scholarly journals Tripartite networks show that keystone species can multitask

2021 ◽  
Author(s):  
Sérgio Timóteo ◽  
Jörg Albrecht ◽  
Beatriz Rumeu ◽  
Ana C. Norte ◽  
Anna Traveset ◽  
...  
Keyword(s):  
Meta Analysis ◽  
Species Abundance ◽  
Keystone Species ◽  
Conservation Strategies ◽  
Single Type ◽  
Functional Importance ◽  
Multiple Dimensions ◽  
Widespread Phenomenon ◽  
Interaction Types ◽  
Niche Dimension

The importance of keystone species is often defined based on a single type of interaction (e.g., keystone predator). However, it remains unclear whether this functional importance extends across interaction types. We conducted a global meta-analysis of interaction networks to examine whether species functional importance in one niche dimension is mirrored in other niche dimensions, and whether this is associated with interaction outcome (mutualistic/antagonistic) or intimacy (temporary/permanent). We show that the importance of keystone species is positively correlated across multiple dimensions of species' ecological niche, independently from species' abundance, interaction outcome or intimacy. This suggests that keystonness multidimensionality is a widespread phenomenon and can be used to identify keystone species across several interaction types, playing a central role in determining ecosystem resilience and defining conservation strategies.

scholarly journals Estimation accuracy of species abundance based on environmental DNA with relation to its production source, state, and assay methodology suggested by meta-analyses

2021 ◽  
Author(s):  
Toshiaki Jo ◽  
Hiroki Yamanaka
Keyword(s):  
Meta Analysis ◽  
Empirical Studies ◽  
Species Abundance ◽  
Environmental Dna ◽  
Efficient Estimation ◽  
Estimation Accuracy ◽  
Accurate Estimation ◽  
Abundance Estimation ◽  
Natural Environments ◽  
Promising Tool

Environmental DNA (eDNA) analysis is a promising tool for non-disruptive and cost-efficient estimation of species abundance. However, its practical applicability in natural environments is limited because it is unclear whether eDNA concentrations actually represent species abundance in the field. Although the importance of accounting for eDNA dynamics, such as transport and degradation, has been discussed, the influences of eDNA characteristics, including production source and state, and methodology, including collection and quantification strategy and abundance metrics, on the accuracy of eDNA-based abundance estimation were entirely overlooked. We conducted a meta-analysis using 56 previous eDNA literature and investigated the relationships between the accuracy (R2) of eDNA-based abundance estimation and eDNA characteristics and methodology. Our meta-regression analysis found that R2 values were significantly lower for crustaceans than fish, suggesting that less frequent eDNA production owing to their external morphology and physiology may impede accurate estimation of their abundance via eDNA. Moreover, R2 values were positively associated with filter pore size, indicating that selective collection of larger-sized eDNA, which is typically fresher, could improve the estimation accuracy of species abundance. Furthermore, R2 values were significantly lower for natural than laboratory conditions, while there was no difference in the estimation accuracy among natural environments. Our findings shed a new light on the importance of what characteristics of eDNA should be targeted for more accurate estimation of species abundance. Further empirical studies are required to validate our findings and fully elucidate the relationship between eDNA characteristics and eDNA-based abundance estimation.

Impacts of nitrogen deposition on terrestrial plant diversity: a meta-analysis in China

2019 ◽  
Vol 12 (6) ◽  
pp. 1025-1033 ◽  
Author(s):  
Wen-Juan Han ◽  
Jia-Yu Cao ◽  
Jin-Liang Liu ◽  
Jia Jiang ◽  
Jian Ni
Keyword(s):  
Species Richness ◽  
Plant Diversity ◽  
Meta Analysis ◽  
N Deposition ◽  
Shannon Index ◽  
N Addition ◽  
Terrestrial Plant ◽  
Multiple Dimensions ◽  
Negative Effect ◽  
Experiment Duration

AbstractAimsWith the global atmospheric nitrogen (N) deposition increasing, the effect of N deposition on terrestrial plant diversity has been widely studied. Some studies have reviewed the effects of N deposition on plant species diversity; however, all studies addressed the effects of N deposition on plant community focused on species richness in specific ecosystem. There is a need for a systematic meta-analysis covering multiple dimensions of plant diversity in multiple climate zones and ecosystems types. Our goal was to quantify changes in species richness, evenness and uncertainty in plant communities in response to N addition across different environmental and experimental contexts.MethodsWe performed a meta-analysis of 623 experimental records published in English and Chinese journals to evaluate the response of terrestrial plant diversity to the experimental N addition in China. Three metrics were used to quantify the change in plant diversity: species richness (SR), evenness (Pielou index) uncertainty (Shannon index).Important FindingsResults showed that (i) N addition negatively affected SR in temperate, Plateau zones and subtropical zone, but had no significant effect on Shannon index in subtropical zones; (ii) N addition decreased SR, Shannon index and Pielou index in grassland, and the negative effect of N addition on SR was stronger in forest than in grassland; (iii) N addition negatively affected plant diversity (SR, Shannon index and Pielou index) in the long term, whereas it did not affect plant diversity in the short term. Furthermore, the increase in N addition levels strengthened the negative effect of N deposition on plant diversity with long experiment duration; and (iv) the negative effect of ammonium nitrate (NH4NO3) addition on SR was stronger than that of urea (CO(NH2)2) addition, but the negative effect of NH4NO3 addition on Pielou index was weaker than that of CO(NH2)2 addition. Our results indicated that the effects of N addition on plant diversity varied depending on climate zones, ecosystem types, N addition levels, N type and experiment duration. This underlines the importance of integrating multiple dimensions of plant diversity and multiple factors into assessments of plant diversity to global environmental change.

A meta-analysis of species-abundance distributions

Oikos ◽  
2010 ◽  
Vol 119 (7) ◽  
pp. 1149-1155 ◽  
Author(s):  
Werner Ulrich ◽  
Marcin Ollik ◽  
Karl Inne Ugland
Keyword(s):  
Meta Analysis ◽  
Species Abundance ◽  
Species Abundance Distributions

scholarly journals Meta-Analysis with Robust Variance Estimation: Expanding the Range of Working Models

2020 ◽  
Author(s):  
James E Pustejovsky ◽  
Elizabeth Tipton
Keyword(s):  
Variance Estimation ◽  
Meta Analysis ◽  
Estimation Methods ◽  
Single Type ◽  
Dependence Structure ◽  
Dependent Effect ◽  
Working Models ◽  
Robust Variance ◽  
Robust Variance Estimation ◽  
Meta Regression

In prevention science and related fields, large meta-analyses are common, and these analyses often involve dependent effect size estimates. Robust variance estimation (RVE) methods provide a way to include all dependent effect sizes in a single meta-regression model, even when the nature of the dependence is unknown. RVE uses a working model of the dependence structure, but the two currently available working models are limited to each describing a single type of dependence. Drawing on flexible tools from multivariate meta-analysis, this paper describes an expanded range of working models, along with accompanying estimation methods, which offer benefits in terms of better capturing the types of data structures that occur in practice and improving the efficiency of meta-regression estimates. We describe how the methods can be implemented using existing software (the ‘metafor’ and ‘clubSandwich’ packages for R) and illustrate the approach in a meta-analysis of randomized trials examining the effects of brief alcohol interventions for adolescents and young adults.

scholarly journals Everything under control? Comparing Knepp Estate rewilding project with ‘traditional’ nature conservation

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0241160
Author(s):  
Benedict Dempsey
Keyword(s):  
Nature Reserve ◽  
Conservation Strategies ◽  
Risk And Uncertainty ◽  
Precise Control ◽  
Human Control ◽  
Multiple Dimensions ◽  
Increased Risk ◽  
Lack Of Control ◽  
High Control ◽  
The Uk

‘Rewilding’ is an increasingly prominent concept in conservation, but one that has attracted controversy. Debate frequently focuses on human ‘control’ over nature. ‘Traditional’ conservation has been presented as involving ‘high control,’ and rewilding as ‘low control.’ Opposition to rewilding often stems from a perceived lack of control and associated perception of increased risk and uncertainty. This paper explores the concept of control in conservation. I identify multiple dimensions of control (‘stabilisation’, ‘location’, ‘prediction’ and ‘outputs’), illustrating that control is not a simple, linear concept. I compare two ethnographic case studies: the Sussex Wildlife Trust’s Old Lodge nature reserve; and Knepp Estate, one of the most influential rewilding projects in the UK. I use them to test assertions made about control in ‘traditional’ conservation and ‘rewilding’. I outline how Old Lodge does not exert precise control in all respects, but involves elements of uncertainty and negotiation. I describe how Knepp’s model of rewilding reduces control in some dimensions but potentially increases it in others. I conclude that, while Knepp’s rewilding does represent a significant conceptual departure from ‘traditional’ conservation, it should not be characterised as an approach that reduces control in a simplistic way. Based on this analysis, I argue that reduction of control does not necessarily underpin the concept of rewilding. Rather, there is interplay between different control dimensions that combine to form multiple ‘configurations of control.’ Using a framework of ‘configurations of control’, debate about the place of rewilding in conservation can become less polarised, and instead involve an active discussion of what configuration of control is desired. This analysis has the potential to increase understanding of rewilding projects as part of plural conservation strategies, in the UK and globally.

scholarly journals Identifying indicator species in ecological habitats using Deep Optimal Feature Learning

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0256782
Author(s):  
Yiting Tsai ◽  
Susan A. Baldwin ◽  
Bhushan Gopaluni
Keyword(s):  
Deep Learning ◽  
Indicator Species ◽  
Prediction Accuracy ◽  
Species Abundance ◽  
Keystone Species ◽  
Feature Learning ◽  
Training Data ◽  
Support Vector ◽  
Vector Machines ◽  
Domain Expertise

Much of the current research on supervised modelling is focused on maximizing outcome prediction accuracy. However, in engineering disciplines, an arguably more important goal is that of feature extraction, the identification of relevant features associated with the various outcomes. For instance, in microbial communities, the identification of keystone species can often lead to improved prediction of future behavioral shifts. This paper proposes a novel feature extractor based on Deep Learning, which is largely agnostic to underlying assumptions regarding the training data. Starting from a collection of microbial species abundance counts, the Deep Learning model first trains itself to classify the selected distinct habitats. It then identifies indicator species associated with the habitats. The results are then compared and contrasted with those obtained by traditional statistical techniques. The indicator species are similar when compared at top taxonomic levels such as Domain and Phylum, despite visible differences in lower levels such as Class and Order. More importantly, when our estimated indicators are used to predict final habitat labels using simpler models (such as Support Vector Machines and traditional Artificial Neural Networks), the prediction accuracy is improved. Overall, this study serves as a preliminary step that bridges modern, black-box Machine Learning models with traditional, domain expertise-rich techniques.

scholarly journals How Responsive Is a Teacher’s Classroom Practice to Intervention? A Meta-Analysis of Randomized Field Studies

2019 ◽  
Vol 43 (1) ◽  
pp. 106-137 ◽  
Author(s):  
Rachel Garrett ◽  
Martyna Citkowicz ◽  
Ryan Williams
Keyword(s):  
Classroom Practice ◽  
Intervention Study ◽  
Meta Analysis ◽  
Field Studies ◽  
Randomized Experiments ◽  
Multiple Dimensions ◽  
Rigorous Research ◽  
Professional Development For Teachers ◽  
Federal Education Policy ◽  
Substantial Heterogeneity

While teacher effectiveness has been a particular focus of federal education policy, and districts allocate significant resources toward professional development for teachers, these efforts are guided by an unexplored assumption that classroom practice can be improved through intervention. Yet even assuming classroom practice is responsive, little information is available to inform stakeholder expectations about how much classroom practice may change through intervention, or whether particular aspects of classroom practice are more amenable to improvement. Moreover, a growing body of rigorous research evaluating programs with a focus on improving classroom practice provides a new opportunity to explore factors associated with changes in classroom practice, such as intervention, study sample, or contextual features. This study examines the question of responsiveness by conducting a meta-analysis of randomized experiments of interventions directed at classroom practice. Our empirical findings indicate that multiple dimensions of classroom practice improve meaningfully through classroom practice-directed intervention, on average, but also find substantial heterogeneity in the effects. Implications for practice and research are discussed.

scholarly journals Provisioning Ecosystem Services of Rhododendron-Rich Forests in the Western Himalayas

2020 ◽  
Vol 3 (1) ◽  
pp. 70
Author(s):  
Sanchi Singh ◽  
Sudipto Chatterjee
Keyword(s):  
Ecosystem Services ◽  
Keystone Species ◽  
Himalayan Region ◽  
Conservation Strategies ◽  
Sustainable Utilization ◽  
Western Himalayas ◽  
High Demand ◽  
Native Communities ◽  
Medicinal Value ◽  
Provisioning Ecosystem Services

Himalayan forests are an important component of the global biodiversity and play a crucial role in maintaining the ecosystem balance. The genera of Rhododendron belongs to the Ericaceae family and are found at an altitudinal range of 1500–3000 m in the Himalayan region. It acts as an important keystone species in the Himalayan ecosystem with high ecological and medicinal value. The present study focuses on highlighting the provisioning ecosystem services offered by the Rhododendron species, which provides a variety of services to the locals and its extraction for commercial utilization provides many livelihood opportunities for the Himalayan native communities. However, due to the high demand for Rhododendron products and services there has been a rampant harvest of the species in the Himalayan region posing a risk to the Rhododendrons which are an important keystone species for maintaining the Himalayan ecosystem. Hence our research lies in the assessment of the provisioning ecosystem services of the Rhododendron species and provides various conservation strategies for its sustainable utilization in the Western Himalayas.

scholarly journals Practical Lessons on 12-Lead ECG Classification: Meta-Analysis of Methods From PhysioNet/Computing in Cardiology Challenge 2020

2022 ◽  
Vol 12 ◽  
Author(s):  
Shenda Hong ◽  
Wenrui Zhang ◽  
Chenxi Sun ◽  
Yuxi Zhou ◽  
Hongyan Li
Keyword(s):  
Data Augmentation ◽  
Meta Analysis ◽  
Single Type ◽  
Training Strategy ◽  
Advantages And Disadvantages ◽  
The Common ◽  
Classification Tasks ◽  
Electrocardiogram Ecg ◽  
And Training ◽  
Better Than

Cardiovascular diseases (CVDs) are one of the most fatal disease groups worldwide. Electrocardiogram (ECG) is a widely used tool for automatically detecting cardiac abnormalities, thereby helping to control and manage CVDs. To encourage more multidisciplinary researches, PhysioNet/Computing in Cardiology Challenge 2020 (Challenge 2020) provided a public platform involving multi-center databases and automatic evaluations for ECG classification tasks. As a result, 41 teams successfully submitted their solutions and were qualified for rankings. Although Challenge 2020 was a success, there has been no in-depth methodological meta-analysis of these solutions, making it difficult for researchers to benefit from the solutions and results. In this study, we aim to systematically review the 41 solutions in terms of data processing, feature engineering, model architecture, and training strategy. For each perspective, we visualize and statistically analyze the effectiveness of the common techniques, and discuss the methodological advantages and disadvantages. Finally, we summarize five practical lessons based on the aforementioned analysis: (1) Data augmentation should be employed and adapted to specific scenarios; (2) Combining different features can improve performance; (3) A hybrid design of different types of deep neural networks (DNNs) is better than using a single type; (4) The use of end-to-end architectures should depend on the task being solved; (5) Multiple models are better than one. We expect that our meta-analysis will help accelerate the research related to ECG classification based on machine-learning models.

scholarly journals When Wolves Recolonize: Indirect Effects on the Small and Medium-Sized Mammal Community

2002 ◽  
Vol 26 ◽  
pp. 35-37
Author(s):  
Brian Miller ◽  
Hank Harlow
Keyword(s):  
Keystone Species ◽  
Natural Disturbance ◽  
Statistical Technique ◽  
Trophic Levels ◽  
Genetic Identification ◽  
Conservation Strategies ◽  
Ecosystem Structure ◽  
Large Carnivores ◽  
Abundance And Diversity ◽  
Indirect Impacts

The abundance and diversity of mammals will be greatly affected by a number of factors, including plant productivity, climate, natural disturbance, and disease. Of particular interest to conservation strategies, there is little known about the ecological role that carnivores play in maintaining ecosystem structure. Large carnivores were essentially eliminated from much of their range during the last century. Yet, a growing body of experimental evidence indicates that top carnivores are keystone species, and they play important roles in maintaining the health of Nature. The predatory activities of large carnivores produce effects that ripple through the trophic levels of an ecosystem and affect organisms that seem distantly removed, ecologically and taxonomically. But, few studies have examined the indirect impacts of predation across those trophic levels. Such studies have been deemed a high priority in the Greater Yellowstone Ecosystem. Presently, we are assessing the abundance of selected species of mammals at sites representing five major vegetation types found in the Grand Teton National Park. The five vegetation sites are sampled in areas with and without wolves. The size range of these mammals extends from voles/mice to coyotes. Small rodents are being assessed through standard capture/recapture techniques using Sherman traps. Carnivores are being estimated by genetic identification of scat. That method is non-invasive, and by walking transects two times, we can essentially estimate populations sizes using the Lincoln-Peterson statistical technique. Mammals that can not be easily trapped or identified through scat will be followed over time using indices of abundance.

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