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bioRxiv2015

Radiating despite a lack of character: closely related, morphologically similar, co-occurring honeyeaters have diverged ecologically

The 75 species of Australian honeyeaters (Meliphagidae) are morphologically and ecologically diverse, with species feeding on nectar, insects, fruit, and other resources. We investigated ecomorphology and community structure of honeyeaters across Australia. First, we asked to what degree morphology and ecology (foraging behavior) are concordant. Second, we estimated rates of trait evolution. Third, we compared phylogenetic and trait community structure across the broad environmental gradients of continental Australia. We found that morphology explained 37% of the variance in ecology (and 62% vice versa), and that recovered multivariate ecomorphological relationships incorporated well-known bivariate relationships. Clades of large-bodied species exhibited elevated rates of morphological trait evolution, while members of Melithreptus showed slightly faster rates of ecological trait evolution. Finally, ecological trait diversity did not decline in parallel with phylogenetic diversity along a gradient of decreasing precipitation. We employ a new method (trait fields) and extend another (phylogenetic fields) to show that while species from phylogenetically clustered assemblages co-occur with morphologically similar species, these species are as varied in foraging behavior as those from more diverse assemblages. Thus, although closely related, these arid-adapted species have diverged in ecological space to a similar degree as their mesic counterparts, perhaps mediated by competition.

Ecology↗

bioRxiv2017

Ecological plant epigenetics: Evidence from model and non-model species, and the way forward

Growing evidence makes a strong case that epigenetic mechanisms contribute to complex traits, with implications across many fields of biology from dissecting developmental processes to understanding aspects of human health and disease. In ecology, recent studies have merged ecological experimental design with epigenetic analyses to elucidate the contribution of epigenetics to plant phenotypes, stress response, adaptation to habitat, or species range distributions. While there has been some progress in revealing the role of epigenetics in ecological processes, many studies with non-model species have so far been limited to describing broad patterns based on anonymous markers of DNA methylation. In contrast, studies with model species have benefited from powerful genomic resources, which allow for a more mechanistic understanding but have limited ecological realism. To understand the true significance of epigenetics for plant ecology and evolution, we must combine both approaches transferring knowledge and methods from model-species research to genomes of evolutionarily divergent species, and examining responses to complex natural environments at a more mechanistic level. This requires transforming genomics tools specifically for studying non-model species, which is challenging given the large and often polyploid genomes of plants. Collaboration between molecular epigeneticists, ecologists and bioinformaticians promises to enhance our understanding of the mutual links between genome function and ecological processes.

ecology↗

bioRxiv2019

Influence Of Urbanization On Ecological Status Of River In Amhara Region, Ethiopia 2019

IntroductionUrbanization is one of the main causes for environmental problems due to the introduction of pollutants into water bodies. Lahi is crossing fintoselam. This river has long been used for a variety of purposes including source of public water supply, small scale irrigation, bathing, washing, animal watering, sand and stone dredging and recreation. ObjectiveTo assess the influence of Finoteselam town on ecological status of Lahi river MethodsThe assessment were assessed using physicochemical parameters, physical habitat assessment, biotic indices /metrics, human activity by observation as well as macroinvertabretes samples from eleven sampling sites coded S1 to S11 along the river using the standard procedures. The assessments were involved in-situ measurements and collection of water samples in April 2019 then, analyzed the water samples in laboratory. ResultsThe biological analysis reveals a notable reduction of the diversity macroinvertabrates in the downstream direction where the minimum was at midstream sites. Upstream has significantly better macroinvertabrates assemblage than midstream (p-value<0.05). Physical habitat evaluation scores varied from 43 at S8 to 173 at S1 and relatively lower score were recorded at midstream sites. Low % of Ephemeroptera, Plecoptera and Trichoptera with high family biotic index and high % of Diptera with low biological monitoring working party also indicated water ecology deteriorated mainly at midstream sites. Multivariate analysis of classical analysis, canonical correspondence analysis and none metric multidimensional scaling also indicated ecological deterioration at midstream sites mainly at S5, S6, S7 and S8. ConclusionMidstream was relatively more polluted than upstream due to influence of pollutant from Finoteselam town. The ecological status of upstream segments of the river is very good with slight organic pollutions, and at midstream is poor and significant degree of organic pollutions; whereas the status of the downstream is fair with fairly substantial water pollution showing moderate ecological disturbance. In general, midstream the sampling stations show the deterioration in water ecology and thus necessitated a need for mitigation measure to save the Lahi.

ecology↗

bioRxiv2020

Temporal environmental variation imposes differential selection on genomic and ecological traits of virtual plant communities

The reaction of species to changing conditions determines how community composition will change functionally -- not only by (temporal) species turnover, but also by trait shifts within species. For the latter, selection from standing variation has been suggested to be more efficient than acquiring new mutations. Yet, studies on community trait composition and trait selection largely focus on phenotypic variation in ecological traits, whereas the underlying genomic traits remain relatively understudied despite evidence of their role to standing variation. Using a genome-explicit, niche- and individual-based model, we address the potential interactions between genomic and ecological traits shaping communities under an environmental selective forcing, namely temporal variation. In this model, all ecological traits are explicitly coded by the genome. For our experiments, we initialized 90 replicate communities, each with ca. 350 initial species, characterized by random genomic and ecological trait combinations, on a 2D spatially-explicit landscape with two orthogonal gradients (temperature and resource use). We exposed each community to two contrasting scenarios: without (i.e. static environments) and with temporal variation. We then analyzed emerging compositions of both genomic and ecological traits at the community, population and genomic levels. Communities in variable environments were species poorer than in static environments, populations more abundant and genomes had a higher numbers of genes. The surviving genomes (i.e. those selected by variable environments) coded for enhanced environmental tolerance and smaller biomass, which resulted in faster life cycles and thus also in increased potential for evolutionary rescue. Even under the constant environmental filtering presented by temporal environmental variation, larger, more linked genomes allowed selection of increased variation in dispersal abilities. Our results provide clues to how sexually-reproducing diploid plant communities might react to increased environmental variation and highlights the importance of genomic traits and their interaction with ecological traits for eco-evolutionary responses to changing climates.

ecology↗

bioRxiv2020

Increased microclimatic variation in artificial nests does not create ecological traps for a secondary cavity breeder

Artificial devices are increasingly used in conservation measures to mitigate the disappearance of natural habitats. However, few studies have demonstrated their benefits for the target species, and they may pose a risk of creating ecological traps. This occurs when lower individual fitness is found in artificial habitats that are more attractive than their natural equivalents. In this study, we tested the ecological trap hypothesis on a dense population of European rollers Coracias garrulus breeding in both natural cavities and nest-boxes. Our initial hypothesis was that the more stressful microclimatic conditions of nest-boxes would lead to reduced fitness of rollers, thus creating an ecological trap. The results showed that nest-boxes were preferred over natural cavities. Despite significantly more extreme microclimatic conditions in nest-boxes, we found similar breeding parameters between artificial and natural nest types. Our results also suggest that rollers selected the nest-boxes which best buffered the temperature, thus avoiding potential ecological traps. Overall our results lead to the conclusion that nest-boxes do not create ecological traps for rollers in this study site. However, other species may be more sensitive to microclimatic variations or less able to avoid the least favourable nest-boxes. These findings could help to inform the placement of nest-boxes in order to reduce extreme temperatures and variation in humidity rates. Future studies could compare nest types for other fitness parameters, such as juvenile body condition or survival. We also recommend the ecological trap hypothesis as a useful framework to evaluate the outcomes of artificial devices used for conservation.

ecology↗

bioRxiv2020

Fuzzy Quantification of Common and Rare Species in Ecological Communities (FuzzyQ)

1. Most species in ecological communities are rare whereas only a few are common. This distributional paradox has intrigued ecologists for decades but the interpretation of species abundance distributions remains elusive. 2. We present Fuzzy Quantification of Common and Rare Species in Ecological Communities (FuzzyQ) as an R package. FuzzyQ shifts the focus from the prevailing species-categorization approach to develop a quantitative framework that seeks to place each species along a rare-commonness gradient. Given a community surveyed over a number of sites, quadrats, or any other convenient sampling unit, FuzzyQ uses a fuzzy clustering algorithm that estimates a probability for each species to be common or rare based on abundance-occupancy information. Such as probability can be interpreted as a commonness index ranging from 0 to 1. FuzzyQ also provides community-level metrics about the coherence of the allocation of species into the common and rare clusters that are informative of the nature of the community under study. 3. The functionality of FuzzyQ is shown with two real datasets. We demonstrate how FuzzyQ can effectively be used to monitor and model spatio-temporal changes in species commonness, and assess the impact of species introductions on ecological communities. We also show that the approach works satisfactorily with a wide range of communities varying in species richness, dispersion and abundance currencies. 4. FuzzyQ produces ecological indicators easy to measure and interpret that can give both clear, actionable insights into the nature of ecological communities and provides a powerful way to monitor environmental change on ecosystems. Comparison among communities is greatly facilitated by the fact that the method is relatively independent of the number of sites or sampling units considered. Thus, we consider FuzzyQ as a potentially valuable analytical tool in community ecology and conservation biology.

ecology↗

bioRxiv2020

Missing pieces in the full annual cycle of fish ecology: a systematic review of the phenology of freshwater fish research

In recent decades, fish ecologists have become increasingly aware of the need for spatially comprehensive sampling. However, a corresponding reflection on the temporal aspects of research has been lacking. We quantified the seasonal timing and extent of freshwater fish research. Since reviewing all prior work was not feasible, we considered two different subsets. First, we compiled the last 30 years of ecological research on juvenile Pacific salmon and trout (Oncorhynchus spp.) (n = 371 studies). In addition to the aggregate, we compared groups classified by subject matter. Next, to evaluate whether riverscape ecology has embraced space at the expense of time, we compiled research across taxa in which fish were enumerated in a spatially continuous fashion (n = 46). We found that ecological Oncorhynchus spp. research was biased towards summer (40% occurred during June-August) and the month of June in particular, at the expense of winter work (only 13% occurred during December-February). Riverscape studies were also biased toward summer (47% of studies) and against winter (11%). It was less common for studies to encompass multiple seasons (43% of ecological Oncorhynchus spp. studies and 54% of riverscape studies) and most were shorter than 4 months (73% of ecological Oncorhynchus spp. studies and 81% of riverscape studies). These temporal biases may cause researchers to overemphasize ecological phenomena observed during summer and limit our ability to recognize seasonal interactions such as carry-over effects or compensatory responses. Full year and winter studies likely hold valuable insights for conservation and management.

ecology↗

bioRxiv2021

Computational capability of ecological dynamics

Ecological dynamics is driven by an ecological network consisting of complex interactions. Information processing capability of artificial networks has been exploited as a computational resource, yet whether an ecological network possesses a computational capability and how we can exploit it remain unclear. Here, we show that ecological dynamics can be exploited as a computational resource. We call this approach "Ecological Reservoir Computing" (ERC) and developed two types of ERC. In silico ERC reconstructs ecological dynamics from empirical time series and uses simulated system responses as reservoir states, which predicts near future of chaotic dynamics and emulates nonlinear dynamics. The real-time ERC uses population dynamics of a unicellular organism, Tetrahymena thermophila. Medium temperature is an input signal and changes in population abundance are reservoir states. Intriguingly, the real-time ERC has necessary conditions for reservoir computing and is able to make near future predictions of model and empirical time series.

ecology↗

bioRxiv2021

Linking intrinsic scales of ecological processes to characteristic scales of biodiversity and functioning patterns

Ecology is a science of scale, which guides our description of both ecological processes and patterns, but we lack a systematic understanding of how process scale and pattern scale are connected. Recent calls for a synthesis between population ecology, community ecology, and ecosystem ecology motivate the integration of phenomena at multiple levels of organization. Furthermore, many studies leave out the scaling of a critical process: species interactions, which may be non-local through movement or foraging and must be distinguished from dispersal scales. Here, we use simulations to explore the consequences of three different process scales (species interactions, dispersal, and the environment) on emergent patterns of biodiversity, ecosystem functioning, and their relationship, in a spatially-explicit landscape and stable equilibrium setting. A major result of our study is that the spatial scales of dispersal and species interactions have opposite effects: a larger dispersal scale homogenizes spatial biomass patterns, while a larger interaction scale amplifies their heterogeneity. Interestingly, the specific scale at which dispersal and interaction scales begin to influence landscape patterns depends on the scale of environmental heterogeneity - in other words, the scale of one process allows important scales to emerge in other processes. This interplay between process scales, i.e., a situation where no single process dominates, can only occur when the environment is heterogeneous and the scale of dispersal small. Finally, contrary to our expectations, we observe that the spatial scale of ecological processes is more clearly reflected in landscape patterns (i.e., distribution of local outcomes) than in global patterns such as Species-Area Relationships or large-scale biodiversity-functioning relationships. Overall we conclude that long-range interactions often act differently and even in opposite ways to dispersal, and that the landscape patterns that emerge from the interplay of long-ranged interactions, dispersal and environmental heterogeneity are not well captured by often-used metrics like the Species-Area Relationship.

ecology↗

bioRxiv2023

Detecting and validating influential organisms for rice growth: An ecological network approach

How to achieve sustainable food production while reducing environmental impacts is a major concern in agricultural science, and advanced breeding techniques are promising for achieving such goals. However, rice is usually grown under field conditions and influenced by surrounding ecological community members. How ecological communities influence the rice performance in the field has been underexplored despite the potential of ecological communities to establish an environment-friendly agricultural system. In the present study, we demonstrate an ecological-network-based approach to detect potentially influential, previously overlooked organisms for rice (Oryza sativa). First, we established small experimental rice plots, and measured rice growth and monitored ecological community dynamics intensively and extensively using quantitative environmental DNA metabarcoding in 2017 in Japan. We detected more than 1000 species (including microbes and macrobes such as insects) in the rice plots, and nonlinear time series analysis detected 52 potentially influential organisms with lower-level taxonomic information. The results of the time series analysis were validated under field conditions in 2019 by field manipulation experiments. In 2019, we focused on two species, Globisporangium nunn and Chironomus kiiensis, whose abundance was manipulated in artificial rice plots. The responses of rice, namely, the growth rate and gene expression patterns, were measured before and after the manipulation. We confirmed that, especially in the G. nunn-added treatment, rice growth rate and gene expression pattern were changed. In the present study, we demonstrated that intensive monitoring of an agricultural system and the application of nonlinear time series analysis were helpful to identify influential organisms under field conditions. Although the effects of the manipulations were relatively small, the research framework presented here has future potential to harness the ecological complexity and utilize it in agriculture. Our proof-of-concept study would be an important basis for the further development of field-basis system management.

ecology↗

bioRxiv2024

Ecological lifestyle and gill slit height across sharks

Morphology that is linked to metabolic rate - metabolic morphology - provides broad comparative insights into the physiological performance and ecological function of species. However, some metabolic morphological traits, such as gill surface area, require costly and lethal sampling. Measurements from anatomically-accurate drawings, such as those in field guides, offer the opportunity to understand physiological and ecological relationships without the need for physical, lethal sampling. Here, we assess the relationship between the metabolic physiology and ecology of nearly all extant sharks. Specifically, we examine the relationship between gill slit height and each of the three traits that comprise ecological lifestyle: activity, maximum size, and depth. We find that gill slit heights are positively related to activity (measured by the aspect ratio of the caudal fin) and maximum size but negatively related to depth. We also show that gill slit height is best explained by the suite of ecological lifestyle traits rather than any single trait. These results suggest that more active, larger, and shallower species have higher metabolic demands and that these greater metabolic demands can be estimated from external morphological and ecological traits. Our work demonstrates that meaningful ecophysiological relationships can be revealed through measurable metabolic morphological traits from anatomically-accurate drawings.

ecology↗

bioRxiv2024

Social-ecological models with social hierarchy and spatial structure applied to small scale fisheries

1Socio-ecological models combine ecological systems with human social dynamics in order to better understand human interactions with the environment. To model human behavior, replicator dynamics can be used to model how societal influence and financial costs can change opinions about resource extraction. Previous research on replicator dynamics has shown how evolving opinions on conservation can change how humans interact with their environment and therefore change population dynamics of the harvested species. However, social-ecological models often assume that human societies are homogeneous with no social structure. Building on previous work on social-ecological models, we develop a two-patch socio-ecological model with social hierarchy in order to study the interactions between spatial dynamics an social inequity. We found that fish movement between patches is a major driver of model dynamics, especially when the two patches exhibit different social equality and fishing practices. Further, we found that the societal influence between groups of harvesters was essential to ensuring stable fishery dynamics. Next, we developed a case-study of a co-managed fishery where one group fishes sustainably while another was over-harvests, resulting in a fishery collapse of both patches. We also found that because social influence only included number of fishers and not effective strategies, increased social pressure actually decreased the sustainability of the fishery. The findings of this study indicate the importance of including spatial components to socio-ecological models and highlights the importance of understanding species movements when making conservation decisions. Further, we demonstrate how incorporating fishing methods from outside sources can result in higher stability of the harvested population, indicating a need for diversified information when managing resources.

ecology↗

bioRxiv2024

Causal discovery in biodiversity-mediated social-ecological systems

Global biodiversity loss and climate change exacerbate feedbacks within social-ecological systems, i.e., between ecosystems, their services and well-being of human societies. Our ability to mediate these feedbacks is hampered by incomplete understanding of the underlying causal links, which could benefit from interdisciplinary approaches to discover theoretical or empirical links from heterogeneous data characteristic of social-ecological studies. We propose a novel framework connecting literature-based causal knowledge with data-driven inference of causality. We test this framework for the highly biodiverse island of Borneo by conducting a systematic literature review of 7473 studies over 170 years, and a causal inference analysis for three conceptual causal diagrams connecting global change, socio-economics, ecosystem services, and biodiversity-ecosystem function using a set of 227 spatially explicit variables. We find that, while natural or social processes have been mostly studied independently, a set of studies already documents causal links across social-ecological domains for processes related to deforestation, food or energy. Causal discovery unveiled consistent negative causal links between global change, social-economic landscape, and biodiversity-ecosystem function, and positive causal links between global change and socio-economics, and these links were robust to indicator selection and addition. We detected few and weak links between social-economic landscape, global change, and ecosystem services. When comparing the data-driven inferred causal links to those documented by the literature, we find that links between biodiversity and ecosystem function with global change, and links between social-economic landscape and ecosystem services were also consistent, and causal analysis uncovered new (potential) causal links not yet described in the literature. Significance StatementAddressing climate change and biodiversity loss in the Anthropocene requires us to recognize that human societies and ecological systems are inherently interconnected in complex adaptive systems. Causal understanding in social-ecological systems enables understanding system dynamics and response to pressures and shocks. While promising, few studies have studied these systems using a combination of big literature which provides the state-of-the-knowledge and big data that provides the underlying information for causal discovery. With this framework, we can specify and rigorously test, causal links in biodiversity-mediated social-ecological processes under global change and examine potential interventions that lead to much needed sustainable outcomes.

ecology↗

bioRxiv2025

A theory of ecological invasions and its implications for eco-evolutionary dynamics

Predicting the outcomes of species invasions is a central goal of ecology, a task made especially challenging due to ecological feedbacks. To address this, we develop a general theory of ecological invasions applicable to a wide variety of ecological models: including Lotka-Volterra models, consumer resource models, and models with cross feeding. Importantly, our framework remains valid even when invading evolved (non-random) communities and accounts for invasion-driven species extinctions. We derive analytical expressions relating invasion fitness to invader abundance, shifts in the community, and extinction probabilities. These results can be understood through a new quantity we term "dressed invasion fitness", which augments the traditional notion of invasion fitness by incorporating ecological feedbacks. We apply our theory to analyze short-term evolutionary dynamics through a series of invasions by mutants whose traits are correlated with an existing parent. We demonstrate that, generically, mutants and parents can coexist, often by driving the extinction of low-abundance species. We validate theoretical predictions against experimental datasets spanning ecosystems from plants to microbial protists. Our work highlights the central role of ecological feedbacks in shaping community responses to invasions and mutations, suggesting that parent-mutant coexistence is widespread in eco-evolutionary dynamics.

ecology↗

bioRxiv2025

Ecology and temporal dynamics of urban Drosophila species communities as potential indicators of biodiversity decline

Understanding the impact of ecological factors on biodiversity is central in the context of accelerating climate change and biodiversity loss. Urban areas, as landscapes under particularly strong anthropogenic influence, are undergoing rapid ecological change, yet the consequences for urban biodiversity and ecosystem functioning remain poorly understood. In this study, we focused on fruit flies of the genus Drosophila - a diverse group of dipterans with variable ecological niches and degrees of synanthropy - to investigate species composition and community ecology in the metropolitan area of Vienna, Austria. With the help of numerous citizen scientists, we have collected approximately 18,000 specimens through dense spatio-temporal sampling both indoors and outdoors of human dwellings. A total of 13 Drosophila species were identified, with communities dominated by widespread cosmopolitan synanthropic species. Among these, D. mercatorum and D. virilis represent novel records for Austria. Comparisons to a previous study from more than 30 years ago revealed that the species richness in Vienna was more than 50% lower than before and showed that formerly common species were potentially replaced by neobiots. We further assessed ecological niches by intersecting species abundance data with high-dimensional, high-resolution earth observation datasets, which revealed distinct ecological preferences among species. In particular, the neozoan D. mercatorum emerged as a highly synanthropic species, tightly confined to urban areas with high levels of imperviousness. In summary, our study underpins the versatility of the Drosophila system as indicators of biodiversity loss in a rapidly changing world.

ecology↗

bioRxiv2018

Species-habitat networks: Bridging applied ecology and network theory

Land-use change is massively reshaping terrestrial ecosystems worldwide, and is recognized as a key driver of biodiversity loss with negative consequences on ecosystem functioning. Understanding how species use resources across landscapes is essential for the design of effective management strategies. Despite recent advances in theoretical ecology, there is still a gap between theory and applied ecological science and we lack the tools to manage entire landscapes to maximize biodiversity conservation and ecosystem service delivery. Here, we propose a new approach that uses existing bipartite networks to create species-habitat networks. Networks enable powerful visualizations via a common language that defines most processes in terms of nodes and links. This approach explicitly links multiple species and habitat resources, provides tools to estimate the importance of particular species in a given landscape, and quantifies emerging properties of entire habitat networks. Most existing metrics used to study properties of bipartite ecological networks can easily be adapted to investigate species-habitat relationships. One key advantage of this approach is that the scale of the derived ecological information will match the scale of management interventions. The flexibility of the proposed approach is that it can be easily applied across a range of ecological fields such as species conservation, habitat restoration, ecosystem services management, or invasion ecology. Network emerging properties could also be used to test the effects of large scale drivers of global change upon ecosystem structure and stability.

ecology↗

bioRxiv2018

Applications for deep learning in ecology

A lot of hype has recently been generated around deep learning, a group of artificial intelligence approaches able to break accuracy records in pattern recognition. Over the course of just a few years, deep learning revolutionized several research fields such as bioinformatics or medicine. Yet such a surge of tools and knowledge is still in its infancy in ecology despite the ever-growing size and the complexity of ecological datasets. Here we performed a literature review of deep learning implementations in ecology to identify its benefits in most ecological disciplines, even in applied ecology, up to decision makers and conservationists alike. We also provide guidelines on useful resources and recommendations for ecologists to start adding deep learning to their toolkit. At a time when automatic monitoring of populations and ecosystems generates a vast amount of data that cannot be processed by humans anymore, deep learning could become a necessity in ecology.

ecology↗

bioRxiv2018

Measuring popularity of ecological topics in a temporal dynamical knowledge network

As interdisciplinary branches of ecology are developing rapidly in the 21st century, contents of ecological researches have become more abundant than ever before. Along with the exponential growth of number of published literature, it is more and more difficult for ecologists to get a clear picture of their discipline. Nevertheless, the era of big data has brought us massive information of well documented historical literature and various techniques of data processing, which greatly facilitates the implementation of bibliometric analysis on ecology. Frequency has long been used as the primary metric in keyword analysis to detect ecological hotspots, however, this method could be somewhat biased. In our study, we have suggested a method called PAFit to measure keyword popularity, which considered ecology-related topics in a large temporal dynamical knowledge network, and found out the popularity of ecological topics follows the \"rich get richer\" and \"fit get richer\" mechanism. Feasibility of network analysis and its superiority over simply using frequency had been explored and justified, and PAFit was testified by its outstanding performance of prediction on the growth of frequency and degree. In addition, our research also encourages ecologists to consider their domain knowledge in a large dynamical network, and be ready to participate in interdisciplinary collaborations when necessary.

ecology↗