| 1. |
- Gardner, Emma, et al.
(författare)
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Reliably predicting pollinator abundance : Challenges of calibrating process-based ecological models
- 2020
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Ingår i: Methods in Ecology and Evolution. - 2041-210X. ; 11:12, s. 1673-1689
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Tidskriftsartikel (refereegranskat)abstract
- Pollination is a key ecosystem service for global agriculture but evidence of pollinator population declines is growing. Reliable spatial modelling of pollinator abundance is essential if we are to identify areas at risk of pollination service deficit and effectively target resources to support pollinator populations. Many models exist which predict pollinator abundance but few have been calibrated against observational data from multiple habitats to ensure their predictions are accurate. We selected the most advanced process-based pollinator abundance model available and calibrated it for bumblebees and solitary bees using survey data collected at 239 sites across Great Britain. We compared three versions of the model: one parameterised using estimates based on expert opinion, one where the parameters are calibrated using a purely data-driven approach and one where we allow the expert opinion estimates to inform the calibration process. All three model versions showed significant agreement with the survey data, demonstrating this model's potential to reliably map pollinator abundance. However, there were significant differences between the nesting/floral attractiveness scores obtained by the two calibration methods and from the original expert opinion scores. Our results highlight a key universal challenge of calibrating spatially explicit, process-based ecological models. Notably, the desire to reliably represent complex ecological processes in finely mapped landscapes necessarily generates a large number of parameters, which are challenging to calibrate with ecological and geographical data that are often noisy, biased, asynchronous and sometimes inaccurate. Purely data-driven calibration can therefore result in unrealistic parameter values, despite appearing to improve model-data agreement over initial expert opinion estimates. We therefore advocate a combined approach where data-driven calibration and expert opinion are integrated into an iterative Delphi-like process, which simultaneously combines model calibration and credibility assessment. This may provide the best opportunity to obtain realistic parameter estimates and reliable model predictions for ecological systems with expert knowledge gaps and patchy ecological data.
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| 2. |
- Olsson, Ola, et al.
(författare)
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Efficient, automated and robust pollen analysis using deep learning
- 2021
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Ingår i: Methods in Ecology and Evolution. - 2041-210X. ; 12:5, s. 850-862
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Tidskriftsartikel (refereegranskat)abstract
- Pollen analysis is an important tool in many fields, including pollination ecology, paleoclimatology, paleoecology, honey quality control, and even medicine and forensics. However, labour‐intensive manual pollen analysis often constrains the number of samples processed or the number of pollen analysed per sample. Thus, there is a desire to develop reliable, high‐throughput, automated systems. We present an automated method for pollen analysis, based on deep learning convolutional neural networks (CNN). We scanned microscope slides with fuchsine stained, fresh pollen and automatically extracted images of all individual pollen grains. CNN models were trained on reference samples (122,000 pollen grains, from 347 flowers of 83 species of 17 families). The models were used to classify images of different pollen grains in a series of experiments. We also propose an adjustment to reduce overestimation of sample diversity in cases where samples are likely to contain few species. Accuracy of a model for 83 species was 0.98 when all samples of each species were first pooled, and then split into a training and a validation set (splitting experiment). However, accuracy was much lower (0.41) when individual reference samples from different flowers were kept separate, and one such sample was used for validation of models trained on remaining samples of the species (leave‐one‐out experiment). We therefore combined species into 28 pollen types where a new leave‐one‐out experiment revealed an overall accuracy of 0.68, and recall rates >0.90 in most pollen types. When validating against 63,650 manually identified pollen grains from 370 bumblebee samples, we obtained an accuracy of 0.79, but our adjustment procedure increased this to 0.85. Validation through splitting experiments may overestimate robustness of CNN pollen analysis in new contexts (samples). Nevertheless, our method has the potential to allow large quantities of real pollen data to be analysed with reasonable accuracy. Although compiling pollen reference libraries is time‐consuming, this is simplified by our method, and can lead to widely accessible and shareable resources for pollen analysis.
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| 3. |
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| 4. |
- Dettki, Holger
(författare)
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A standardisation framework for bio-logging data to advance ecological research and conservation
- 2021
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Ingår i: Methods in Ecology and Evolution. - 2041-210X. ; 12, s. 996-1007
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Tidskriftsartikel (refereegranskat)abstract
- Bio-logging data obtained by tagging animals are key to addressing global conservation challenges. However, the many thousands of existing bio-logging datasets are not easily discoverable, universally comparable, nor readily accessible through existing repositories and across platforms, slowing down ecological research and effective management. A set of universal standards is needed to ensure discoverability, interoperability and effective translation of bio-logging data into research and management recommendations.We propose a standardisation framework adhering to existing data principles (FAIR: Findable, Accessible, Interoperable and Reusable; and TRUST: Transparency, Responsibility, User focus, Sustainability and Technology) and involving the use of simple templates to create a data flow from manufacturers and researchers to compliant repositories, where automated procedures should be in place to prepare data availability into four standardised levels: (a) decoded raw data, (b) curated data, (c) interpolated data and (d) gridded data. Our framework allows for integration of simple tabular arrays (e.g. csv files) and creation of sharable and interoperable network Common Data Form (netCDF) files containing all the needed information for accuracy-of-use, rightful attribution (ensuring data providers keep ownership through the entire process) and data preservation security.We show the standardisation benefits for all stakeholders involved, and illustrate the application of our framework by focusing on marine animals and by providing examples of the workflow across all data levels, including filled templates and code to process data between levels, as well as templates to prepare netCDF files ready for sharing.Adoption of our framework will facilitate collection of Essential Ocean Variables (EOVs) in support of the Global Ocean Observing System (GOOS) and inter-governmental assessments (e.g. the World Ocean Assessment), and will provide a starting point for broader efforts to establish interoperable bio-logging data formats across all fields in animal ecology.
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| 5. |
- Edler, Daniel, et al.
(författare)
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raxmlGUI 2.0: A graphical interface and toolkit for phylogenetic analyses using RAxML
- 2021
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Ingår i: Methods in Ecology and Evolution. - : John Wiley & Sons. - 2041-210X. ; 12:2, s. 373-377
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Tidskriftsartikel (refereegranskat)abstract
- raxmlGUI is a graphical user interface to RAxML, one of the most popular and widely used softwares for phylogenetic inference using maximum likelihood. Here we present raxmlGUI 2.0, a complete rewrite of the GUI which seamlessly integrates RAxML binaries for all major operating systems with an intuitive graphical front-end to setup and run phylogenetic analyses. Our program offers automated pipelines for analyses that require multiple successive calls of RAxML, built-in functions to concatenate alignment files while automatically specifying the appropriate partition settings, and one-click model testing to select the best substitution models using ModelTest-NG. In addition to RAxML 8.x, raxmlGUI 2.0 also supports the new RAxML-NG, which provides new functionality and higher performance on large datasets. raxmlGUI 2.0 facilitates phylogenetic analyses by coupling an intuitive interface with the unmatched performance of RAxML. © 2020 The Authors. Methods in Ecology and Evolution published by John Wiley & Sons Ltd on behalf of British Ecological Society
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| 6. |
- Ekström, Magnus, 1966-, et al.
(författare)
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Estimating density from presence/absence data in clustered populations
- 2020
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Ingår i: Methods in Ecology and Evolution. - : John Wiley & Sons. - 2041-210X. ; 11:3, s. 390-402
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Tidskriftsartikel (refereegranskat)abstract
- Inventories of plant populations are fundamental in ecological research and monitoring, but such surveys are often prone to field assessment errors. Presence/absence (P/A) sampling may have advantages over plant cover assessments for reducing such errors. However, the linking between P/A data and plant density depends on model assumptions for plant spatial distributions. Previous studies have shown, for example, how that plant density can be estimated under Poisson model assumptions on the plant locations. In this study, new methods are developed and evaluated for linking P/A data with plant density assuming that plants occur in clustered spatial patterns. New theory was derived for estimating plant density under Neyman-Scott-type cluster models such as the Matern and Thomas cluster processes. Suggested estimators, corresponding confidence intervals and a proposed goodness-of-fit test were evaluated in a Monte Carlo simulation study assuming a Matern cluster process. Furthermore, the estimators were applied to plant data from environmental monitoring in Sweden to demonstrate their empirical application. The simulation study showed that our methods work well for large enough sample sizes. The judgment of what is' large enough' is often difficult, but simulations indicate that a sample size is large enough when the sampling distributions of the parameter estimators are symmetric or mildly skewed. Bootstrap may be used to check whether this is true. The empirical results suggest that the derived methodology may be useful for estimating density of plants such as Leucanthemum vulgare and Scorzonera humilis. By developing estimators of plant density from P/A data under realistic model assumptions about plants' spatial distributions, P/A sampling will become a more useful tool for inventories of plant populations. Our new theory is an important step in this direction.
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| 7. |
- Farage, C., et al.
(författare)
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Identifying flow modules in ecological networks using Infomap
- 2021
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Ingår i: Methods in Ecology and Evolution. - : Wiley. - 2041-210X. ; 12:5, s. 778-786
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Tidskriftsartikel (refereegranskat)abstract
- Analysing how species interact in modules is a fundamental problem in network ecology. Theory shows that a modular network structure can reveal underlying dynamic ecological and evolutionary processes, influence dynamics that operate on the network and affect the stability of the ecological system. Although many ecological networks describe flows, such as biomass flows in food webs or disease transmission, most modularity analyses have ignored network flows, which can hinder our understanding of the interplay between structure and dynamics. Here we present Infomap, an established method based on network flows to the field of ecological networks. Infomap is a flexible tool that can identify modules in virtually any type of ecological network and is particularly useful for directed, weighted and multilayer networks. We illustrate how Infomap works on all these network types. We also provide a fully documented repository with additional ecological examples. Finally, to help researchers to analyse their networks with Infomap, we introduce the open-source R package infomapecology. Analysing flow-based modularity is useful across ecology and transcends to other biological and non-biological disciplines. A dynamic approach for detecting modular structure has strong potential to provide new insights into the organisation of ecological networks.
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| 8. |
- Farage, Carmel, et al.
(författare)
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Identifying flow modules in ecological networks using Infomap
- 2021
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Ingår i: Methods in Ecology and Evolution. - London : British Ecology Society. - 2041-210X. ; 12:5, s. 778-786
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Tidskriftsartikel (refereegranskat)abstract
- Analysing how species interact in modules is a fundamental problem in network ecology. Theory shows that a modular network structure can reveal underlying dynamic ecological and evolutionary processes, influence dynamics that operate on the network and affect the stability of the ecological system. Although many ecological networks describe flows, such as biomass flows in food webs or disease transmission, most modularity analyses have ignored network flows, which can hinder our understanding of the interplay between structure and dynamics. Here we present Infomap, an established method based on network flows to the field of ecological networks. Infomap is a flexible tool that can identify modules in virtually any type of ecological network and is particularly useful for directed, weighted and multilayer networks. We illustrate how Infomap works on all these network types. We also provide a fully documented repository with additional ecological examples. Finally, to help researchers to analyse their networks with Infomap, we introduce the open-source R package infomapecology. Analysing flow-based modularity is useful across ecology and transcends to other biological and non-biological disciplines. A dynamic approach for detecting modular structure has strong potential to provide new insights into the organisation of ecological networks.
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| 9. |
- Gaboriau, T., et al.
(författare)
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A multi-platform package for the analysis of intra- and interspecific trait evolution
- 2020
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Ingår i: Methods in Ecology and Evolution. - 2041-210X. ; 11:11, s. 1439-1447
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Tidskriftsartikel (refereegranskat)abstract
- Evolutionary forces affect the distribution of phenotypes both within and among species. Yet, at the macro-evolutionary scale, the evolution of intraspecific variance is rarely considered. Here, we present an r and a BEAST 2 implementation that extends the JIVE (Joint inter- and Intraspecific Variance Evolution) model aimed at the analysis of continuous trait evolution at both inter- and intraspecific level. Using a hierarchical Bayesian approach, we implemented a range of models for continuous trait evolution that operate independently on species means and variances along a phylogeny. The package uses Markov chain Monte Carlo for the inference of parameters and the evaluation of model fit. JIVE is available in the bite (Bayesian Integrative models of Trait Evolution) r package, as well as in BEAST 2. The two implementations offer the same continuous trait evolutionary models, but differ in their use and types of analyses. The r implementation allows for faster analyses by taking the phylogeny as data, while providing graphical and statistical functions as part of tools for model comparison, result parsing and summary, and plotting. In the BEAST 2 implementation, the species tree is a parameter, and both its topology and divergence times are jointly estimated with trait model parameters. The bite package and the BEAST 2 implementation introduce new frameworks within comparative phylogenetics that explicitly model intraspecific variance. These tools allow users to tackle long-standing questions in evolutionary biology, such as the identification of key evolutionary processes determining niche conservatism, niche partitioning, and life-history strategies. © 2020 British Ecological Society
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| 10. |
- Kass, Jamie M., et al.
(författare)
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ENMeval 2.0 : Redesigned for customizable and reproducible modeling of species’ niches and distributions
- 2021
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Ingår i: Methods in Ecology and Evolution. - : John Wiley & Sons. - 2041-210X. ; 12:9, s. 1602-1608
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Tidskriftsartikel (refereegranskat)abstract
- Quantitative evaluations to optimize complexity have become standard for avoiding overfitting of ecological niche models (ENMs) that estimate species’ potential geographic distributions. ENMeval was the first R package to make such evaluations (often termed model tuning) widely accessible for the Maxent algorithm. It also provided multiple methods for partitioning occurrence data and reported various performance metrics.Requests by users, recent developments in the field, and needs for software compatibility led to a major redesign and expansion. We additionally conducted a literature review to investigate trends in ENMeval use (2015–2019).ENMeval 2.0 has a new object-oriented structure for adding other algorithms, enables customizing algorithmic settings and performance metrics, generates extensive metadata, implements a null-model approach to quantify significance and effect sizes, and includes features to increase the breadth of analyses and visualizations. In our literature review, we found insufficient reporting of model performance and parameterization, heavy reliance on model selection with AICc and low utilization of spatial cross-validation; we explain how ENMeval 2.0 can help address these issues.This redesigned and expanded version can promote progress in the field and improve the information available for decision-making.
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