxa will persist in the face of these interactions. Ecological theory has demonstrated that when direct, pairwise interactions among a group of species are too numerous, or too strong, then the coexistence of these species will be unstable to any slight perturbation. This instability worsens when mutualistic interactions complement competition. Here, we refine and to some extent overturn that understanding, by considering explicitly the resources that microbes consume and produce. In contrast to more complex organisms, microbial cells consume primarily abiotic resources, and mutualistic interactions are often mediated by these same abiotic resources through the mechanism of cross-feeding. Our model therefore considers the consumption and production of a set of abiotic resources by a group of microbial species. We show that if microbes consume, but do not produce resources, then any positive equilibrium will always be stable to small perturbations. We go on to show that in the presence of crossfeeding, stability is no longer guaranteed. However, stability still holds when mutualistic interations are either symmetric, or sufficiently weak.

ecology↗

bioRxiv2018

npphen: an R-package for non-parametric reconstruction of vegetation phenology and anomaly detection using remote sensing

For ecologists, the challenge at using remote sensing tools is to convert spectral data into ecologically relevant information like abundance, productivity or traits distribution. Among these features, plant phenology is one of the most used variables in any study applying remote sensing to plant ecology and it has formally considered as one of the Essential Biodiversity Variables. Currently, satellite imagery make possible cost-efficient monitoring of land surface phenology (LSP), but methods applicable to different ecosystems are not available. Here, we introduce the npphen R-package developed for remote sensing LSP reconstruction and anomaly detection using non-parametric techniques. The package implements basic and high-level functions for manipulating vector and raster data to obtain high resolution spatial and temporal LSP reconstructions. Advantages of npphen are: its flexibility to describe any LSP pattern (suitable for any ecosystem), it handles time series or raster stacks with and without gaps, and it provides confidence interval for the expected LSP at yearly basis, useful to judge anomaly magnitudes. We present two study cases to show how npphen can successfully reconstruct and map LSP and anomalies for contrasting ecosystems.

ecology↗

bioRxiv2018

A geometric approach to scaling individual distributions to macroecological patterns

Understanding macroecological patterns across scales is a central goal of ecology and a key need for conservation biology. Much research has focused on quantifying and understanding macroecological patterns such as the species-area relationship (SAR), the endemic-area relationship (EAR) and relative species abundance curve (RSA). Understanding how these aggregate patterns emerge from underlying spatial pattern at individual level, and how they relate to each other, has both basic and applied relevance. To address this challenge, we develop a novel spatially explicit geometric framework to understand multiple macroecological patterns, including the SAR, EAR, RSA, and their relationships, using theory of point processes. The geometric approach provides a theoretical framework to derive SAR, EAR, and RSA from species range distributions and the pattern of individual distribution patterns therein. From this model, various well-documented macroecological patterns are recovered, including the tri-phasic SAR on a log-log plot with its asymptotic slope, and various RSAs (e.g., Fisher {boxtimes} s logseries and the Poisson lognormal distribution). Moreover, this approach can provide new insights such as a single equation describing the RSA at an arbitrary spatial scale, and explicit forms of the EAR with its asymptotic slope. The theory, which links spatial distributions of individuals and species with macroecological patterns, is ambiguous with regards to the mechanism(s) responsible for the statistical properties of individual distributions and species range sizes. However, our approach can be connected to mechanistic models that make such predictions about lower-level patterns and be used to scale them up to aggregate patterns, and therefore is applicable to many ecological questions. We demonstrate an application of the geometric model to scaling issue of beta diversity.

ecology↗

bioRxiv2018

Seed predation increases from the Arctic to the Equator and from high to low elevations

Species interactions have long been predicted to increase in intensity towards the tropics and low elevations, due to gradients in climate, productivity, or biodiversity. Despite their importance for understanding global ecological and evolutionary processes, plant-animal interaction gradients are particularly difficult to test systematically across large geographic gradients, and evidence from smaller, disparate studies is inconclusive. By systematically measuring post-dispersal seed predation using 6980 standardized seed depots along 18 mountains in the Pacific cordillera, we found that seed predation increases 18% from the Arctic to Equator and 16% from 4000 masl to sea level. Clines in total predation, likely driven by invertebrates, were consistent across tree-line ecotones and in continuous forest, and were better explained by climate seasonality than by productivity, biodiversity, or latitude. These results suggest that species interactions play predictably greater ecological and evolutionary roles in tropical, lowland, and other less seasonal ecosystems.\n\nOne Sentence SummaryPost-dispersal seed predation increases from the Arctic to the Equator and from high elevations to sea level.

ecology↗

bioRxiv2018

When higher carrying capacities lead to faster propagation

This preprint has been reviewed and recommended by Peer Community In Ecology (https://dx.doi.org/10.24072/pci.ecology.100004). Finding general patterns in the expansion of natural populations is a major challenge in ecology and invasion biology. Classical spatio-temporal models predict that the carrying capacity (K) of the environment should have no influence on the speed (v) of an expanding population. We tested the generality of this statement with reaction-diffusion equations, stochastic individual-based models, and microcosms experiments with Trichogramma chilonis wasps. We investigated the dependence between K and v under different assumptions: null model (Fisher-KPP-like assumptions), strong Allee effects, and positive density-dependent dispersal. These approaches led to similar and complementary results. Strong Allee effects, positive density-dependent dispersal and demographic stochasticity in small populations lead to a positive dependence between K and v. A positive correlation between carrying capacity and propagation speed might be more frequent than previously expected, and be the rule when individuals at the edge of a population range are not able to fully drive the expansion.

ecology↗

bioRxiv2018

Habitat heterogeneity reduces the richness of ant species by increasing abundance of the local dominant species

The effect of environmental heterogeneity on species richness is frequently discussed in ecology. However, the empirical evidence has been contradictory as to the direction of the effect. Although some authors have considered that this might be a methodological problem, we argue that for ants, ecological interactions within the community, as interspecific competition is more important. We analyzed the plausibility of models in explaining the ant richness distribution patterns in a semi-desert environment. We used three predicting variables in the construction of the models to explain ant richness distribution: heterogeneity based on the amount of structures regardless of their type, heterogeneity based on the diversity of structures, and the abundance of individuals of the dominant species. We used ANOVA to chose the best model and corroborated the prediction that in this system abundance of dominant species is the best predictor of ant species richness. Neither of the heterogeneity conceptions contributed much to explain richness distribution. However, in a second analysis, we concluded that heterogeneity could affect the abundance of the dominant species. We conclude that competitive dominance is a better predictor of species richness distribution patterns than structural heterogeneity. However, the structural heterogeneity affects the distribution of dominant individuals. We suggest that some unexplained patterns observed about the relationship between heterogeneity and richness could be due to an indirect effect.

ecology↗

bioRxiv2018

Testing soil nematode extraction efficiency using different variations of the Baermann funnel method

Nematodes are increasingly used as powerful bioindicators of soil food web composition and functioning in ecological studies. Todays ecological research aims to investigate not only local relationships but global patterns, which requires consistent methodology across locales. Thus, a common and easy extraction protocol of soil nematodes is needed. In this study, we present a detailed protocol of the Baermann funnel method and highlight how different soil pre-treatments and equipment (soil type, amount of soil, sieving, filter type) can affect extraction efficiency and community composition by using natural nematode communities. We found that highest nematode extraction efficiency was achieved using lowest soil weight (25 g instead of 50 g or 100 g) in combination with soil sieving, and by using milk filters (instead of paper towels). PCA at the family level revealed that different pre-treatments significantly affected nematode community composition. Increasing the amount of soil increased the proportion of larger-sized nematodes being able to overcome long distances. Sieving is suggested to break up soil aggregates and, therefore, facilitate moving in general. Interestingly, sieving did not negatively affect larger nematodes that are supposed to have a higher probability of getting bruised during sieving. The present study shows that variations in the extraction protocol can alter the total density and community composition of extracted nematodes and provides recommendations for an efficient and standardized approach in future studies. Having a simple, cheap, and standardized extraction protocol can facilitate the assessment of soil biodiversity in global contexts.

ecology↗

bioRxiv2018

On the importance of accounting for intraspecific genomic relatedness in multi-species studies

1. Analyses in many fields of ecology are increasingly considering multiple species and multiple individuals per species. Premises of statistical tests are often violated with such datasets because of the non-independence of residuals due to phylogenetic relationships or intraspecific population structure. Comparative approaches that account for the phylogenetic relationship of species--for which the benefits are demonstrated--are well developed. However, the importance of accounting for the intraspecific genetic structure, especially with the phylogenetic structure, is rarely addressed in the ecological literature.\n\n2. We investigated whether it is beneficial to account for intraspecific genomic relatedness in multi-species studies. For this, we used a Phylogenetic Mixed Model to analyze simulated data and results from a budburst experiment where clippings of 10 tree and shrub species were subjected to different treatments in terms of temperature and photoperiod.\n\n3. We found that accounting for intraspecific genomic relatedness yields more accurate and precise fixed effects as well as increased statistical power, but more so when the relative importance of the intraspecific to the phylogenetic genetic structure is greater. Analysis of the budburst experiment further showed that accounting for intraspecific and phylogenetic structures yields improved estimates of warming and photoperiod effects and their interactions in explaining the time to budburst.\n\n4. Our results show that important statistical gains can be made by incorporating information on the intraspecific genomic relatedness of individuals in multi-species studies. This is relevant for investigations that are interested in intraspecific variation and that plan to include such observations in statistical tests.

ecology↗

bioRxiv2018

A quantitative framework for investigating the reliability of network construction

Descriptions of ecological networks typically assume that the same interspecific interactions occur each time a community is observed. This contrasts with the known stochasticity of ecological communities: community composition, species abundances, and link structure all vary in space and time. Moreover, finite sampling generates variation in the set of interactions actually observed. Here we develop the conceptual and analytical tools needed to capture uncertainty in the estimation of pairwise interactions. To define the problem, we identify the different contributions to the uncertainty of an interaction and its implications for the estimation of network properties. We then outline a framework to quantify the uncertainty around each interaction. We illustrate this framework using the most extensively sampled network to date. We found significant uncertainty in estimates for the probability of most pairwise interactions which we could, however, limit with informative priors. Through these efforts, we demonstrate the utility of our approach and the importance of acknowledging the uncertainty inherent in network studies. Most importantly, we stress that networks are best thought of as systems constructed from random variables, the stochastic nature of which must be acknowledged for an accurate representation. Doing so will fundamentally change networks analyses and yield greater realism.

ecology↗

bioRxiv2018

High-order interactions dominate the functional landscape of microbial consortia

Understanding the link between community composition and function is a major challenge in microbial ecology, with implications for the management of natural microbiomes and the design of synthetic consortia. For this purpose, it is critical to understand the extent to which community functions and properties can be predicted from species traits and what role is played by complex interactions. Inspired by the study of complex genetic interactions and fitness landscapes, here we have examined how the amylolytic function of combinatorial assemblages of seven starch-degrading soil bacteria depends on the functional contributions from each species and their interactions. Filtering our experimental results through the theory of enzyme kinetics, we show that high-order functional interactions dominate the amylolytic rate of our consortia, even though this function is biochemically simple, redundantly distributed in the community, and additive in the absence of inter-species interactions. As the community grows in size, the contribution of high-order functional interactions grows too, making the community function increasingly unpredictable. We can explain the prevalence of high order effects and their sign from the redundancy of ecological interactions in the network, in particular from redundant facilitation towards a high-performing community member. Our results suggest that even simple functions can be dominated by complex interactions, posing challenges for the predictability and bottom-up engineering of ecosystem function in complex multi-species communities.

ecology↗

bioRxiv2018

Vegetation monitoring using multispectral sensors - best practices and lessons learned from high latitudes

Emerging drone technologies have the potential to revolutionise ecological monitoring. The rapid technological advances in recent years have dramatically increased affordability and ease of use of Unmanned Aerial Vehicles (UAVs) and associated sensors. Compact multispectral sensors, such as the Parrot Sequoia (Paris, France) and MicaSense RedEdge (Seattle WA, USA) capture spectrally accurate high-resolution (fine grain) imagery in visible and near-infrared parts of the electromagnetic spectrum, providing supplement to satellite and aircraft-based imagery. Observations of surface reflectance can be used to calculate vegetation indices such as the Normalised Difference Vegetation Index (NDVI) for productivity estimates and vegetation classification. Despite the advances in technology, challenges remain in capturing consistently high-quality data, particularly when operating in extreme environments such as the high latitudes. Here, we summarize three years of ecological monitoring with drone-based multispectral sensors in the remote Canadian Arctic. We discuss challenges, technical aspects and practical considerations, and highlight best practices that emerged from our experience, including: flight planning, factoring in weather conditions, and geolocation and radiometric calibration. We propose a standardised methodology based on established principles from remote sensing and our collective field experiences, using the Parrot Sequoia sensor as an example. With these good practises, multispectral sensors can provide meaningful spatial data that is reproducible and comparable across space and time.

ecology↗

bioRxiv2018

Comparison of large-scale citizen science data and long-term study data for phenology modeling

Large-scale observational data from citizen science efforts are becoming increasingly common in ecology, and researchers often choose between these and data from intensive local-scale studies for their analyses. This choice has potential trade-offs related to spatial scale, observer variance, and inter-annual variability. Here we explored this issue with phenology by comparing models built using data from the large-scale, citizen science National Phenology Network (NPN) effort with models built using data from more intensive studies at Long Term Ecological Research (LTER) sites. We built process based phenology models for species common to each dataset. From these models we compared parameter estimates, estimates of phenological events, and out-of-sample errors between models derived from both NPN and LTER data. We found that model parameter estimates for the same species were most similar between the two datasets when using simple models, but parameter estimates varied widely as model complexity increased. Despite this, estimates for the date of phenological events and out-of-sample errors were similar, regardless of the model chosen. Predictions for NPN data had the lowest error when using models built from the NPN data, while LTER predictions were best made using LTER-derived models, confirming that models perform best when applied at the same scale they were built. Accordingly, the choice of dataset depends on the research question. Inferences about species-specific phenological requirements are best made with LTER data, and if NPN or similar data are all that is available, then analyses should be limited to simple models. Large-scale predictive modeling is best done with the larger-scale NPN data, which has high spatial representation and a large regional species pool. LTER datasets, on the other hand, have high site fidelity and thus characterize inter-annual variability extremely well. Future research aimed at forecasting phenology events for particular species over larger scales should develop models which integrate the strengths of both datasets.

ecology↗

bioRxiv2018

Neighborhood size-effects in nonlinear public goods games

Ecological and evolutionary dynamics can be strongly affected by population assortment and frequency-dependent selection. In growing populations, a particular challenge is to disentangle global ecological effects from local frequency-dependent effects. Here we implement a logistic growth and death model on the global scale, coupled to frequency-dependent growth rates influenced by a public goods game between cooperators and defectors. For each individual, the public good is only effective within a neighborhood of other individuals, and the public good-growth rate relationship can be nonlinear. At low numbers of cooperators, increases of public good accumulate synergistically; at high numbers, increases in public good only provide diminishing returns-the inflection point of this pattern is given by the strength of frequency-dependent selection in relation to the background fitness effect. We observed complex critical behavior in the evolutionary dynamics equilibria, determined by the relative magnitude of frequency-dependent to constant (background) growth benefits. We predict neighborhood-size-driven state changes, hysteresis between polymorphic and monomorphic equilibria, and observed that type-dependent differences in neighborhood sizes can destabilize monomorphic cooperative states but increase coexistence of cooperators and defectors. Stochastic neighborhood size fluctuations also led to coexistence and could stabilize the purely cooperative equilibrium. Our results quantify the role of assortment through neighborhood-size effects and nonlinearity of the gains function in eco-evolutionary dynamics, which is relevant for a variety of microbial and cellular public goods games.

ecology↗

bioRxiv2018

Food web networks shift across a precipitation gradient due to changes in community composition and species interactions.

Ecological networks change across spatial and environmental gradients due to (i) changes in species composition or (ii) changes in the frequency or strength of interactions. Here we use the communities of aquatic invertebrates inhabiting clusters of bromeliad phytotelms along the Brazilian coast as a model system for examining turnover in the properties of ecological networks. We first document the variation in the species pools of sites across a geographical climate gradient. Using the same sites, we also explored the geographic variation in species interaction strength using a newly developed Markov network approach. We found that community composition differed along a gradient of water volume within bromeliads due to the turnover of some species. From the Markov network analysis, we found that the top-down effects of certain predators differed geographically, which could also be explained by geographic differences in bromeliad water volumes. Overall, this study illustrates how a network can change across an environmental gradient through both changes in both species and their interactions.

ecology↗

bioRxiv2018

Pyramids and cascades: a synthesis of food chain functioning and stability

Food chain theory is one of the cornerstones of ecology, providing many of its basic predictions, such as biomass pyramids, trophic cascades and predator-prey oscillations. Yet, ninety years into this theory, the conditions under which these patterns may occur and persist in nature remain subject to debate. Rather than address each pattern in isolation, we propose that they must be understood together, calling for synthesis in a fragmented landscape of theoretical and empirical results. As a first step, we propose a minimal theory that combines the long-standing energetic and dynamical approaches of food chains. We chart theoretical predictions on a concise map, where two main regimes emerge: across various functioning and stability metrics, one regime is characterized by pyramidal patterns, the other by cascade patterns. The axes of this map combine key physiological and ecological variables, such as metabolic rates and self-regulation. A quantitative comparison with data sheds light on conflicting theoretical predictions and empirical puzzles, from size spectra to causes of trophic cascade strength. We conclude that drawing systematic connections between various existing approaches to food chains, and between their predictions on functioning and stability, is a crucial step in confronting this theory to real ecosystems.

ecology↗

bioRxiv2018

Disentangling bacterial invasiveness from lethality in an experimental host-pathogen system

Quantifying virulence remains a central problem in human health, pest control, disease ecology, and evolutionary biology. Bacterial virulence is typically quantified by the LT50 (i.e. the time taken to kill 50% of infected hosts), however, such an indicator cannot account for the full complexity of the infection process, such as distinguishing between the pathogens ability to colonize vs. kill the hosts. Indeed, the pathogen needs to breach the primary defenses in order to colonize, find a suitable environment to replicate, and finally express the virulence factors that cause disease. Here, we show that two virulence attributes, namely pathogen lethality and invasiveness, can be disentangled from the survival curves of a laboratory population of Caenorhabditis elegans nematodes exposed to three bacterial pathogens: Pseudomonas aeruginosa, Serratia marcescens and Salmonella enterica. We first show that the host population eventually experiences a constant mortality rate, which quantifies the lethality of the pathogen. We then show that the time necessary to reach this constant-mortality rate regime depends on the pathogen growth rate and colonization rate, and thus determines the pathogen invasiveness. Our framework reveals that Serratia marcescens is particularly good at the initial colonization of the host, whereas Salmonella enterica is a poor colonizer yet just as lethal once established. Pseudomonas aeruginosa, on the other hand, is both a good colonizer and highly lethal after becoming established. The ability to quantitatively characterize the ability of different pathogens to perform each of these steps has implications for treatment and prevention of disease and for the evolution and ecology of pathogens.

ecology↗

bioRxiv2018

Long-term blooming filamentous calcareous alga provides substrate for other coral-competitive macroalgae in the degraded lagoon of Dongsha Atoll (Pratas), Taiwan

BackgroundThe chemically-rich seaweed Galaxaura is not only highly competitive with corals, but also provides substrate for other macroalgae. Its ecology and associated epiphytes remain largely unexplored. To fill this knowledge gap, we herein undertook an ecological assessment to explore the spatial variation, temporal dynamics, and epiphytic macroalgae of G. divaricata on patch reefs in the lagoon of Dongsha Atoll, a shallow coral reef ecosystem in the northern South China Sea, repeatedly impacted by mass coral bleaching events.\n\nMethodsTwelve spatially independent patch reefs in the Dongsha lagoon were first surveyed to assess the benthic composition in April 2016, and then revisited to determine G. divaricata percent cover in September 2017, with one additional Galaxaura-dominated reef (site 9). Four surveys over a period of 17 months were carried out on a degraded patch reef (site 7) to assess the temporal variation in G. divaricata cover. Epiphytic macroalgae associated with G. divaricata were quantified and identified through the aid of DNA barcoding.\n\nResultsPatch reefs in the Dongsha lagoon were degraded, exhibiting relatively low live coral cover (5-43%), but high proportions of macroalgae (13-58%) and other substrates (rubble and dead corals; 23-69%). The distribution of G. divaricata was heterogeneous across the lagoon, with highest abundance (16-41%) in the southeast area. Temporal surveys from site 7 and photo-evidence from site 9 suggested that an overgrowth by G. divaricata was still present to a similar extend after 17 months and 3.5 years. Yet, G. divaricata provides a suitable substrate some allelopathic macroalgae (e.g., Lobophora sp.).\n\nConclusionsOur study demonstrates that an allelopathic seaweed, such as G. divaricata, can overgrow degraded coral reefs for extended periods of time. By providing habitat for harmful macroalgae, a prolonged Galaxaura overgrowth could strengthen negative feedback loops on degraded coral reefs, further decreasing their recovery potential.

ecology↗

bioRxiv2018

A national ‘safe and just operating space’ for all in India: Past, Present and Futurea

With 1.3 billion populaces on the commencement of the 21st century, India is currently impending towards upholding a subtle equilibrium between persisting social development and well-being without depleting existing biophysical resources at the national level or surpassing global average per capita obtainability. In this paper, we have structured a top-down per capita framework to explore national safe and just operating space (NSJOS) to apprehend not only past fluctuations that bring about the present conditions but also the plausible future consequences, with India as a case study. Coalescing 27 indicators, all pertaining to Sustainable Development Goals (except - SDG 17), accompanied by their corresponding environmental boundaries or preferred social thresholds, present study probes into both biophysical (for environmental stress) and social development (for social deficit) attributes of India. This analysis shows India has already crossed three of seven dimensions of biophysical boundaries (freshwater, nitrogen and phosphorus use). Also, at the existing rate, India is going to cross the remainder of the boundaries within 2045-2050 (climate change, arable land use, ecological and material footprint). Of 20 indicators used for social development, only five have already or will meet corresponding desired thresholds of United Nations Sustainable Development Goals 2015. Using tendencies of past variations, the results indicate that if lowest per capita consumption can be attained and uphold, even with projected population growth, total consumption of four biophysical resources (climate change, nitrogen use, ecological and material footprint) can be slashed from todays level in 2050. Adaptations in national policy are indispensable if India wants to accomplish sufficiency in biophysical resources whilst bestowing social equity in access and exploitation of those resources towards the continuance of social developments in forthcoming times.

ecology↗