scholarly journals Unmasking structural patterns in incidence matrices: an application to ecological data

2019 ◽  
Vol 16 (151) ◽  
pp. 20180747
Author(s):  
Bernat Bramon Mora ◽  
Giulio V. Dalla Riva ◽  
Daniel B. Stouffer
Keyword(s):  
Community Ecology ◽  
Incidence Matrix ◽  
Null Model ◽  
Statistical Modelling ◽  
Null Models ◽  
Ecological Communities ◽  
Ecological Data ◽  
Incidence Matrices ◽  
Structural Patterns ◽  
The Impact

Null models have become a crucial tool for understanding structure within incidence matrices across multiple biological contexts. For example, they have been widely used for the study of ecological and biogeographic questions, testing hypotheses regarding patterns of community assembly, species co-occurrence and biodiversity. However, to our knowledge we remain without a general and flexible approach to study the mechanisms explaining such structures. Here, we provide a method for generating ‘correlation-informed’ null models, which combine the classic concept of null models and tools from community ecology, like joint statistical modelling. Generally, this model allows us to assess whether the information encoded within any given correlation matrix is predictive for explaining structural patterns observed within an incidence matrix. To demonstrate its utility, we apply our approach to two different case studies that represent examples of common scenarios encountered in community ecology. First, we use a phylogenetically informed null model to detect a strong evolutionary fingerprint within empirically observed food webs, reflecting key differences in the impact of shared evolutionary history when shaping the interactions of predators or prey. Second, we use multiple informed null models to identify which factors determine structural patterns of species assemblages, focusing in on the study of nestedness and the influence of site size, isolation, species range and species richness. In addition to offering a versatile way to study the mechanisms shaping the structure of any incidence matrix, including those describing ecological communities, our approach can also be adapted further to test even more sophisticated hypotheses.

scholarly journals Pleistocene megafaunal interaction networks became more vulnerable after human arrival

2015 ◽  
Vol 282 (1814) ◽  
pp. 20151367 ◽  
Author(s):  
Mathias M. Pires ◽  
Paul L. Koch ◽  
Richard A. Fariña ◽  
Marcus A. M. de Aguiar ◽  
Sérgio F. dos Reis ◽  
...  
Keyword(s):  
Structural Changes ◽  
Ecological Communities ◽  
Ecological Interactions ◽  
Ecological Data ◽  
The Past ◽  
As Species ◽  
Extinction Events ◽  
Almost All ◽  
The Impact ◽  
Unstable Dynamics

The end of the Pleistocene was marked by the extinction of almost all large land mammals worldwide except in Africa. Although the debate on Pleistocene extinctions has focused on the roles of climate change and humans, the impact of perturbations depends on properties of ecological communities, such as species composition and the organization of ecological interactions. Here, we combined palaeoecological and ecological data, food-web models and community stability analysis to investigate if differences between Pleistocene and modern mammalian assemblages help us understand why the megafauna died out in the Americas while persisting in Africa. We show Pleistocene and modern assemblages share similar network topology, but differences in richness and body size distributions made Pleistocene communities significantly more vulnerable to the effects of human arrival. The structural changes promoted by humans in Pleistocene networks would have increased the likelihood of unstable dynamics, which may favour extinction cascades in communities facing extrinsic perturbations. Our findings suggest that the basic aspects of the organization of ecological communities may have played an important role in major extinction events in the past. Knowledge of community-level properties and their consequences to dynamics may be critical to understand past and future extinctions.

scholarly journals Hill-based Dissimilarity Indices and Null Models for Analysis of Microbial Community Assembly

2020 ◽  
Author(s):  
Oskar Modin ◽  
Raquel Liebana ◽  
Soroush Saheb-Alam ◽  
Britt-Marie Wilén ◽  
Carolina Suarez ◽  
...  
Keyword(s):  
Microbial Community ◽  
Microbial Communities ◽  
Community Assembly ◽  
Null Model ◽  
Amplicon Sequencing ◽  
Null Models ◽  
Marker Genes ◽  
Experimental Systems ◽  
Microbial Community Assembly ◽  
The Impact

Abstract Background: High-throughput amplicon sequencing of marker genes, such as the 16S rRNA gene in Bacteria and Archaea, provides a wealth of information about the composition of microbial communities. To quantify differences between samples and draw conclusions about factors affecting community assembly, dissimilarity indices are typically used. However, results are subject to several biases and data interpretation can be challenging. The Jaccard and Bray-Curtis indices, which are often used to quantify taxonomic dissimilarity, are not necessarily the most logical choices. Instead, we argue that Hill-based indices, which make it possible to systematically investigate the impact of relative abundance on dissimilarity, should be used for robust analysis of data. In combination with a null model, mechanisms of microbial community assembly can be analyzed. Here, we also introduce a new software, qdiv, which enables rapid calculations of Hill-based dissimilarity indices in combination with null models.Results: Using amplicon sequencing data from two experimental systems, aerobic granular sludge (AGS) reactors and microbial fuel cells (MFC), we show that the choice of dissimilarity index can have considerable impact on results and conclusions. High dissimilarity between replicates because of random sampling effects make incidence-based indices less suited for identifying differences between groups of samples. Determining a consensus table based on count tables generated with different bioinformatic pipelines reduced the number of low-abundant, potentially spurious amplicon sequence variants (ASVs) in the data sets, which led to lower dissimilarity between replicates. Analysis with a combination of Hill-based indices and a null model allowed us to show that different ecological mechanisms acted on different fractions of the microbial communities in the experimental systems.Conclusions: Hill-based indices provide a rational framework for analysis of dissimilarity between microbial community samples. In combination with a null model, the effects of deterministic and stochastic community assembly factors on taxa of different relative abundances can be systematically investigated. Calculations of Hill-based dissimilarity indices in combination with a null model can be done in qdiv, which is freely available as a Python package (https://github.com/omvatten/qdiv). In qdiv, a consensus table can also be determined from several count tables generated with different bioinformatic pipelines.

scholarly journals Hill-based dissimilarity indices and null models for analysis of microbial community assembly

2020 ◽  
Author(s):  
Oskar Modin ◽  
Raquel Liébana ◽  
Soroush Saheb-Alam ◽  
Britt-Marie Wilén ◽  
Carolina Suarez ◽  
...  
Keyword(s):  
Microbial Community ◽  
Microbial Communities ◽  
Community Assembly ◽  
Null Model ◽  
Amplicon Sequencing ◽  
Null Models ◽  
Marker Genes ◽  
Experimental Systems ◽  
Microbial Community Assembly ◽  
The Impact

Abstract Background: High-throughput amplicon sequencing of marker genes, such as the 16S rRNA gene in Bacteria and Archaea, provides a wealth of information about the composition of microbial communities. To quantify differences between samples and draw conclusions about factors affecting community assembly, dissimilarity indices are typically used. However, results are subject to several biases and data interpretation can be challenging. The Jaccard and Bray-Curtis indices, which are often used to quantify taxonomic dissimilarity, are not necessarily the most logical choices. Instead, we argue that Hill-based indices, which make it possible to systematically investigate the impact of relative abundance on dissimilarity, should be used for robust analysis of data. In combination with a null model, mechanisms of microbial community assembly can be analyzed. Here, we also introduce a new software, qdiv, which enables rapid calculations of Hill-based dissimilarity indices in combination with null models.Results: Using amplicon sequencing data from two experimental systems, aerobic granular sludge (AGS) reactors and microbial fuel cells (MFC), we show that the choice of dissimilarity index can have considerable impact on results and conclusions. High dissimilarity between replicates because of random sampling effects make incidence-based indices less suited for identifying differences between groups of samples. Determining a consensus table based on count tables generated with different bioinformatic pipelines reduced the number of low-abundant, potentially spurious amplicon sequence variants (ASVs) in the data sets, which led to lower dissimilarity between replicates. Analysis with a combination of Hill-based indices and a null model allowed us to show that different ecological mechanisms acted on different fractions of the microbial communities in the experimental systems.Conclusions: Hill-based indices provide a rational framework for analysis of dissimilarity between microbial community samples. In combination with a null model, the effects of deterministic and stochastic community assembly factors on taxa of different relative abundances can be systematically investigated. Calculations of Hill-based dissimilarity indices in combination with a null model can be done in qdiv, which is freely available as a Python package (https://github.com/omvatten/qdiv). In qdiv, a consensus table can also be determined from several count tables generated with different bioinformatic pipelines.

scholarly journals Hill-based dissimilarity indices and null models for analysis of microbial community assembly

2020 ◽  
Author(s):  
Oskar Modin ◽  
Raquel Liébana ◽  
Soroush Saheb-Alam ◽  
Britt-Marie Wilén ◽  
Carolina Suarez ◽  
...  
Keyword(s):  
Microbial Community ◽  
Microbial Communities ◽  
Community Assembly ◽  
Null Model ◽  
Amplicon Sequencing ◽  
Null Models ◽  
Marker Genes ◽  
Experimental Systems ◽  
Microbial Community Assembly ◽  
The Impact

Abstract Background: High-throughput amplicon sequencing of marker genes, such as the 16S rRNA gene in Bacteria and Archaea, provides a wealth of information about the composition of microbial communities. To quantify differences between samples and draw conclusions about factors affecting community assembly, dissimilarity indices are typically used. However, results are subject to several biases and data interpretation can be challenging. The Jaccard and Bray-Curtis indices, which are often used to quantify taxonomic dissimilarity, are not necessarily the most logical choices. Instead, we argue that Hill-based indices, which make it possible to systematically investigate the impact of relative abundance on dissimilarity, should be used for robust analysis of data. In combination with a null model, mechanisms of microbial community assembly can be analyzed. Here, we also introduce a new software, qdiv, which enables rapid calculations of Hill-based dissimilarity indices in combination with null models.Results: Using amplicon sequencing data from two experimental systems, aerobic granular sludge (AGS) reactors and microbial fuel cells (MFC), we show that the choice of dissimilarity index can have considerable impact on results and conclusions. High dissimilarity between replicates because of random sampling effects make incidence-based indices less suited for identifying differences between groups of samples. Determining a consensus table based on count tables generated with different bioinformatic pipelines reduced the number of low-abundant, potentially spurious amplicon sequence variants (ASVs) in the data sets, which led to lower dissimilarity between replicates. Analysis with a combination of Hill-based indices and a null model allowed us to show that different ecological mechanisms acted on different fractions of the microbial communities in the experimental systems.Conclusions: Hill-based indices provide a rational framework for analysis of dissimilarity between microbial community samples. In combination with a null model, the effects of deterministic and stochastic community assembly factors on taxa of different relative abundances can be systematically investigated. Calculations of Hill-based dissimilarity indices in combination with a null model can be done in qdiv, which is freely available as a Python package (https://github.com/omvatten/qdiv). In qdiv, a consensus table can also be determined from several count tables generated with different bioinformatic pipelines.

scholarly journals Hill-based dissimilarity indices and null models for analysis of microbial community assembly

Microbiome ◽  
2020 ◽  
Vol 8 (1) ◽  
Author(s):  
Oskar Modin ◽  
Raquel Liébana ◽  
Soroush Saheb-Alam ◽  
Britt-Marie Wilén ◽  
Carolina Suarez ◽  
...  
Keyword(s):  
Microbial Community ◽  
Microbial Communities ◽  
Community Assembly ◽  
Null Model ◽  
Amplicon Sequencing ◽  
Null Models ◽  
Marker Genes ◽  
Experimental Systems ◽  
Microbial Community Assembly ◽  
The Impact

Abstract Background High-throughput amplicon sequencing of marker genes, such as the 16S rRNA gene in Bacteria and Archaea, provides a wealth of information about the composition of microbial communities. To quantify differences between samples and draw conclusions about factors affecting community assembly, dissimilarity indices are typically used. However, results are subject to several biases, and data interpretation can be challenging. The Jaccard and Bray-Curtis indices, which are often used to quantify taxonomic dissimilarity, are not necessarily the most logical choices. Instead, we argue that Hill-based indices, which make it possible to systematically investigate the impact of relative abundance on dissimilarity, should be used for robust analysis of data. In combination with a null model, mechanisms of microbial community assembly can be analyzed. Here, we also introduce a new software, qdiv, which enables rapid calculations of Hill-based dissimilarity indices in combination with null models. Results Using amplicon sequencing data from two experimental systems, aerobic granular sludge (AGS) reactors and microbial fuel cells (MFC), we show that the choice of dissimilarity index can have considerable impact on results and conclusions. High dissimilarity between replicates because of random sampling effects make incidence-based indices less suited for identifying differences between groups of samples. Determining a consensus table based on count tables generated with different bioinformatic pipelines reduced the number of low-abundant, potentially spurious amplicon sequence variants (ASVs) in the data sets, which led to lower dissimilarity between replicates. Analysis with a combination of Hill-based indices and a null model allowed us to show that different ecological mechanisms acted on different fractions of the microbial communities in the experimental systems. Conclusions Hill-based indices provide a rational framework for analysis of dissimilarity between microbial community samples. In combination with a null model, the effects of deterministic and stochastic community assembly factors on taxa of different relative abundances can be systematically investigated. Calculations of Hill-based dissimilarity indices in combination with a null model can be done in qdiv, which is freely available as a Python package (https://github.com/omvatten/qdiv). In qdiv, a consensus table can also be determined from several count tables generated with different bioinformatic pipelines.

scholarly journals Hill-based dissimilarity indices and null models for analysis of microbial community assembly

2020 ◽  
Author(s):  
Oskar Modin ◽  
Raquel Liébana ◽  
Soroush Sabeh-Alam ◽  
Britt-Marie Wilén ◽  
Carolina Suarez ◽  
...  
Keyword(s):  
Microbial Community ◽  
Microbial Communities ◽  
Community Assembly ◽  
Null Model ◽  
Amplicon Sequencing ◽  
Null Models ◽  
Marker Genes ◽  
Experimental Systems ◽  
Microbial Community Assembly ◽  
The Impact

Abstract Background: High-throughput amplicon sequencing of marker genes, such as the 16S rRNA gene in Bacteria and Archaea, provides a wealth of information about the composition of microbial communities. To quantify differences between samples and draw conclusions about factors affecting community assembly, dissimilarity indices are typically used. However, results are subject to several biases and data interpretation can be challenging. The Jaccard and Bray-Curtis indices, which are often used to quantify taxonomic dissimilarity, are not necessarily the most logical choices. Instead, we argue that Hill-based indices, which make it possible to systematically investigate the impact of relative abundance on dissimilarity, should be used for robust analysis of data. In combination with a null model, mechanisms of microbial community assembly can be analyzed. Here, we also introduce a new software, qdiv, which enables rapid calculations of Hill-based dissimilarity indices in combination with null models.Results: Using amplicon sequencing data from two experimental systems, aerobic granular sludge (AGS) reactors and microbial fuel cells (MFC), we show that the choice of dissimilarity index can have considerable impact on results and conclusions. High dissimilarity between replicates because of random sampling effects make incidence-based indices less suited for identifying differences between groups of samples. Determining a consensus table based on count tables generated with different bioinformatic pipelines reduced the number of low-abundant, potentially spurious amplicon sequence variants (ASVs) in the data sets, which led to lower dissimilarity between replicates. Analysis with a combination of Hill-based indices and a null model allowed us to show that different ecological mechanisms acted on different fractions of the microbial communities in the experimental systems.Conclusions: Hill-based indices provide a rational framework for analysis of dissimilarity between microbial community samples. In combination with a null model, the effects of deterministic and stochastic community assembly factors on taxa of different relative abundances can be systematically investigated. Calculations of Hill-based dissimilarity indices in combination with a null model can be done in qdiv, which is freely available as a Python package (https://github.com/omvatten/qdiv). In qdiv, a consensus table can also be determined from several count tables generated with different bioinformatic pipelines.

scholarly journals Detecting anomalous citation groups in journal networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Sadamori Kojaku ◽  
Giacomo Livan ◽  
Naoki Masuda
Keyword(s):  
Null Model ◽  
Impact Factors ◽  
Academic Publishing ◽  
Scientific Communities ◽  
Citation Behavior ◽  
Journal Editors ◽  
Journal Citation Reports ◽  
Academic Climate ◽  
The Impact ◽  
Publication Standards

AbstractThe ever-increasing competitiveness in the academic publishing market incentivizes journal editors to pursue higher impact factors. This translates into journals becoming more selective, and, ultimately, into higher publication standards. However, the fixation on higher impact factors leads some journals to artificially boost impact factors through the coordinated effort of a “citation cartel” of journals. “Citation cartel” behavior has become increasingly common in recent years, with several instances being reported. Here, we propose an algorithm—named CIDRE—to detect anomalous groups of journals that exchange citations at excessively high rates when compared against a null model that accounts for scientific communities and journal size. CIDRE detects more than half of the journals suspended from Journal Citation Reports due to anomalous citation behavior in the year of suspension or in advance. Furthermore, CIDRE detects many new anomalous groups, where the impact factors of the member journals are lifted substantially higher by the citations from other member journals. We describe a number of such examples in detail and discuss the implications of our findings with regard to the current academic climate.

scholarly journals COVID-19 Pandemic Turns Life-Science Students into “Citizen Scientists”: Data Indicate Multiple Negative Effects of Urbanization on Biota

2021 ◽  
Vol 13 (5) ◽  
pp. 2992
Author(s):  
Jens Schirmel
Keyword(s):  
Citizen Science ◽  
Online Teaching ◽  
Teaching Strategy ◽  
Ecosystem Functions ◽  
Negative Effects ◽  
Ecological Data ◽  
Pollinating Insects ◽  
Impact Of Urbanization ◽  
Data Collections ◽  
The Impact

The COVID-19 pandemic and its restrictions strongly affect the higher education community and require diverse teaching strategies. We designed a course where we combined online teaching with independently conducted ecological data collections by students using a “citizen science” approach. The aim was to analyze the impact of urbanization on biota by comparing urban and rural grasslands. Seventy-five students successfully conducted the data collections and the results provide evidence for prevailing negative effects of urbanization. Individual numbers of ground-dwelling invertebrates (−25%) and pollinating insects (−33%) were generally lower in urban sites. Moreover, animal and seed predation were reduced in urban grasslands, indicating the potential of urbanization to alter ecosystem functions. Despite the general limitations of online teaching and citizen science approaches, outcomes of this course showed this combination can be a useful teaching strategy, which is why this approach could be used to more actively involve students in scientific research.

scholarly journals Impact of mapping errors on the reliability of landslide hazard maps

2002 ◽  
Vol 2 (1/2) ◽  
pp. 3-14 ◽  
Author(s):  
F. Ardizzone ◽  
M. Cardinali ◽  
A. Carrara ◽  
F. Guzzetti ◽  
P. Reichenbach
Keyword(s):  
Statistical Modelling ◽  
Landslide Hazard ◽  
Great Effort ◽  
Hazard Maps ◽  
Data Errors ◽  
Inventory Map ◽  
Terrain Unit ◽  
Discriminant Models ◽  
The Impact ◽  
Landslide Deposit

Abstract. Identification and mapping of landslide deposits are an intrinsically difficult and subjective operation that requires a great effort to minimise the inherent uncertainty. For the Staffora Basin, which extends for almost 300 km2 in the northern Apennines, three landslide inventory maps were independently produced by three groups of geomorphologists. In comparing each map with the others, large positional discrepancies arise (in the range of 55–65%). When all three maps are overlain, the locational mismatch of landslide deposit polygons increases to over 80%. To assess the impact of these errors on predictive models of landslide hazard, for the study area discriminant models were built up from the same set of geological-geomorphological factors as predictors, and the occurrence of landslide deposits within each terrain-unit, derived from each inventory map, as dependent variable. The comparison of these models demonstrates that statistical modelling greatly minimises the impact of input data errors which remain, however, a major limitation on the reliability of landslide hazard maps.

scholarly journals Sexual dimorphism in a top predator ( Notophthalmus viridescens ) drives aquatic prey community assembly

2018 ◽  
Vol 285 (1890) ◽  
pp. 20181717 ◽  
Author(s):  
Denon Start ◽  
Stephen De Lisle
Keyword(s):  
Sexual Dimorphism ◽  
Sex Ratio ◽  
Intraspecific Variation ◽  
Structure And Function ◽  
Sexually Dimorphic ◽  
Ecological Communities ◽  
Top Predator ◽  
Aquatic Mesocosms ◽  
And Function ◽  
The Impact

Intraspecific variation can have important consequences for the structure and function of ecological communities, and serves to link community ecology to evolutionary processes. Differences between the sexes are an overwhelmingly common form of intraspecific variation, but its community-level consequences have never been experimentally investigated. Here, we manipulate the sex ratio of a sexually dimorphic predacious newt in aquatic mesocosms, then track their impact on prey communities. Female and male newts preferentially forage in the benthic and pelagic zones, respectively, causing corresponding reductions in prey abundances in those habitats. Sex ratio differences also explained a large proportion (33%) of differences in the composition of entire pond communities. Ultimately, we demonstrate the impact of known patterns of sexual dimorphism in a predator on its prey, uncovering overlooked links between evolutionary adaptation and the structure of contemporary communities. Given the extreme prevalence of sexual dimorphism, we argue that the independent evolution of the sexes will often have important consequences for ecological communities.

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