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bioRxiv2021

Microclimates can be accurately predicted across ecologically important remote ecosystems

Microclimate information is often crucial for understanding ecological patterns and processes, including under climate change, but is typically absent from ecological and biogeographic studies owing to difficulties in obtaining microclimate data. Recent advances in microclimate modelling, however, suggest that microclimate conditions can now be predicted anywhere at any time using hybrid physically- and empirically-based models. Here, for the first time, we test the utility of this approach across a remote, inaccessible, and climate change threatened polar island ecosystem at ecologically relevant scales. Microclimate predictions were generated at a 100 x 100 m grain (at a height of 4 cm) across the island, with models parameterised using either meteorological observations from the islands weather station (AWS) or climate reanalysis data (CRA). AWS models had low error rates and were highly correlated with observed seasonal and daily temperatures (root mean squared error of predicted seasonal average Tmean [≤] 0.6 {degrees}C; Pearsons correlation coefficient (r) for the daily Tmean [≥] 0.86). By comparison, CRA models had a slight warm bias in all seasons and a smaller diurnal range in the late summer period than in situ observations. Despite these differences, the modelled relationship between the percentage cover of the threatened endemic cushion plant Azorella macquariensis and microclimate varied little with the source of microclimate data (r = 0.97), suggesting that both model parameterisations capture similar patterns of spatial variation in microclimate conditions across the island ecosystem. Here, we have shown that the accurate prediction of microclimate conditions at ecologically relevant spatial and temporal scales is now possible using hybrid physically- and empirically-based models across even the most remote and climatically extreme environments. These advances will help add the microclimate dimension to ecological and biogeographic studies, which could be critical for delivering climate change-resilient conservation planning in climate-change exposed ecosystems.

ecology↗

bioRxiv2021

Effects of stochasticity on the length and behaviour of ecological transients

There is a growing recognition that ecological systems can spend extended periods of time far away from an asymptotic state, and that ecological understanding will therefore require a deeper appreciation for how long ecological transients arise. Recent work has defined classes of deterministic mechanisms that can lead to long transients. Given the ubiquity of stochasticity in ecological systems, a similar systematic treatment of transients that includes the influence of stochasticity is important. Stochasticity can of course promote the appearance of transient dynamics by preventing systems from settling permanently near their asymptotic state, but stochasticity also interacts with deterministic features to create qualitatively new dynamics. As such, stochasticity may shorten, extend, or fundamentally change a systems transient dynamics. Here, we describe a general framework that is developing for understanding the range of possible outcomes when random processes impact the dynamics of ecological systems over realistic time scales. We emphasize that we can understand the ways in which stochasticity can either extend or reduce the lifetime of transients by studying the interactions between the stochastic and deterministic processes present, and we summarize both the current state of knowledge and avenues for future advances.

ecology↗

bioRxiv2021

Let's Do the Time Warp Again: Non-linear time series matching as a tool for sequentially structured data in ecology

Ecological patterns are often fundamentally chronological. However, generalization of data is necessarily accompanied by a loss of detail or resolution. Temporal data in particular contains information not only in data values but in the temporal structure, which is lost when these values are aggregated to provide point estimates. Dynamic Time Warping (DTW) is a time series comparison method that is capable of efficiently comparing series despite temporal offsets that confound other methods. The DTW method is both efficient and remarkably flexible, capable of efficiently matching not only time series but any sequentially structured dataset, which has made it a popular technique in machine learning, artificial intelligence, and big data analytical tasks. DTW is rarely used in ecology despite the ubiquity of temporally structured data. As technological advances have increased the richness of small-scale ecological data, DTW may be an attractive analysis technique because it is able to utilize the additional information contained in the temporal structure of many ecological datasets. In this study we use an example dataset of high-resolution fish movement records obtained from otolith microchemistry to compare traditional analysis techniques with DTW clustering. Our results suggest that DTW is capable of detecting subtle behavioral patterns within otolith datasets which traditional data aggregation techniques cannot. These results provide evidence that the DTW method may be useful across many of the temporal data types commonly collected in ecology, as well other sequentially ordered "pseudo time series" data such as classification of species by shape. Keywords: classification, cluster analysis, data generalization, DTW, dynamic time warping, otolith chemistry, time series

ecology↗

bioRxiv2021

Impact of Grain for Green Project on Water Resources and Ecological Water Stress in Yanhe River Basin

Grain for Green project (GGP) initialed by China government since 1999 has achieved substantial achievements accompanied with surface runoff decrease in the Loess Plateau but impacts of large-scale afforestation on regional water resources are uncertain. Hence, the objective of this study is to explore the impact of land use change on generalized water resources and ecological water stress using blue and green water concept taking Yanhe River Basin as a case study. Soil and Water Assessment Tool (SWAT) is applied to quantify summary of green and blue water which is defined as generalized water resources, ecological water requirement of vegetation (forest and grass), agricultural water footprint and virtual water flow are considered as regional water requirements. Land use types of 1980 (scenario I), 2017 (scenario II) are input in SWAT model while keeps other parameters constant in order to isolate the influence of land use changes. Results show that average annual difference of blue, green and generalized water resources is -72.08 million m3, 24.34 million m3, -47.74 million m3 respectively when simulation results of scenario II subtracts scenario I and it presents that land use change caused by GGP leads to decrease in blue and generalized water resources whereas increase in green water resources. SURQ in scenario I is more than that in scenario IIin all the study period from 1980-2017, green water storage in scenario I is more than that in scenario II in all the study period except in 1998; whereas LATQ in scenario I is less than that in scenario II except in 2000 and 2015, GWQ in 1992, 2000 and 2015, green water flow in 1998. Blue water, green water storage and green water flow in scenario II is less than that in scenario I in the whole basin, 12.89 percent of the basin and 99.21 percent of the basin respectively. Total WF increases from 1995 to 2010 because forest WF increases significantly in this period though agricultural WF and grass WF decreases. Ecological water stress index has no obvious temporal change trend in both land use scenarios but ecological water stress index in scenario II is more than that in scenario I which illustrates that GGP leads to increase of ecological water stress from perspective of generalized water resources

ecology↗

bioRxiv2021

Attracting pollinators vs escaping herbivores: eco-evolutionary dynamics of plants confronted with an ecological trade-off

Many plant traits are subject to an ecological trade-off between attracting pollinators and escaping herbivores. The interplay of both plant-animal interaction types determines their evolution. As most studies focus on either pollination or herbivory, how they jointly affect the eco-evolutionary dynamics of plant-animal communities is often left unknown. Within a plant-pollinator-herbivore community where interaction strengths depend on trait matching, we consider the evolution of a plant trait involved in both plant-animal interactions. Using adaptive dynamics, we uncover when stabilizing, runaway (i.e. directional) or disruptive selection emerges and its consequences for multispecies coexistence. We find that strong pollination relative to herbivory favors stabilizing selection and coexistence. Strong herbivory relative to pollination fosters runaway selection and threatens coexistence. Importantly, given balanced interactions, joint effects may lead to disruptive selection, allowing the emergence of plant dimorphism. The strength of the ecological trade-off largely explains the occurrence of these contrasting eco-evolutionary dynamics. In particular, plant diversification requires strong trade-offs, with the strongest trade-offs allowing long-term polymorphism. We discuss how our results relate to various empirical cases where the interplay of pollination and herbivory maintains plant polymorphism. Beyond maintenance, our work suggests that it might also have fueled the diversification process itself. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=116 SRC="FIGDIR/small/470900v5_ufig1.gif" ALT="Figure 1"> View larger version (21K): org.highwire.dtl.DTLVardef@13ec25org.highwire.dtl.DTLVardef@1f4a371org.highwire.dtl.DTLVardef@1654d2org.highwire.dtl.DTLVardef@1ab565_HPS_FORMAT_FIGEXP M_FIG Eco-evolutionary dynamics resulting from the evolution of plant phenotype under ecological trade-off A. Typical eco-evolutionary landscape. The type of selection and the ecological outcome depend on the dissimilarity between animal phenotypes (i.e. preferences for plant phenotype), which is a proxy for the strength of the ecological trade-off. B. The long-term community composition depends on the type of selection. (1) Runaway selection leads to the extinction of a first animal species as the plant phenotype is diverging. (a) Pollinators are lost first so that runway selection continues until herbivores are also lost. (b) Herbivores are lost first so that selection turns stabilizing over time, leading to a perfect plant-pollinator matching. (2) Stabilizing selection can enable the maintenance of coexistence. (3) Disruptive selection leads to the emergence of plant dimorphism. C_FIG

ecology↗

bioRxiv2022

Dynamic Generalised Additive Models (DGAM) for forecasting discrete ecological time series

O_LIGeneralised Additive Models (GAMs) are increasingly popular for describing smooth nonlinear relationships between predictors and response variables. GAMs are particularly relevant in ecology for representing hierarchical functions for discrete responses that encompass complex features including zero-inflation, bounding and uneven sampling. However, GAMs are less useful for producing forecasts as their smooth functions provide unstable predictions outside the range of training data. C_LIO_LIWe introduce Dynamic Generalised Additive Models (DGAMs), where the GAM linear predictor is jointly estimated with unobserved dynamic components to model time series that evolve as a function of nonlinear predictor associations and latent temporal processes. These models are particularly useful for multivariate series, as they can estimate hierarchical smooths while learning complex temporal associations via dimension-reduced latent factor processes. We implement our models in the mvgam R package, which uses the mgcv and rjags packages to construct smoothing splines and estimate unobserved parameters in a probabilistic framework. C_LIO_LIUsing simulations, we illustrate how our models outperform competing formulations in realistic ecological forecasting tasks while identifying important smooth predictor functions. We use a real-world case study to highlight some of mvgams key features, which include functions for: calculating correlations among series latent trends, performing model selection using rolling window forecasts, online data augmentation via a recursive particle filter, and visualising probabilistic uncertainties for smooths and predictions. C_LIO_LIDynamic GAMs (DGAM) offer a solution to the challenge of forecasting discrete time series while estimating ecologically relevant nonlinear predictor associations. Our Bayesian latent factor approach will be particularly useful for exploring competing dynamic ecological models that encompass hierarchical smoothing structures while providing forecasts with robust uncertainties, tasks that are becoming increasingly important in applied ecology. C_LI

ecology↗

bioRxiv2022

Phylogenetic and Ecological Trends in Specialization: Disentangling the Drivers of Ectoparasite Host Specificity

O_LIEcological specialization reflects both evolutionary and ecological processes. For parasitic taxa, ecological specialization can be assessed as the degree to which a parasite species will associate with certain host species, a property known as host specificity. C_LIO_LIEctoparasitic bat flies have been previously reported as highly host specific, presumably due to a history of coevolution with their bat hosts. However, there is conflicting evidence of coevolution between bats and bat flies. Resource-driven competition between parasite individuals and between species may also be important in explaining patterns of bat fly specificity. C_LIO_LITo test the importance of evolutionary and ecological factors on bat fly specificity, we collected and identified 21 bat fly species from 16 host bat species from the State of Rio de Janeiro in Brazil. We generated a bat fly species phylogeny from molecular data, estimated d specialization values (a metric of specificity), and used linear and cophylogenetic models to compare the importance of various drivers of parasite ecological specialization. C_LIO_LIWe found that bat fly co-occurrence frequency (a proxy for interspecific competition) and mean infection intensity (a proxy for intraspecific competition) best predicted patterns of bat fly specialization. Co-occurrence frequency had a significantly negative association with specialization, while mean infection intensity has a significantly positive association with specialization. Coevolutionary congruence had a small effect size and did not significantly predict parasite specialization. C_LIO_LIWe found multiple shifts toward more generalized host niches across the bat fly phylogeny. Our results suggest that ecological processes such as resource-driven competition may be more important than evolutionary processes in shaping bat fly host specialization networks. C_LIO_LIBat flies showed variable degrees of host specialization, parasitized phylogenetically distant host species, and showed low phylogenetic congruence to their hosts. This suggests that as a group, bat flies may show flexibility in their host preference phenotypes and may change their host associations in the face of environmental disturbance. C_LI

ecology↗

bioRxiv2022

A user-friendly guide to using distance measures to compare time series in ecology.

O_LITime series are a critical component of ecological analysis, used to track changes in biotic and abiotic variables. Information can be extracted from the properties of time series for tasks such as classification, clustering, prediction, and anomaly detection. These common tasks in ecological research rely on the notion of (dis-) similarity which can be determined by using distance measures. A plethora of distance measures have been described in the scientific literature, but many of them have not been introduced to ecologists. Furthermore, little is known about how to select appropriate distance measures and the properties they focus on for time-series related tasks. C_LIO_LIHere we describe 16 potentially desirable properties of distance measures, test 42 distance measures for each property, and present an objective method to select appropriate distance measures for any task and ecological dataset. We then demonstrate our selection method by applying it to a set of real-world data on breeding bird populations in the UK. We also discuss ways to overcome some of the difficulties involved in using distance measures to compare time series. C_LIO_LIOur real-world population trends exhibit a common challenge for time series comparison: a high level of stochasticity. We demonstrate two different ways of overcoming this challenge, first by selecting distance measures with properties that make them well-suited to comparing noisy time series, and second by applying a smoothing algorithm before selecting appropriate distance measures. In both cases, the distance measures chosen through our selection method are not only fit-for-purpose but are consistent in their rankings of the population trends. C_LIO_LIThe results of our study should lead to an improved understanding of, and greater scope for, the use of distance measures for comparing time series within ecology, and allow for the answering of new ecological questions. C_LI

ecology↗

bioRxiv2022

Trait-based paleontological niche prediction demonstrates deep time parallel ecological occupation in specialized ant predators

Understanding the paleoecology of species is fundamental to reconstructions of paleoecological communities, analyses of changing paleoenvironments, and the evolutionary history of many lineages. One method of establishing paleoecology is through comparing the morphology of extant analogs to extinct species; this method has been applied to many vertebrate groups using predictive linear models, but has been rarely applied using invertebrate taxa or non-linear frameworks. The ant fossil record, which frequently preserves specimens in three-dimensional fidelity, chronicles putative faunal turnover during the Late Cretaceous and into Cenozoic. The earliest fossil species comprise enigmatic stem-groups that underwent extinction concomitant with crown ant diversification. Here, we apply a wide-ranging extant ecomorphological dataset to demonstrate the utility of Random Forest machine learning classification in predicting the ecology of stem-group "hell ants". We reconstruct a predicted ecomorphological assemblage of this phenotypically aberrant group of extinct ants, and compare predicted hell ant ecologies to the ecological occupations of their closest living analogs, lineages of solitary predators with highly specialized mandibular morphology. In contrast to previous hypotheses, we find that hell ants were primarily leaf litter or ground-nesting and foraging taxa, and that the ecological breadth of this unusual lineage mirrored that of living groups. Results suggest ecological coherence between the Mesozoic and modern communities, even as the earliest occupants of predatory niches were phylogenetically and morphologically distinct.

ecology↗

bioRxiv2022

Mini-batching ecological data to improve ecosystem models with machine learning

Process-based, dynamic models are essential for extrapolating beyond current trends and anticipating biodiversity responses to global change. However, their practical adoption for forecasting purposes remains limited due to difficulties in calibrating them against data and structural inaccuracies in their mathematical formulations. While ecological time series could, in principle, be used to directly estimate model parameters and refine model structures, the large noise levels in ecological datasets and the strong nonlinearity of ecological dynamics challenge conventional calibration methods. Here, we present a robust and scalable calibration framework that addresses these challenges by integrating techniques from scientific computing and deep learning. Our approach combines a segmentation strategy where state variables are estimated independently, differentiable programming for efficient gradient computation, parameter transformations to ensure the feasibility and stability of the model simulations, and mini-batching to accomodate large datasets. Through comprehensive benchmarks using simulated food web dynamics of increasing complexity, we demonstrate that the framework substantially improves the convergence of gradient descent algorithms and Monte Carlo sampling methods, accommodating for realistic scenarios with noisy and partial observations. This yields improved parameter estimation and forecasts within both mode estimation and full posterior distribution contexts. Crucially, we show that the calibration framework scales effectively with both the number of parameters and state variables. The improved convergence and scalability of the calibration framework enables hybrid modeling approaches, where neural networks parameterize complex processes within the dynamic model. In particular, we demonstrate that neural networks can effectively capture environmental dependencies in demographic rates and recover functional responses governing trophic interactions. Neural network-based parameterizations have the capability to improve the structural inaccuracies of models while maintaining ecological interpretability through post-hoc analysis of learned representations. We provide an implementation of the calibration framework and other key utilities as the open-source Julia package HybridDynamicModels.jl, with the hope that the package will facilitate the development of hybrid modeling approaches in quantitative ecology and related fields.

ecology↗

bioRxiv2022

Automated community ecology using deep learning: a case study of planktonic foraminifera

The development of deep learning methods using convolutional neural networks (CNNs) has revolutionised the field of computer vision in recent years. The automation of taxonomic identification using CNNs leads naturally to the use of such technology for rapidly generating large organismal datasets in order to study the evolutionary and ecological dynamics of biological communities across time and space. While CNNs have been used to train machine learning classifiers that can identify organisms to the species level for several groups, this vision of automated community ecology has yet to be thoroughly tested or fulfilled. Here, we present a case study of automated community ecology using a large dataset of Atlantic planktonic foraminifera for which the generation of species labels and morphometric measurements was completely automated. We compare standard community diversity metrics between the fully automated dataset and a "traditional" dataset with human-identified specimens. We show that there is high congruence between the results, and that machine classifications help avoid biases that can result in the inference of misleading biodiversity patterns. Our study demonstrates the viability and potential of fully automated community ecology and sets the stage for a new era of ecological and evolutionary inquiry driven by artificial intelligence.

ecology↗

bioRxiv2022

Pairing Metagenomics and Metaproteomics to Pinpoint Ecological Niches and Metabolic Essentiality of Microbial Communities

The genome of a microorganism encodes its potential functions that can be implemented through expressed proteins. It remains elusive how a proteins selective expression depends on its metabolic essentiality to microbial growth or its ability to claim resources as ecological niches. To reveal a proteins metabolic or ecological role, we developed a computational pipeline, which pairs metagenomics and metaproteomics data to quantify each proteins gene-level and protein-level functional redundancy simultaneously. We first illustrated the idea behind the pipeline using simulated data of a consumer-resource model. We then validated it using real data from human and mouse gut microbiome samples. In particular, we analyzed ABC-type transporters and ribosomal proteins, confirming that the metabolic and ecological roles predicted by our pipeline agree well with prior knowledge. Finally, we performed in vitro cultures of a human gut microbiome sample and investigated how oversupplying various sugars involved in ecological niches influences the community structure and protein abundance. The presented results demonstrate the performance of our pipeline in identifying proteins metabolic and ecological roles, as well as its potential to help us design nutrient interventions to modulate the human microbiome.

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bioRxiv2023

One marine protected area is not enough: The trophic ecology of the broadnose sevengill shark (Notorynchus cepedianus) in the Southwest Atlantic

O_LIThe broadnose sevengill shark (Notorynchus cepedianus) has been categorized as Vulnerable by the IUCN and shows a declining population trend in the Southwest Atlantic. Bycatch and poaching are the major threats in the region. C_LIO_LIAlthough some ecological requirements have been described, there are still several information gaps regarding its ecology. Important aspects of its trophic ecology, like main prey items or key feeding grounds, remain uncertain and are essential to design effective conservation strategies. C_LIO_LIWe applied stable isotope and stomach content analyses to describe the trophic ecology of sevengill shark within a marine protected area of Peninsula Valdes in Patagonia, Argentina. C_LIO_LIThe stomach content analysis determined the southern elephant seal, Mirounga leonina, as the most frequently regurgitated prey item (70%F) during abundance peaks of both species in Peninsula Valdes. The stable isotope analysis indicated that the overall contribution of the elephant seal to the diet of the sevengill shark was around 30% and that this percentage varied with the size of individuals. C_LIO_LIPresent results strengthen the current understanding of the trophic ecology of the sevengill shark. This study confirmed the use of the marine protected area as an essential foraging ground and identified its main prey items. Also, it reinforced the critical need to expand conservation tools beyond this particular coastal protection. C_LI

ecology↗

bioRxiv2023

Telling mutualistic and antagonistic ecological networks apart by learning their multiscale structure

O_LICharacterizing and understanding the processes that shape the structure of ecological networks, which represent who interacts with whom in a community, has many implications in ecology, evolutionary biology, and conservation. A highly debated question is whether and how the structure of a bipartite ecological network differs between antagonistic (e.g., herbivory) and mutualistic (e.g., pollination) interaction types. C_LIO_LIHere, we tackle this question by using a multiscale characterization of network structure, machine learning tools, and a large database of empirical and simulated bipartite networks. C_LIO_LIContrary to previous studies focusing on global structural metrics, such as nestedness and modularity, which concluded that antagonistic and mutualistic networks cannot be told apart from only their structure, we find that they can be told apart by combining a meso-scale characterization of their structure and supervised machine learning. Motif frequencies appear particularly informative, with an over-representation of densely connected motifs in antagonistic networks and of motifs with asymmetrical specialization in mutualistic networks. These characteristics can be used to predict interaction types with relatively good confidence. Beyond this classical mutualism/antagonism dichotomy, we also find significant structural uniqueness linked to specific ecologies (e.g., pollination, parasitism). C_LIO_LIOur results clarify structural differences between antagonistic and mutualistic networks and suggest the investigation of the structural uniqueness of specific ecologies as a promising approach for characterizing interactions beyond the coarse antagonistic/mutualistic dichotomy. C_LI

ecology↗

bioRxiv2023

The architecture of multifunctional ecological networks

Understanding how biotic interactions affect ecosystem functioning has been a research priority in natural sciences due to their critical role in bolstering ecological resilience1-3. Yet, traditional assessment of ecological complexity typically focus on species-species effective interactions that mediate a particular function (e.g. pollination4 or seed dispersal5), overlooking the synergistic effect of multiple functions that further underpin species-function and function-function interactions in multifunctional ecosystems. At the same time, while ecological network theory holds a potential to quantify the relationship between biodiversity and ecosystem multifunctionality6, 7, its connection has been done mainly conceptually, due to challenges measuring different interactions and establishing their relevance across multiple niche dimensions8, 9. Such lack of quantitative studies therefore limits our ability to determine which species and interactions are important to maintain the multiple functions of ecosystems10. Here we develop a framework -derived from a resource-consumer-function tensor analysis-that bridges these gaps by framing biodiversity-ecosystem multifunctionality in terms of multilayer ecological network theory. Its application to recently collected ecological data -- reporting weighted interactions between plants, animals and fungi across multiple function types--allows to (i) unveil and quantify the existence of both (multi-functional) keystone species and a dual function keystoneness pattern, and (ii) project plants and functions into a similarity space where clear clusters emerge and the importance of weak links is manifested. This dual insight from species and functional perspectives will better guide conservation efforts to reduce biodiversity loss.

ecology↗

bioRxiv2023

Systematics of harvester ants (Messor) in Israel based on integrated morphological, genetic, and ecological data

Harvester ants of the genus Messor are considered ecosystem engineers, whose distribution is broadly influenced by a variety of environmental factors. Although distinct Messor species have been reported to inhabit different habitats, their taxonomy in Israel remains largely ambiguous, hampering the proper ecological characterization of these species. Here, we applied an integrative species delimitation approach combining morphology-based identification, phylogenetic analyses of nuclear and mitochondrial genes, and ecological niche modelling to investigate the phylogenetic relationships among Messor species in the small but ecologically diverse region of Israel. Our analyses of mitochondrial genes revealed the presence of at least 13 well-defined lineages, whereas only seven were supported by the analysis of the nuclear genes. However, the concatenated tree that included all the three markers supported 11 lineages. Among two of the lineages-in M. semirufus and in a group of ants closest in resemblance to M. grandinidus- we identified 3-4 clades that were well established on most trees, inviting further study. In addition, we reveal three undescribed species and raise two subspecies to species rank, highlighting the high diversity of harvester ants in Israel. Ecological niche modelling consistently supported the observed distribution of species, with soil type and average annual temperature being the most influential factors. These results demonstrate that species distribution modelling can serve as a valuable component of integrative species delimitation. We call for future studies to investigate these fascinating lineages of one of the most prominent and ecologically important genera of ants in the Mediterranean Basin.

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bioRxiv2023

Ecological metabolomics of tropical tree communities across an elevational gradient: Implications for chemically-mediated biotic interactions and species diversity

Seminal hypotheses in ecology and evolution postulate that stronger and more specialized biotic interactions contribute to higher species diversity at lower elevations and latitudes. Plant-chemical defenses mediate biotic interactions between plants and their natural enemies and provide a highly dimensional trait space in which chemically mediated niches may facilitate plant species coexistence. However, the role of chemically mediated biotic interactions in shaping plant communities remains largely untested across large-scale ecological gradients. To test this hypothesis, we used ecological metabolomics to quantify the chemical dissimilarity of foliar metabolomes among 473 tree species (906 unique species-plot combinations) in 16 tropical tree communities along an elevational gradient in Madidi National Park, Bolivia. We predicted that chemical dissimilarity among co-occurring tree species would be greater, and chemical phylogenetic signal lower, in communities with greater tree species richness and warmer, wetter, and less-seasonal climates, as pressure from natural enemies is likely to be greater in these locales. Further, we predicted that these relationships should be especially pronounced for secondary metabolites derived from biosynthetic pathways known to include anti-herbivore and antimicrobial defenses than for primary metabolites. We found that median chemical dissimilarity among tree species with respect to all metabolites and secondary metabolites increased with tree species richness, decreased with elevation, and increased along a principal component of climatic variation that reflected increasing temperature and precipitation and decreasing seasonality. In contrast, median chemical dissimilarity among tree species with respect to primary metabolites was unrelated to tree species richness, elevation, or the principal component of climatic variation. Furthermore, phylogenetic signal of secondary and primary metabolites decreased with tree species richness. Among tree communities in moist forests, phylogenetic signal of secondary metabolites also increased with elevation and decreased with the temperature and precipitation. Our results support the hypothesis that chemically mediated biotic interactions shape elevational diversity gradients by imposing stronger selection for interspecific divergence in plant chemical defenses in warmer, wetter, and more stable climates. Our study also illustrates the promise of ecological metabolomics in the study of biogeography, community ecology, and complex species interactions in high-diversity ecosystems.

ecology↗

bioRxiv2023

Transferability of ecological forecasting models to novel biotic conditions in a long-term experimental study

Ecological forecasting models play an increasingly important role for managing natural resources and assessing our fundamental knowledge of processes driving ecological dynamics. As global environmental change pushes ecosystems beyond their historical conditions, the utility of these models may depend on their transferability to novel conditions. Because species interactions can alter resource use, timing of reproduction, and other aspects of a species realized niche, changes in biotic conditions, which can arise from community reorganization events in response to environmental change, have the potential to impact model transferability. Using a long-term experiment on desert rodents, we assessed model transferability under novel biotic conditions to better understand the limitations of ecological forecasting. We show that ecological forecasts can be less accurate when the models generating them are transferred to novel biotic conditions, and that the extent of model transferability can depend on the species being forecast. We also demonstrate the importance of incorporating uncertainty in forecast evaluation with transferred models generating less accurate and more uncertain forecasts. These results suggest that how a species perceives its competitive landscape can influence model transferability, and that when uncertainties are properly accounted for, transferred models may still be appropriate for decision making. Assessing the extent of the transferability of forecasting models is a crucial step to increase our understanding of the limitations of ecological forecasts. Open Research StatementData (10.5281/zenodo.8436468) and code (10.5281/zenodo.11053744) used to conduct data analyses are archived in Zenodo and supplementary results (10.6084/m9.figshare.25733691.v1) in Figshare.

ecology↗