Hasil untuk "Ecology"

C. Violle, B. Enquist, B. McGill et al.

C. Tucker, M. Cadotte, S. Carvalho et al.

The use of phylogenies in ecology is increasingly common and has broadened our understanding of biological diversity. Ecological sub‐disciplines, particularly conservation, community ecology and macroecology, all recognize the value of evolutionary relationships but the resulting development of phylogenetic approaches has led to a proliferation of phylogenetic diversity metrics. The use of many metrics across the sub‐disciplines hampers potential meta‐analyses, syntheses, and generalizations of existing results. Further, there is no guide for selecting the appropriate metric for a given question, and different metrics are frequently used to address similar questions. To improve the choice, application, and interpretation of phylo‐diversity metrics, we organize existing metrics by expanding on a unifying framework for phylogenetic information.

A. Ramette

Environmental microbiology is undergoing a dramatic revolution due to the increasing accumulation of biological information and contextual environmental parameters. This will not only enable a better identification of diversity patterns, but will also shed more light on the associated environmental conditions, spatial locations, and seasonal fluctuations, which could explain such patterns. Complex ecological questions may now be addressed using multivariate statistical analyses, which represent a vast potential of techniques that are still underexploited. Here, well-established exploratory and hypothesis-driven approaches are reviewed, so as to foster their addition to the microbial ecologist toolbox. Because such tools aim at reducing data set complexity, at identifying major patterns and putative causal factors, they will certainly find many applications in microbial ecology.

Jan M. Ivery

Tim Clutton-Brock, F. Guinness, S. Albon

H. Godfray

C. Merchant

J. Murray

E. Armbrust, J. Berges, C. Bowler et al.

Richard S. Miller

L. Holdridge

M. Usher, George Oster, Edward O. Wilson

M. Weinbauer

D. Savage

T. Quinn

T. Dawson, S. Mambelli, A. Plamboeck et al.

R. Boelens, Jaime Hoogesteger, E. Swyngedouw et al.

Michael B. Bonsall, Claire A. Dooley

We document the impact of blood parasite infections caused by Hepatozoon sp. on water python (Liasis fuscus) life history traits such as growth rates, condition, reproductive output and survival. Individual snakes maintained similar among-year parasite loads. Hepatozoon infections affected python growth rate, i.e. snakes suffering from high infection levels exhibited significantly slower growth compared to individuals with low parasite loads. Our results suggest that the parasites also affected the pythons’ nutritional status (condition), as snakes with low condition scores suffered from higher parasite infection levels than snakes with high scores. Furthermore, our data suggest that parasitaemia may affect female reproductive output, as reproductive female pythons harboured lower parasite loads compared to non-reproductive adult females. High levels of parasite infections also affected juvenile python survival, as recaptured snakes harboured significantly lower parasite loads compared to non-recaptured yearling pythons. In our study area, water python have very few natural predators and, hence, experience low mortality rates and commonly reach an age of >15 years. In contrast to results obtained in other studies, parasite loads in larger/ older pythons were lower compared to younger snakes, suggesting that only snakes harbouring lower levels of parasitaemia were able to survive to old age. We suggest that a possible cause for the opposing results regarding parasite prevalence and host age may be due to different levels of extrinsic mortality rates and longevity. Longlived organisms, such as water pythons, may invest relatively more into crucial self-maintenance functions such as parasite defence, compared to short-lived organisms.

K. Van Meerbeek, T. Jucker, Jens‐Christian Svenning

Characterizing how ecosystems are responding to rapid environmental change has become a major focus of ecological research. The empirical study of ecological stability, which aims to quantify these ecosystem responses, is therefore more relevant than ever. Based on a historical review and bibliometric mapping of the field of ecological stability, we show that the two main schools relating to the study of stability—one focusing on systems close to their equilibrium and the other on non‐equilibrium behaviour—have developed in parallel leading to divergence in both concepts and definitions. We synthesize and expand previous frameworks and capitalize on the latest developments in the field to build towards an integrated framework by elaborating the overarching concept of ecological stability and its properties. Finally, the broad applicability of our work is demonstrated in two empirical cases. Synthesis. With rapidly changing environmental conditions, the stability of ecosystems has become a major focus of ecological research. Still, the concept of stability remains a major source of confusion and disagreement among ecologists. The conceptual framework presented here provides a basis to integrate currently diverging views on the study of ecological stability.

M. Borowiec, P. Frandsen, R. Dikow et al.

Deep learning is driving recent advances behind many everyday technologies, including speech and image recognition, natural language processing and autonomous driving. It is also gaining popularity in biology, where it has been used for automated species identification, environmental monitoring, ecological modelling, behavioural studies, DNA sequencing and population genetics and phylogenetics, among other applications. Deep learning relies on artificial neural networks for predictive modelling and excels at recognizing complex patterns. In this review we synthesize 818 studies using deep learning in the context of ecology and evolution to give a discipline‐wide perspective necessary to promote a rethinking of inference approaches in the field. We provide an introduction to machine learning and contrast it with mechanistic inference, followed by a gentle primer on deep learning. We review the applications of deep learning in ecology and evolution and discuss its limitations and efforts to overcome them. We also provide a practical primer for biologists interested in including deep learning in their toolkit and identify its possible future applications. We find that deep learning is being rapidly adopted in ecology and evolution, with 589 studies (64%) published since the beginning of 2019. Most use convolutional neural networks (496 studies) and supervised learning for image identification but also for tasks using molecular data, sounds, environmental data or video as input. More sophisticated uses of deep learning in biology are also beginning to appear. Operating within the machine learning paradigm, deep learning can be viewed as an alternative to mechanistic modelling. It has desirable properties of good performance and scaling with increasing complexity, while posing unique challenges such as sensitivity to bias in input data. We expect that rapid adoption of deep learning in ecology and evolution will continue, especially in automation of biodiversity monitoring and discovery and inference from genetic data. Increased use of unsupervised learning for discovery and visualization of clusters and gaps, simplification of multi‐step analysis pipelines, and integration of machine learning into graduate and postgraduate training are all likely in the near future.