SEARCH · bioRxiv Science

Results for “ecology”

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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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

At least 145 records · Page 8Linked to original sources

bioRxiv2019

Complex ecological phenotypes on phylogenetic trees: a hidden Markov model for comparative analysis of multivariate count data

The evolutionary dynamics of complex ecological traits - including multistate representations of diet, habitat, and behavior - remain poorly understood. Reconstructing the tempo, mode, and historical sequence of transitions involving such traits poses many challenges for comparative biologists, owing to their multidimensional nature and intraspecific variability. Continuous-time Markov chains (CTMC) are commonly used to model ecological niche evolution on phylogenetic trees but are limited by the assumption that taxa are monomorphic and that states are univariate categorical variables. Thus, a necessary first step when using standard CTMC models is to categorize species into a pre-determined number of ecological states. This approach potentially confounds interpretation of state assignments with effects of sampling variation because it does not directly incorporate empirical observations of resource use into the statistical inference model. The neglect of sampling variation, along with univariate representations of true multivariate phenotypes, potentially leads to the distortion and loss of information, with substantial implications for downstream macroevolutionary analyses. In this study, we develop a hidden Markov model using a Dirichlet-multinomial framework to model resource use evolution on phylogenetic trees. Unlike existing CTMC implementations, states are unobserved probability distributions from which observed data are sampled. Our approach is expressly designed to model ecological traits that are intra-specifically variable and to account for uncertainty in state assignments of terminal taxa arising from effects of sampling variation. The method uses multivariate count data for individual species to simultaneously infer the number of ecological states, the proportional utilization of different resources by different states, and the phylogenetic distribution of ecological states among living species and their ancestors. The method is general and may be applied to any data expressible as a set of observational counts from different categories.

evolutionary biology↗

bioRxiv2019

Ecological divergence in sympatry causes gene misregulation in hybrids

Ecological speciation occurs when reproductive isolation evolves as a byproduct of adaptive divergence between populations. However, it is unknown whether divergent ecological selection on gene regulation can directly cause reproductive isolation. Selection favoring regulatory divergence between species could result in gene misregulation in F1 hybrids and ultimately lower hybrid fitness. We combined 58 resequenced genomes with 124 transcriptomes to test this hypothesis in a young, sympatric radiation of Cyprinodon pupfishes endemic to San Salvador Island, Bahamas, which consists of a dietary generalist and two novel trophic specialists - a molluscivore and a scale-eater. We found more differential gene expression between closely related sympatric specialists than between allopatric generalist populations separated by 1000 km. Intriguingly, 9.6% of genes that were differentially expressed between sympatric species were also misregulated in their F1 hybrids. Consistent with divergent ecological selection causing misregulation, a subset of these genes were in highly differentiated genomic regions and enriched for functions important for trophic specialization, including head, muscle, and brain development. These regions also included genes that showed evidence of hard selective sweeps and were significantly associated with oral jaw length - the most rapidly diversifying skeletal trait in this radiation. Our results indicate that divergent ecological selection in sympatry can cause hybrid gene misregulation which may act as a primary reproductive barrier between nascent species.\n\nSignificanceIt is unknown whether the same genes that regulate ecological traits can simultaneously contribute to reproductive barriers between species. We measured gene expression in two trophic specialist species of Cyprinodon pupfishes that rapidly diverged from a generalist ancestor. We found genes differentially expressed between species that also showed extreme expression levels in their hybrid offspring. Many of these genes showed signs of selection and have putative effects on the development of traits that are important for ecological specialization. This suggests that genetic variants contributing to adaptive trait divergence between parental species negatively interact to cause hybrid gene misregulation, potentially producing unfit hybrids. Such loci may be important barriers to gene flow during the early stages of speciation, even in sympatry.

evolutionary biology↗

bioRxiv2026

Ecological Interactions Drive Metabolomic Diversification in Amazonian Pseudonocardia Symbionts

Fungus-growing ants engage in a multipartite symbiosis, including Pseudonocardia bacteria that produce antifungal metabolites to protect their fungal cultivar from the specialized pathogen Escovopsis. While different bioactive metabolites have been reported from ant-associated Pseudonocardia, most studies have focused on a limited number of strains, leaving the extent of chemical diversity across broader ecological contexts less resolved. Here, we investigated the antagonistic potential and metabolomic repertoires of 36 Pseudonocardia strains isolated from Amazonian Paratrachymyrmex ants. Pairwise bioactivity assays against two Escovopsis isolates revealed striking variability, with inhibition generally stronger and more diverse against the pathogenic fungus originating from the same ant genus. Untargeted LC-MS/MS metabolomics coupled with 16S rRNA-based phylogenetic analyses showed that closely related strains harbored highly divergent chemical profiles, underscoring a decoupling between taxonomy and metabolite output. Detailed analyses of selected isolates revealed the production of structurally diverse metabolites, including dentigerumycin analogs, provipeptide A, {beta}-carbolines, and tetracycline-related compounds. Co-culture analysis uncovered metabolites absent in monocultures, including lichenysins, pepstatins, and hallobacillins, as well as conserved attinimicin, whose production was enhanced under pathogen challenge. These results highlight that both strain-specific metabolic repertoires and interaction-induced chemistry contribute to the defensive arsenal of Pseudonocardia. Together, our findings likely demonstrate that ecological pressures and local adaptation, rather than phylogeny alone, drive metabolomic diversification in this defensive symbiosis. Beyond their potential for novel bioactive compound discovery, these results provide insights into the chemical basis of multipartite symbioses, the dynamics of defensive mutualisms, and the ecological forces shaping microbial diversity in underexplored environments such as the Amazon. IMPORTANCEMicrobial symbionts are central to host defense and natural product discovery, yet the factors driving their chemical diversification remain unclear. The fungus-growing ant-Pseudonocardia-Escovopsis system offers a powerful model to study how ecological context shapes microbial metabolism. By systematically characterizing multiple Amazonian Pseudonocardia strains, we show that antagonistic capacity and metabolomic repertoires vary widely, even among strains with highly similar 16S rRNA gene sequences, revealing a pronounced discordance between 16S-based phylogenetic relatedness and specialized metabolite production. These findings highlight the likely importance of ecological pressures and local adaptation in shaping metabolomic output, emphasizing symbiotic actinobacteria as both key ecological players and promising sources of antifungal natural products.

microbiology↗

bioRxiv2026

Paired oral clinical specimens reveal the underlying ecology supporting the emergence of inflammophilic microbiome communities

BackgroundInflammatory oral diseases are associated with reproducible shifts from commensal-dominated microbiota toward pathobiont-enriched communities, yet the ecological mechanisms underlying the emergence of inflammophiles remain poorly understood. This study aims to investigate if host-derived inflammatory environments act as selective pressures that restructure microbial metabolism and community organization during disease progression. MethodsWe performed 16S rRNA gene sequencing of patient-matched pediatric dental plaque and odontogenic abscess specimens to capture microbial community transitions across an inflammatory ecological gradient. Community ecology modeling and inferred metagenomic analyses were used to identify taxa and functional programs associated with commensal and inflammophilic states. ResultsPatient-matched comparisons revealed a reproducible ecological selection gradient linking inflammatory environments to expansion of metabolically specialized inflammophiles and depletion of carbohydrate-utilizing commensals. Commensal-dominated plaque communities exhibited anabolic, carbohydrate-centered metabolic capacity, whereas abscess microbiota were enriched for catabolic metabolism, amino acid fermentation, and antimicrobial resistance, consistent with adaptation to inflammation-driven nutrient landscapes and immune pressure. ConclusionsThese findings support a model in which host inflammation drives ecological restructuring of the oral microbiome toward metabolically adapted inflammophilic communities. Defining the metabolic requirements and selective pressures governing these transitions provides a framework for microbiome-directed therapeutic strategies aimed at restoring ecological stability during inflammatory dysbiosis.

bioinformatics↗

bioRxiv2026

Adaptive coupling of chromosomal inversions with multilocus ecological, mating-bias, and hybrid-incompatibility genotypes facilitates sympatric speciation

Chromosomal inversions (CIs) are widespread structural variants that suppress recombination and maintain favorable allele combinations as tightly linked supergenes. They are increasingly recognized for their contributions to local adaptation and speciation, yet their role in sympatric speciation under ongoing gene flow remains unresolved. Here, we develop mathematical models and computer simulations to examine how polygenic architectures and CI invasion jointly shape the evolution of reproductive isolation (RI) in a two-niche model under disruptive ecological selection. Extending previous frameworks, we incorporate multilocus ecological traits, multilocus mating-bias traits, and multilocus hybrid-incompatibility genotypes, and evaluate the invasion fitness and evolutionary consequences of CIs capturing different combinations of locally adaptive alleles. We show that the evolutionary impact of CI invasion depends critically on how polygenic trait structure influences hybrid production and the strength of barrier mechanisms. Increasing the number of ecological or hybrid-incompatibility loci strengthens disruptive ecological selection and postzygotic incompatibility selection by generating more unfit hybrids, whereas increasing the number of mating-bias loci weakens premating selection because mating-bias hybrids remain viable within the same niche. Accordingly, CIs capturing ecological or hybrid-incompatibility alleles tend to reduce the effective number of loci, diminish hybrid loss, and weaken existing RI, whereas CIs capturing mating-bias alleles strengthen premating isolation by reducing the effective number of mating-bias loci. Importantly, CIs that couple alleles across distinct barrier mechanisms exhibit elevated invasion fitness and generate synergistic reinforcement and positive feedback among premating isolation, postmating isolation, and ecological divergence. These findings reconcile contrasting theoretical predictions by demonstrating that CIs can either facilitate or constrain sympatric speciation depending on how they reshape effective locus number and barrier coupling, and provide a unified framework for understanding how structural genomic variants interact with polygenic architectures to influence the origin and stability of reproductive isolation.

evolutionary biology↗

bioRxiv2026

Emergent frequency-dependent selection predicts mutation outcomes in complex ecological communities

Ecological interactions can dramatically alter evolutionary outcomes in complex communities. Yet, the framework of population genetics largely neglects interactions from a species-rich community. Here, we bridge this gap by using dynamical mean-field theory to integrate community ecology into classical population genetics models. We show that ecological interactions result in emergent frequency-dependent selection between parents and mutants, characterized by a single parameter measuring the strength of ecological feedbacks. This result generalizes classical population genetics models to highly diverse communities and enables predictions of mutation outcomes in these eco-evolutionary settings. We derive an analytic expression for fixation probability that extends Kimuras formula and reveals that ecological interactions strongly suppress the fixation of moderately beneficial mutations. This suppression arises because frequency-dependent selection leads to prolonged coexistence between parent and mutant lineages, which acts as a barrier to fixation. The strength of these effects increases with effective population size and the number of open niches in the ecosystem. Our study establishes a framework for integrating ecological interactions into population genetics, showing that evolutionary outcomes can be predicted using simple models even in the presence of complex community feedbacks.

evolutionary biology↗

bioRxiv2015

Elevating the status of code in ecology

Introduction Introduction The role of code... Current standards for code... Promoting code in ecology... References Code is increasingly central to research in ecology. From running statistical analyses for field and lab projects to developing software intended to be used broadly for modeling and data analysis, most ecologists now commonly write code as part of their research. It is important that the communication of research results addresses the fact that code is now a key component of most ecological studies.\n\nThe role of code in ecological research Introduction The role of code... Current standards for code... Promoting code in ecology... References The transition to a greate ...

Ecology↗

bioRxiv2016

How ecological networks evolve

Ecological networks represent the backbone of biodiversity. As species diversify over macro-evolutionary time-scales, the structure of these networks changes; this happens because species are gained and lost, and therefore add or remove interactions in their communities. The mechanisms underlying such dynamic changes in ecological network structure, however, remain poorly understood. Here we show that several types of ecological interactions share common evolutionary mechanisms that can be parametrised based on extant interaction data. In particular, we found that a model mimicking birth-death processes for species interactions describes the structure of extant networks remarkably well. Moreover, the various types of ecological interactions we considered--seed dispersal, herbivory, parasitism, bacteriophagy, and pollination--only differed in the position they occupy in the parameters multi-dimensional space. Notably, we found no clustering of parameters values between antagonistic and mutualistic interactions. Our results provide a common modelling framework for the evolution of ecological networks that we anticipate will contribute to the greater consideration of the explicit role played by species interactions in models of macro-evolution and adaptive radiations.

Ecology↗

bioRxiv2017

Consensus and conflict among ecological forecasts of Zika virus outbreaks in the United States

Ecologists are increasingly involved in the pandemic prediction process. In the course of the Zika outbreak in the Americas, several ecological models were developed to forecast the potential global distribution of the disease. Conflicting results produced by alternative methods are unresolved, hindering the development of appropriate public health forecasts. We compare ecological niche models and experimentally-driven mechanistic forecasts for Zika transmission in the continental United States, a region of high model conflict. We use generic and uninformed stochastic county-level simulations to demonstrate the downstream epidemiological consequences of conflict among ecological models, and show how assumptions and parameterization in the ecological and epidemiological models propagate uncertainty and produce downstream model conflict. We conclude by proposing a basic consensus method that could resolve conflicting models of potential outbreak geography and seasonality. Our results illustrate the unacceptable and often undocumented margin of uncertainty that could emerge from using any one of these predictions without reservation or qualification. In the short term, ecologists face the task of developing better post hoc consensus that accurately forecasts spatial patterns of Zika virus outbreaks. Ultimately, methods are needed that bridge the gap between ecological and epidemiological approaches to predicting transmission and realistically capture both outbreak size and geography.

ecology↗

bioRxiv2017

Mapping the ecological networks of microbial communities from steady-state data

Microbes form complex and dynamic ecosystems that play key roles in the health of the animals and plants with which they are associated. Such ecosystems are often represented by a directed, signed and weighted ecological network, where nodes represent microbial taxa and edges represent ecological interactions. Inferring the underlying ecological networks of microbial communities is a necessary step towards understanding their assembly rules and predicting their dynamical response to external stimuli. However, current methods for inferring such networks require assuming a particular population dynamics model, which is typically not known a priori. Moreover, those methods require fitting longitudinal abundance data, which is not readily available, and often does not contain the variation that is necessary for reliable inference. To overcome these limitations, here we develop a new method to map the ecological networks of microbial communities using steady-state data. Our method can qualitatively infer the inter-taxa interaction types or signs (positive, negative or neutral) without assuming any particular population dynamics model. Additionally, when the population dynamics is assumed to follow the classic Generalized Lotka-Volterra model, our method can quantitatively infer the inter-taxa interaction strengths and intrinsic growth rates. We systematically validate our method using simulated data, and then apply it to four experimental datasets of microbial communities. Our method offers a novel framework to infer microbial interactions and reconstruct ecological networks, and represents a key step towards reliable modeling of complex, real-world microbial communities, such as the human gut microbiota.

ecology↗

bioRxiv2017

Extinction models of robustness for weighted ecological networks

O_LIAnalysis of ecological networks is a valuable approach to understanding the vulnerability of systems to environmental change. The tolerance of ecological networks to co-extinctions, resulting from sequences of primary extinctions, is a widely-used tool for modelling network robustness. Previously, these extinction models have been developed for and applied mostly to binary networks and recently used to predict cascades of co-extinctions in plant-pollinator networks. There is a need for robustness models that can make the most of the weighted data available and most importantly there is a need to understand how the structure of a network affects its robustness.\nC_LIO_LIHere, we developed a framework of extinction models for bipartite ecological networks (specifically plant-pollinator networks). In previous models co-extinctions occurred when nodes lost all their links, but by relaxing this rule (according to a set threshold) our models can be applied to binary and weighted networks, and can permit structurally correlated extinctions, i.e. the potential for avalanches of extinctions. We tested how the average and the range of robustness values is impacted by network structure and the impact of structurally-correlated extinctions sampling non-uniformly from the distribution of random extinction sequences.\nC_LIO_LIWe found that the way that structurally-correlated extinctions are modelled impacts the results; our two ecologically-plausible models produce opposing effects which shows the importance of understanding the model. We found that when applying the models to networks with weighted interactions, the effects are amplified and the variation in robustness increases. Variation in robustness is a key feature of these extinction models and is driven by the structural heterogeneity (i.e. the skewness of the degree distribution) of nodes (specifically, plant nodes) in the network.\nC_LIO_LIOur new framework of models enables us to calculate robustness with weighted, as well as binary, bipartite networks, and to make direct comparisons between models and between networks. This allows us to differentiate effects of the model and of the data (network structure) which is vital for those making ecological inferences from robustness models. The models can be applied to mutualistic and antagonistic networks, and can be extended to food webs.\nC_LI

ecology↗

bioRxiv2020

Unification of Environmental Metabolomics with Metacommunity Ecology

Environmental metabolomics, enabled by high-resolution mass spectrometric techniques, have demonstrated the biogeochemical importance of the metabolites which comprise natural organic matter (NOM). However, significant gaps exist in our understanding of the spatiotemporal organization of NOM composition. We suggest that the underlying mechanisms governing NOM can be revealed by applying tools and concepts from metacommunity ecology to environmental metabolomics. After illustrating the similarities between metabolomes and ecological communities, we call this conceptual synthesis meta-metabolome ecology and demonstrate its potential utility using a freshwater mass spectrometry dataset. Specifically, we developed three relational metabolite dendrograms using combinations of molecular properties (i.e., aromaticity index, double-bond equivalents, etc.) and putative biochemical transformations. Using these dendrograms, which are similar to phylogenetic or functional trait trees in ecological communities, we illustrate potential analytical techniques by investigating relationally-informed -diversity and {beta}-diversity metrics (e.g., MPD, MNTD, UniFrac), and null model analyses (e.g., NRI, NTI, and {beta}NTI). Furthermore, we demonstrate that this synthesis allows ecological communities (e.g., microbes) and the metabolites they produce and consume using the same framework. We propose that applying this framework to a broad range of ecosystems will reveal generalizable principles that can advance our predictive capabilities regarding NOM dynamics.

ecology↗

bioRxiv2020

An algorithm for quantifying and characterizing misleading trajectories in ecological processes

A core issue in temporal ecology is the concept of trajectory--that is, when can ecologists have reasonable assurance that they know where a system is going? In this paper, we describe a non-random resampling method to directly address the temporal aspects of scaling ecological observations by leveraging existing data. Findings from long-term research sites have been hugely influential in ecology because of their unprecedented longitudinal perspective, yet short-term studies more consistent with typical grant cycles and graduate programs are still the norm. We use long-term insights to create broken windows, that is, reanalyze long-term studies from short-term observational perspectives to examine discontinuities in trends at differing temporal scales. The broken window algorithm connects our observations between the short-term and the long-term with an automated, systematic resampling approach: in short, we repeatedly sample moving windows of data from existing long-term time series, and analyze these sampled data as if they represented the entire dataset. We then compile typical statistics used to describe the relationship in the sampled data, through repeated samplings, and then use these derived data to gain insights to the questions: 1) how often are the trends observed in short-term data misleading, and 2) can characteristics of these trends be used to predict our likelihood of being misled? We develop a systematic resampling approach, the broken_window algorithm, and illustrate its utility with a case study of firefly observations produced at the Kellogg Biological Station Long-Term Ecological Research Site (KBS LTER). Through a variety of visualizations, summary statistics, and downstream analyses, we provide a standardized approach to evaluating the trajectory of a system, the amount of observation required to find a meaningful trajectory in similar systems, and a means of evaluating our confidence in our conclusions. HighlightsTrends identified in short-term ecology studies can be misleading. Non-random resampling can show how prone different systems are to misleading trends The Broken Window algorithm is a new tool to help synthesize temporal data This tool helps to understand how much data is needed for forecasting to be reliable It can also be used to quantify how likely it is that an observed trend is spurious.

ecology↗

bioRxiv2020

Ecological theory predicts ecosystem stressor interactions in freshwater ecosystems, but highlights the strengths and weaknesses of the additive null model

Understanding and predicting how multiple co-occurring environmental stressors combine to affect biodiversity and ecosystem services is an on-going grand challenge for ecology. So far progress has been made through accumulating large numbers of smaller-scale individual studies that are then investigated by meta-analyses to look for general patterns. In particular there has been an interest in checking for so-called ecological surprises where stressors interact in a synergistic manner. Recent reviews suggest that such synergisms do not dominate, but few other generalities have emerged. This lack of general prediction and understanding may be due in part to a dearth of ecological theory that can generate clear hypotheses and predictions to tested against empirical data. Here we close this gap by analysing food web models based upon classical ecological theory and comparing their predictions to a large (546 interactions) dataset for the effects of pairs of stressors on freshwater communities, using trophic- and population-level metrics of abundance, density, and biomass as responses. We find excellent overall agreement between the stochastic version of our models and the experimental data, and both conclude additive stressor interactions are the most frequent, but that meta-analyses report antagonistic summary interaction classes. Additionally, we show that the statistical tests used to classify the interactions are very sensitive to sampling variation. It is therefore likely that current weak sampling and low sample sizes are masking many non-additive stressor interactions, which our theory predicts to dominate when sampling variation is removed. This leads us to suspect ecological surprises may be more common than currently reported. Our results highlight the value of developing theory in tandem with empirical tests, and the need to examine the robustness of statistical machinery, especially the widely-used null models, before we can draw strong conclusions about how environmental drivers combine.

ecology↗

bioRxiv2020

Tractable models of ecological assembly

Ecological assembly, the way natural communities form under ecological time-scales, is a fundamental and yet poorly understood process. Recent theoretical and empirical approaches to assembly consider systems in which a group of species is introduced in a new environment, and dynamics prune the system down to a sub-community of coexisting species. This "top-down" assembly approach contrasts with the well-studied "bottom-up", or sequential, assembly, in which species from a pool enter the system one at a time, giving rise to priority effects and complex dynamics. Here we determine under which conditions the two approaches are equivalent, i.e., lead asymptotically to the same exact set of coexisting species. To achieve this result, we represent the assembly process as a network in which nodes are sub-communities and edges stand for invasions shifting the composition of the ecological community from a stable configuration to another. This abstraction makes it easy to determine which states the community can occupy, as well as highlight the potential for priority effects or cyclic species composition. We discuss how the equivalence between bottom-up and top-down assembly can advance our understanding of this challenging process from an empirical and theoretical point of view, informing the study of ecological restoration and the design and control of ecological communities.

ecology↗

bioRxiv2020

Deep learning for time series classification in ecology

Temporal data is ubiquitous in ecology and ecologists often face the challenge of accurately differentiating these data into predefined classes, such as biological entities or ecological states. The usual approach consists of transforming the time series into user-defined features and then using these features as predictors in conventional statistical or machine learning models. Here we suggest the use of deep learning models as an alternative to this approach. Recent deep learning techniques can perform the classification directly from the time series, eliminating subjective and resource-consuming data transformation steps, and potentially improving classification results. We describe some of the deep learning architectures relevant for time series classification and show how these architectures and their hyper-parameters can be tested and used for the classification problems at hand. We illustrate the approach using three case studies from distinct ecological subdisciplines: i) insect species identification from wingbeat spectrograms; ii) species distribution modelling from climate time series and iii) the classification of phenological phases from continuous meteorological data. The deep learning approach delivered ecologically sensible and accurate classifications demonstrating its potential for wide applicability across subfields of ecology.

ecology↗

bioRxiv2020

Trends in plant ecology research in Ethiopia (1969-2019): Systematic analysis

The objective of this paper was to systematically analyze the trend of plant ecological research in Ethiopia. The inclusion and exclusion of the articles for analysis were performed using Reporting Standards for Systematic Evidence Syntheses (ROSES) flow diagram developed for systematic review/meta-analysis. The number of articles published, authors, and collaboration has increased dramatically since the 1960s. Most of the research largely focused on the Dry evergreen Afromontane Forest and grassland complex (DAF) and Moist evergreen Afromontane Forest (MAF) vegetation types, comprising of about 52.6%. Of the remaining vegetation types, the woodlands (14.3%) i.e. Acacia-Commiphora woodland and bushland proper (ACW), and Combretum-Terminalia woodland and wooded grassland (CTW), desert and semi-desert scrubland (DSS) (2.3%), and the Afroalpine (AA) and Ericaceous Belt (EB) (1.5%) received little attention. A descriptive study on plant community ecology revolving on floristic composition and community structure is the dominant research theme, which revealed a narrow theme in contrast to the global trend. Other plant ecological studies such as reproductive and dispersal ecology of invasive plant species, and pollination ecology seems largely a neglected topic by the academia. Furthermore, the recommendations forwarded are not result-based. As a future direction, the Ethiopian government should develop a project database both for completed and ongoing projects.

ecology↗

bioRxiv2020

Adaptive management plans rooted in quantitative ecological predictions of ecosystem processes: putting monitoring data to practical use

It is demonstrated how a hierarchical structural equation model that is fitted to temporal ecological monitoring data from a number of sites may be used to generate local ecological predictions and how these local ecological predictions may form the basis of adaptive management plans. Local ecological predictions will be made for the cover of cross-leaved heath on Danish wet heathlands, which is one of the indicators that determine the conservation status of wet heathlands under different management scenarios. Based on a realistic example, the model predictions concludes that grazing by domestic herbivores on wet heathlands with a relatively low cover cross-leaved heath cannot be recommended as the only management practice. Generally, it is recommended to use ecological monitoring data to generate quantitative and credible local adaptive management plans where the uncertainty is taken into account.

ecology↗