| 1. |
- Abdelgadir, Mohanad, et al.
(författare)
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Distribution of denitrifiers predicted by correlative niche modeling of changing environmental conditions and future climatic scenarios across the Baltic Sea
- 2023
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Ingår i: Ecological Informatics. - : Elsevier. - 1574-9541 .- 1878-0512. ; 78
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Tidskriftsartikel (refereegranskat)abstract
- Denitrifying microbial communities provide an important ecosystem function in aquatic systems. Yet, knowledge on predictive and modeling of these complex and changing communities is limited. The emergently challenging question of how the geographical distribution of denitrifiers responds to ongoing and future environmental change is not yet fully understood. In our study we use metadata-based correlative niche modeling to analyze the geographical distribution of selected putative denitrifiers in the genus Sphingomonas, Mycoplana, Shewanella, and Alteromonas at different predicted environmental conditions and future climatic scenarios across the Baltic Sea. Using the predictive power of an ensemble modeling approach and eight different machine-learning algorithms, habitat suitability and the distribution of the selected denitrifiers were evaluated using geophysical and bioclimatic variables, benthic conditions, and four Representative Concentration Pathway (RCP) trajectories of future global warming scenarios. All algorithms provided successful prediction capabilities both for variable importance, and for habitat suitability with Area Under the Curve (AUC) values between 0.89 and 1.00. Model findings revealed that salinity and nitrate concentrations significantly explained the variation in distribution of the selected denitrifiers. Rising temperatures of 0.8 to 1.8 °C at future RCP60–2050 trajectories are predicted to diminish or eliminate the bioclimatic suitable habitats for denitrifier distributions across the Baltic Sea. Multi-collated terrestrial and marine environmental variables contributed to the successful prediction of denitrifier distributions within the study area. The correlative niche modeling approach with high AUC values presented in the study allowed for accurate projections of the future distributions of the selected denitrifiers. The modeling approach can be used to improve our understanding of how ongoing and predicted future environmental changes may affect habitat suitability for organisms with denitrification capacity across the Baltic Sea.
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| 2. |
- Bengtsson-Palme, Johan, 1985, et al.
(författare)
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Metaxa2 Diversity Tools: Easing microbial community analysis with Metaxa2
- 2016
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Ingår i: Ecological Informatics. - : Elsevier BV. - 1574-9541. ; 33, s. 45-50
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Tidskriftsartikel (refereegranskat)abstract
- DNA sequencing has become an integrated part of microbial ecology, and taxonomic marker genes such as the SSU and LSU rRNA are frequently used to assess community structure. One solution for taxonomic community analysis based on shotgun metagenomic data is the Metaxa2 software, which can extract and classify sequence fragments belonging to the rRNA genes. This paper describes the Metaxa2 Diversity Tools, a set of new open-source software programs that extends the capabilities of the Metaxa2 software. These tools allow for better handling of data from multiple samples, improved species classifications, rarefaction analysis accounting for unclassified entries, and determination of significant differences in community composition of different samples. We demonstrate the performance of the software tools on rRNA data extracted from different shotgun metagenomes, and find the tools to streamline and improve the assessments of community diversity, particularly for samples from environments for which few reference genomes are available. Finally, we establish that our resampling algorithm for determining community dissimilarity is robust to differences in coverage depth, suggesting that it forms a complement to multidimensional visualization approaches for finding differences between communities. The Metaxa2 Diversity Tools are included in recent versions (2.1 and later) of Metaxa2 (http://microbiology.se/software/metaxa2/) and facilitate implementation of Metaxa2 within software pipelines for taxonomic analysis of environmental communities.
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| 3. |
- Björklund, Mats, et al.
(författare)
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Is it possible to infer the number of colonisation events from genetic data alone?
- 2010
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Ingår i: Ecological informatics. - : Elsevier BV. - 1574-9541. ; 5:3, s. 173-176
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Tidskriftsartikel (refereegranskat)abstract
- The current state of populations is to a large determined by events in the past that we have no information about Thus, we have to rely on indirect methods to infer likely scenarios of these events In this paper we describe a simple simulation approach to infer the minimum number of introductions of an invasive species, the round goby in the Baltic Sea The results show that several introductions are most likely to have occurred, possibly even a constant rate of immigration. This poses new threats to local fish populations that currently suffer from overfishing The method is very general and can be applied to other similar situations.
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| 4. |
- Björklund, Mats, et al.
(författare)
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The effect of local population dynamics on patterns of isolation by distance
- 2010
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Ingår i: Ecological Informatics. - : Elsevier BV. - 1574-9541. ; 5:3, s. 167-172
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Tidskriftsartikel (refereegranskat)abstract
- Isolation-by-distance (IBD) is a widely used model explaining population structure and how gene flow decreases with increasing distances. It is biologically intuitive that populations which rarely exchange individuals should drift apart genetically. However, the model is based on the assumptions that populations are large, equal in size and stable over time - conditions that are unlikely to occur in natural conditions. The model has been challenged in the past, for example, in the light of metapopulations or variance in reproductive success. However, an appraisal of the assumption of a large and stable population size per se is lacking. We investigate the robustness of the results concerning IBD patterns when smaller and fluctuating population sizes, or differences in population size are allowed. Through computer simulations we show that allowing for different population sizes and randomly fluctuations leads to unpredictable patterns regarding the results concerning gene flow and IBD. A pattern of IBD could be the result of high gene flow or no gene flow at all, depending on how populations differ in size and how they fluctuate. Adding environmental noise (white, red and blue noise corresponding to random, positive and negative autocorrelation respectively) gives even more unpredictable results concerning patterns of IBD. Our results have important implications for genetic and conservation research. Interpreting an IBD pattern, or lack thereof, is not as easy as earlier thought and needs to be more thoroughly explored.
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| 5. |
- Boroughani, Mahdi, et al.
(författare)
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Application of remote sensing techniques and machine learning algorithms in dust source detection and dust source susceptibility mapping
- 2020
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Ingår i: Ecological Informatics. - : Elsevier BV. - 1574-9541. ; 56
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Tidskriftsartikel (refereegranskat)abstract
- The aim of this research was to develop a method to produce a Dust Source Susceptibility Map (DSSM). For this purpose, we applied remote sensing and statistical-based machine learning algorithms for experimental dust storm studies in the Khorasan Razavi Province, in north-eastern Iran. We identified dust sources in the study area using MODIS satellite images during the 2005–2016 period. For dust source identification, four indices encompassing BTD3132, BTD2931, NDDI, and D variable for 23 MODIS satellite images were calculated. As a result, 65 dust source points were identified, which were categorized into dust source data points for training and validation of the machine learning algorithms. Three statistical-based machine learning algorithms were used including Weights of Evidence (WOE), Frequency Ratio (FR), and Random Forest (RF) to produce DSSM for the study region. We used land use, lithology, slope, soil, geomorphology, NDVI (Normalized Difference Vegetation Index), and distance from river as conditioning variables in the modelling. To check the performance of the models, we applied the Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC). As for the AUC success rate (training), the FR and WOE algorithms resulted in 82 and 83% accuracy, respectively, while the RF algorithm resulted in 91% accuracy. As for the AUC predictive rate (validation), the accuracy of all three models, FR, WOE, and RF, were 80, 81, and 88%, respectively. Although all three algorithms produced acceptable susceptibility maps of dust sources, the results indicated better performance of the RF algorithm.
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| 6. |
- Dalmayne, Jonas, et al.
(författare)
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Assessment of fine-scale plant species beta diversity using WorldView-2 satellite spectral dissimilarity
- 2013
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Ingår i: Ecological Informatics. - : Elsevier BV. - 1574-9541. ; 18:november, s. 1-9
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Tidskriftsartikel (refereegranskat)abstract
- Plant species beta diversity is influenced by spatial heterogeneity in the environment. This heterogeneity can potentially be characterised with the help of remote sensing. We used WorldView-2 satellite data acquired over semi-natural grasslands on The Baltic island of Öland (Sweden) to examine whether dissimilarities in remote sensing response were related to fine-scale, between-plot dissimilarity (beta diversity) in non-woody vascular plant species composition within the grasslands. Fieldwork, including the on-site description of a set of 30 2 m × 2 m plots and a set of 30 4 m × 4 m plots, was performed to record the species dissimilarity between pairs of same-sized plots. Spectral data were extracted by associating each plot with a suite of differently sized pixel windows, and spectral dissimilarity was calculated between pairs of same-sized pixel windows. Relationships between spectral dissimilarity and beta diversity were analysed using univariate regression and partial least squares regression. The study revealed significant positive relationships between spectral dissimilarity and fine-scale (2 m × 2 m and 4 m × 4 m) between-plot species dissimilarity. The correlation between the predicted and the observed species dissimilarity was stronger for the set of large plots (4 m × 4 m) than for the set of small plots (2 m × 2 m), and the association between spectral and species data at both plot scales decreased when pixel windows larger than 3 × 3 pixels were used. We suggest that the significant relationship between spectral dissimilarity and species dissimilarity is a reflection of between-plot environmental heterogeneity caused by differences in grazing intensity (which result in between-plot differences in field-layer height, and amounts of biomass and litter). This heterogeneity is reflected in dissimilarities in both the species composition and the spectral response of the grassland plots. Between-plot dissimilarities in both spectral response and species composition may also be caused by between-plot variations in edaphic conditions. Our results indicate that high spatial resolution satellite data may potentially be able to complement field-based recording in surveys of fine-scale species diversity in semi-natural grasslands.
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| 7. |
- Daume, Stefan
(författare)
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Mining Twitter to monitor invasive alien species - An analytical framework and sample information topologies
- 2016
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Ingår i: Ecological Informatics. - : Elsevier BV. - 1574-9541 .- 1878-0512. ; 31, s. 70-82
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Tidskriftsartikel (refereegranskat)abstract
- Social online media increasingly emerge as important informal information sources that can contribute to the detection of trends and early warnings in critical fields such as public health monitoring or emergency management. In the face of global environmental challenges the utilization of this information in ecological monitoring contexts has been called for, but examples remain sparse. This can be attributed to the significant technical challenges in processing this data and concerns about the quality, reliability and applicability of information mined from social media to the ecological domain. Here the strength and weaknesses of social media mining for ecological monitoring are assessed using the micro-blogging service Twitter and invasive alien species (IAS) monitoring as an example. The assessment is based on a manual analysis of 2842 Tweets sampled from Twitter data with potential direct or descriptive references to IAS impacting forest ecosystems, which was collected over a period of nearly three years. The results are presented as information topologies for Twitter messages of observational and non-observational character for three IAS with distinctive characteristics (Oak Processionary Moth, Emerald Ash Borer, Eastern Grey Squirrel). The results show that the social media channel Twitter is a rich source of primary and secondary observational biodiversity information. It also provides useful insights in the topical landscape of public communications on IAS as well as the public perception of IAS and IAS management. The analysis suggests broad application opportunities in IAS monitoring and management, and points at applications for related environmental questions. The results highlight that social media mining for ecological monitoring needs to be approached with the same best practices as ecological monitoring in general, requiring a good understanding of the monitored subjects and specific monitoring questions. The challenges in utilizing this information for operational systems are of technical rather than conceptual nature and include extending the degree of automation, especially with regard to image recognition and the automatic provisioning of location information.
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| 8. |
- Ekström, Magnus, et al.
(författare)
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Logistic regression for clustered data from environmental monitoring programs
- 2018
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Ingår i: Ecological Informatics. - : Elsevier. - 1574-9541 .- 1878-0512. ; 43, s. 165-173
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Tidskriftsartikel (refereegranskat)abstract
- Large-scale surveys, such as national forest inventories and vegetation monitoring programs, usually have complex sampling designs that include geographical stratification and units organized in clusters. When models are developed using data from such programs, a key question is whether or not to utilize design information when analyzing the relationship between a response variable and a set of covariates. Standard statistical regression methods often fail to account for complex sampling designs, which may lead to severely biased estimators of model coefficients. Furthermore, ignoring that data are spatially correlated within clusters may underestimate the standard errors of regression coefficient estimates, with a risk for drawing wrong conclusions. We first review general approaches that account for complex sampling designs, e.g. methods using probability weighting, and stress the need to explore the effects of the sampling design when applying logistic regression models. We then use Monte Carlo simulation to compare the performance of the standard logistic regression model with two approaches to model correlated binary responses, i.e. cluster-specific and population-averaged logistic regression models. As an example, we analyze the occurrence of epiphytic hair lichens in the genus Bryoria; an indicator of forest ecosystem integrity. Based on data from the National Forest Inventory (NFI) for the period 1993-2014 we generated a data set on hair lichen occurrence on > 100,000 Picea abies trees distributed throughout Sweden. The NFI data included ten covariates representing forest structure and climate variables potentially affecting lichen occurrence. Our analyses show the importance of taking complex sampling designs and correlated binary responses into account in logistic regression modeling to avoid the risk of obtaining notably biased parameter estimators and standard errors, and erroneous interpretations about factors affecting e.g. hair lichen occurrence. We recommend comparisons of unweighted and weighted logistic regression analyses as an essential step in development of models based on data from large-scale surveys.
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| 9. |
- Ely, K. S., et al.
(författare)
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A reporting format for leaf-level gas exchange data and metadata
- 2021
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Ingår i: Ecological Informatics. - : Elsevier BV. - 1574-9541. ; 61
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Tidskriftsartikel (refereegranskat)abstract
- Leaf-level gas exchange data support the mechanistic understanding of plant fluxes of carbon and water. These fluxes inform our understanding of ecosystem function, are an important constraint on parameterization of terrestrial biosphere models, are necessary to understand the response of plants to global environmental change, and are integral to efforts to improve crop production. Collection of these data using gas analyzers can be both technically challenging and time consuming, and individual studies generally focus on a small range of species, restricted time periods, or limited geographic regions. The high value of these data is exemplified by the many publications that reuse and synthesize gas exchange data, however the lack of metadata and data reporting conventions make full and efficient use of these data difficult. Here we propose a reporting format for leaf-level gas exchange data and metadata to provide guidance to data contributors on how to store data in repositories to maximize their discoverability, facilitate their efficient reuse, and add value to individual datasets. For data users, the reporting format will better allow data repositories to optimize data search and extraction, and more readily integrate similar data into harmonized synthesis products. The reporting format specifies data table variable naming and unit conventions, as well as metadata characterizing experimental conditions and protocols. For common data types that were the focus of this initial version of the reporting format, i.e., survey measurements, dark respiration, carbon dioxide and light response curves, and parameters derived from those measurements, we took a further step of defining required additional data and metadata that would maximize the potential reuse of those data types. To aid data contributors and the development of data ingest tools by data repositories we provided a translation table comparing the outputs of common gas exchange instruments. Extensive consultation with data collectors, data users, instrument manufacturers, and data scientists was undertaken in order to ensure that the reporting format met community needs. The reporting format presented here is intended to form a foundation for future development that will incorporate additional data types and variables as gas exchange systems and measurement approaches advance in the future. The reporting format is published in the U.S. Department of Energy?s ESS-DIVE data repository, with documentation and future development efforts being maintained in a version control system.
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| 10. |
- Fourcade, Yoan
(författare)
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Comparing species distributions modelled from occurrence data and from expert-based range maps. Implication for predicting range shifts with climate change
- 2016
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Ingår i: Ecological Informatics. - : Elsevier BV. - 1574-9541 .- 1878-0512. ; 36, s. 8-14
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Tidskriftsartikel (refereegranskat)abstract
- Species range and climate change risk are often assessed using species distribution models (SDM) that model species niche from presence points and environmental variables and project it in space and time. These presence points frequently originate from occurrence data downloaded from public biodiversity databases, but such data are known to suffer from high biases. There is thus a need to find alternative sources of information to train these models. In this regard, expert-based range maps such as those provided by the International Union for Conservation of Nature (IUCN) have the potential to be used as a source of species presence in a SDM workflow. Here, I compared the predictions of SDM built using true occurrences provided by GBIF or iNaturalist, or using pseudo-occurrences sampled from IUCN expert-based range maps, in current and future climate. I found that the agreement between both types of SDM did not depend on the spatial resolution of environmental data but instead were affected by the number of points sampled from range maps and even more by the spatial congruence between input data. A strong agreement between occurrence data and range maps resulted in very similar SDM outputs, which suggests that expert knowledge can be a valuable alternative source of data to feed SDM and assess potential range shifts when the only available occurrences are biased or fragmentary. (C) 2016 Elsevier B.V. All rights reserved.
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