scholarly journals How to find a metapopulationThis review is one of a series dealing with some aspects of the impact of habitat fragmentation on animals and plants. This series is one of several virtual symposia focussing on ecological topics that will be published in the Journal from time to time.

2007 ◽  
Vol 85 (10) ◽  
pp. 1031-1048 ◽  
Author(s):  
D.A. Driscoll
Keyword(s):  
Native Species ◽  
Field Data ◽  
Spatial Dynamics ◽  
Metapopulation Dynamics ◽  
Data Sets ◽  
Fragmented Landscapes ◽  
Metapopulation Theory ◽  
Metapopulation Models ◽  
The Impact ◽  
Analytical Approaches

Where habitat loss and fragmentation is severe, many native species are likely to have reduced levels of dispersal between remnant populations. For those species to avoid regional extinction in fragmented landscapes, they must undergo some kind of metapopulation dynamics so that local extinctions are countered by recolonisation. The importance of spatial dynamics for regional survival means that research into metapopulation dynamics is essential. In this review I explore the approaches taken to examine metapopulation dynamics, highlight the analytical methods used to get the most information out of field data, and discover some of the major research gaps. Statistical models, including Hanski’s incidence function model (IFM) are frequently applied to presence–absence data, an approach that is often strengthened using long-term data sets that document extinctions and colonisations. Recent developments are making the IFM more biologically realistic and expanding the range of situations for which the model is relevant. Although accurate predictions using the IFM seem unlikely, it may be useful for ranking management decisions. A key weakness of presence–absence modelling is that the mechanisms underlying spatial dynamics remain inferential, so combining modelling approaches with detailed demographic research is warranted. For species where very large data sets cannot be obtained to facilitate statistical modelling, a demographic approach alone or with stochastic modelling may be the only viable research angle to take. Dispersal is a central process in metapopulation dynamics. Research combining mark–recapture or telemetry methods with model-selection procedures demonstrate that dispersal is frequently oversimplified in conceptual and statistical metapopulation models. Dispersal models like the island model that underlies classic metapopulation theory do not approximate the behaviour of real species in fragmented landscapes. Nevertheless, it remains uncertain if additional biological realism will improve predictions of statistical metapopulation models. Genetic methods can give better estimates of dispersal than direct methods and take less effort, so they should be routinely explored alongside direct ecological methods. Recent development of metacommunity theory (communities connected by dispersal) emphasises a range of mechanisms that complement metapopulation theory. Taking both theories into account will enhance interpretation of field data. The extent of metapopulation dynamics in human modified landscapes remains uncertain, but we have a powerful array of field and analytical approaches for reducing this knowledge gap. The most informative way forward requires that many species are studied in the same fragmented landscape by applying a selection of approaches that reveal complementary aspects of spatial dynamics.

scholarly journals Dispersers’ habitat detection and settling abilities modulate the effect of habitat amount on metapopulation resilience

2021 ◽  
Author(s):  
Louise Riotte-Lambert ◽  
Fabien Laroche
Keyword(s):  
Animal Movement ◽  
Habitat Amount ◽  
Fragmented Landscapes ◽  
Habitat Patches ◽  
Shadow Effect ◽  
Metapopulation Theory ◽  
Metapopulation Models ◽  
Mechanistic Understanding ◽  
The Relationship

Abstract Context Metapopulation theory makes useful predictions for conservation in fragmented landscapes. For randomly distributed habitat patches, it predicts that the ability of a metapopulation to recover from low occupancy level (the “metapopulation capacity”) linearly increases with habitat amount. This prediction derives from describing the dispersal between two patches as a function of their features and the distance separating them only, without interaction with the rest of the landscape. However, if individuals can stop dispersal when hitting a patch (“habitat detection and settling” ability), the rest of habitat may modulate the dispersal between two patches by intercepting dispersers (which constitutes a “shadow” effect). Objectives We aim at evaluating how habitat detection and settling ability, and the subsequent shadow effect, can modulate the relationship between the metapopulation capacity and the habitat amount in the metapopulation. Methods Considering two simple metapopulation models with contrasted animal movement types, we used analytical predictions and simulations to study the relationship between habitat amount and metapopulation capacity under various levels of dispersers’ habitat detection and settling ability. Results Increasing habitat detection and settling ability led to: (i) larger metapopulation capacity values than expected from classic metapopulation theory and (ii) concave habitat amount–metapopulation capacity relationship. Conclusions Overlooking dispersers’ habitat detection and settling ability may lead to underestimating the metapopulation capacity and misevaluating the conservation benefit of increasing habitat amount. Therefore, a further integration of our mechanistic understanding of animals’ displacement into metapopulation theory is urgently needed.

Some remarks on surface multiple attenuation

Geophysics ◽  
1998 ◽  
Vol 63 (2) ◽  
pp. 772-789 ◽  
Author(s):  
William H. Dragoset ◽  
Željko Jeričević
Keyword(s):  
Field Data ◽  
Survey Design ◽  
Eigenvalue Decomposition ◽  
Data Sets ◽  
Data Set ◽  
Integer Multiple ◽  
Multiple Attenuation ◽  
Large Matrix ◽  
Straightforward Solution ◽  
The Impact

The surface multiple attenuation algorithm discussed in this paper is a prestack inversion of a surface‐recorded, 2-D wavefield that aims to remove all orders of all surface multiples present within the wavefield. Although the algorithm requires no assumptions or modeling regarding the positions and reflection coefficients of the multiple‐causing reflectors, it does require complete internal physical consistency between primary and multiple events—something that exists only in ideal 2-D data sets. In field data sets the physical consistency between primaries and multiples is disturbed by phenomena such as variations in the acquisition wavelet, cable feathering, cross‐line dip, a finite near offset, and unequal or too coarse spatial sampling in source and receiver coordinates. Careful survey design can minimize the impact of those phenomena on surface multiple attenuation. If it is not too large, trace extrapolation can solve the finite near‐offset problem. Minor adjustments to the algorithm allow processing of data for which the source and receiver intervals differ by an integer multiple, although for those and other acquisition geometries, trace interpolation may be preferred. In the f-x domain, surface multiple attenuation can be formulated as an equation whose straightforward solution involves the inversion of a large matrix that is a function of the acquisition wavelet. Since that wavelet is generally unknown, solving this matrix equation becomes an optimization problem. Many matrix inversions are needed to estimate the acquisition wavelet that leads to the best multiple suppression, rendering the straightforward solution to the surface multiple attenuation equation quite costly. We offer two alternative approaches. In our first approach we compute an eigenvalue decomposition of the large matrix, allowing the equation to be recast so that the wavelet dependency appears in a diagonal matrix for which repetitive inversion is trivial. In our second approach we begin by using the surface multiple attenuation algorithm with a fixed, approximately correct wavelet to compute the surface multiple wavefield. We then filter the predicted multiples adaptively to match the actual multiples in the original wavefield and subtract these filtered multiples from the original wavefield. The second approach is relatively inexpensive and to some extent can cope with physical inconsistencies between primaries and multiples caused by field data set imperfections.

USING FIELD DATA FOR THE EVALUATION OF THE IMPACT OF ULTRA-LOW FREQUENCY MOTIONS ON THE FATIGUE OF MOORING LINES

2019 ◽  
Author(s):  
Guilherme Borzacchiello ◽  
Carl Albrecht ◽  
Fabricio N Correa ◽  
Breno Jacob ◽  
Guilherme da Silva Leal
Keyword(s):  
Field Data ◽  
Low Frequency ◽  
Mooring Lines ◽  
The Impact

scholarly journals Land Subsidence Estimation for Aquifer Drainage Induced by Underground Mining

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4658
Author(s):  
Artur Guzy ◽  
Wojciech T. Witkowski
Keyword(s):  
Environmental Impact ◽  
Land Subsidence ◽  
Field Data ◽  
Underground Mining ◽  
Small Scale ◽  
Aquifer System ◽  
Maximum Value ◽  
The Impact ◽  
Method Model ◽  
Impact Of Mining

Land subsidence caused by groundwater withdrawal induced by mining is a relatively unknown phenomenon. This is primarily due to the small scale of such movements compared to the land subsidence caused by deposit extraction. Nonetheless, the environmental impact of drainage-related land subsidence remains underestimated. The research was carried out in the “Bogdanka” coal mine in Poland. First, the historical impact of mining on land subsidence and groundwater head changes was investigated. The outcomes of these studies were used to construct the influence method model. With field data, our model was successfully calibrated and validated. Finally, it was used for land subsidence estimation for 2030. As per the findings, the field of mining exploitation has the greatest land subsidence. In 2014, the maximum value of the phenomenon was 0.313 cm. However, this value will reach 0.364 m by 2030. The spatial extent of land subsidence caused by mining-induced drainage extends up to 20 km beyond the mining area’s boundaries. The presented model provided land subsidence patterns without the need for a complex numerical subsidence model. As a result, the method presented can be effectively used for land subsidence regulation plans considering the impact of mining on the aquifer system.

scholarly journals Investigating the impact of pre-processing techniques and pre-trained word embeddings in detecting Arabic health information on social media

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Yahya Albalawi ◽  
Jim Buckley ◽  
Nikola S. Nikolov
Keyword(s):  
Social Media ◽  
Deep Learning ◽  
Comprehensive Evaluation ◽  
Classification Problem ◽  
Data Sets ◽  
Word Embeddings ◽  
Data Set ◽  
Lower Accuracy ◽  
Health Related ◽  
The Impact

AbstractThis paper presents a comprehensive evaluation of data pre-processing and word embedding techniques in the context of Arabic document classification in the domain of health-related communication on social media. We evaluate 26 text pre-processings applied to Arabic tweets within the process of training a classifier to identify health-related tweets. For this task we use the (traditional) machine learning classifiers KNN, SVM, Multinomial NB and Logistic Regression. Furthermore, we report experimental results with the deep learning architectures BLSTM and CNN for the same text classification problem. Since word embeddings are more typically used as the input layer in deep networks, in the deep learning experiments we evaluate several state-of-the-art pre-trained word embeddings with the same text pre-processing applied. To achieve these goals, we use two data sets: one for both training and testing, and another for testing the generality of our models only. Our results point to the conclusion that only four out of the 26 pre-processings improve the classification accuracy significantly. For the first data set of Arabic tweets, we found that Mazajak CBOW pre-trained word embeddings as the input to a BLSTM deep network led to the most accurate classifier with F1 score of 89.7%. For the second data set, Mazajak Skip-Gram pre-trained word embeddings as the input to BLSTM led to the most accurate model with F1 score of 75.2% and accuracy of 90.7% compared to F1 score of 90.8% achieved by Mazajak CBOW for the same architecture but with lower accuracy of 70.89%. Our results also show that the performance of the best of the traditional classifier we trained is comparable to the deep learning methods on the first dataset, but significantly worse on the second dataset.

scholarly journals Metapopulation Patterns of Iberian Butterflies Revealed by Fuzzy Logic

Insects ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 392
Author(s):  
Antonio Pulido-Pastor ◽  
Ana Luz Márquez ◽  
José Carlos Guerrero ◽  
Enrique García-Barros ◽  
Raimundo Real
Keyword(s):  
Fuzzy Logic ◽  
Set Theory ◽  
Fuzzy Set Theory ◽  
Metapopulation Dynamics ◽  
Butterfly Species ◽  
Dynamics Analysis ◽  
Metapopulation Structure ◽  
Occurrence Data ◽  
Metapopulation Theory ◽  
The Cost

Metapopulation theory considers that the populations of many species are fragmented into patches connected by the migration of individuals through an interterritorial matrix. We applied fuzzy set theory and environmental favorability (F) functions to reveal the metapopulational structure of the 222 butterfly species in the Iberian Peninsula. We used the sets of contiguous grid cells with high favorability (F ≥ 0.8), to identify the favorable patches for each species. We superimposed the known occurrence data to reveal the occupied and empty favorable patches, as unoccupied patches are functional in a metapopulation dynamics analysis. We analyzed the connectivity between patches of each metapopulation by focusing on the territory of intermediate and low favorability for the species (F < 0.8). The friction that each cell opposes to the passage of individuals was computed as 1-F. We used the r.cost function of QGIS to calculate the cost of reaching each cell from a favorable patch. The inverse of the cost was computed as connectivity. Only 126 species can be considered to have a metapopulation structure. These metapopulation structures are part of the dark biodiversity of butterflies because their identification is not evident from the observation of the occurrence data but was revealed using favorability functions.

Survey Data Quality in Analyzing Harmonized Indicators of Protest Behavior: A Survey Data Recycling Approach

2021 ◽  
pp. 000276422110216
Author(s):  
Kazimierz M. Slomczynski ◽  
Irina Tomescu-Dubrow ◽  
Ilona Wysmulek
Keyword(s):  
Data Processing ◽  
Data Quality ◽  
Survey Data ◽  
A Priori ◽  
Data Sets ◽  
New Approach ◽  
Survey Quality ◽  
Survey Error ◽  
Ex Post ◽  
The Impact

This article proposes a new approach to analyze protest participation measured in surveys of uneven quality. Because single international survey projects cover only a fraction of the world’s nations in specific periods, researchers increasingly turn to ex-post harmonization of different survey data sets not a priori designed as comparable. However, very few scholars systematically examine the impact of the survey data quality on substantive results. We argue that the variation in source data, especially deviations from standards of survey documentation, data processing, and computer files—proposed by methodologists of Total Survey Error, Survey Quality Monitoring, and Fitness for Intended Use—is important for analyzing protest behavior. In particular, we apply the Survey Data Recycling framework to investigate the extent to which indicators of attending demonstrations and signing petitions in 1,184 national survey projects are associated with measures of data quality, controlling for variability in the questionnaire items. We demonstrate that the null hypothesis of no impact of measures of survey quality on indicators of protest participation must be rejected. Measures of survey documentation, data processing, and computer records, taken together, explain over 5% of the intersurvey variance in the proportions of the populations attending demonstrations or signing petitions.

scholarly journals Does agricultural intensification cause tipping points in ecosystem services?

2021 ◽  
Author(s):  
Stephen C. L. Watson ◽  
Adrian C. Newton ◽  
Lucy E. Ridding ◽  
Paul M. Evans ◽  
Steven Brand ◽  
...  
Keyword(s):  
Ecosystem Services ◽  
Agricultural Intensification ◽  
Spatial Dynamics ◽  
Policy Option ◽  
List Type ◽  
Tipping Points ◽  
Software Models ◽  
Non Linear ◽  
Study Interval ◽  
The Impact

Abstract Context Agricultural intensification is being widely pursued as a policy option to improve food security and human development. Yet, there is a need to understand the impact of agricultural intensification on the provision of multiple ecosystem services, and to evaluate the possible occurrence of tipping points. Objectives To quantify and assess the long-term spatial dynamics of ecosystem service (ES) provision in a landscape undergoing agricultural intensification at four time points 1930, 1950, 1980 and 2015. Determine if thresholds or tipping points in ES provision may have occurred and if there are any detectable impacts on economic development and employment. Methods We used the InVEST suite of software models together with a time series of historical land cover maps and an Input–Output model to evaluate these dynamics over an 85-year period in the county of Dorset, southern England. Results Results indicated that trends in ES were often non-linear, highlighting the potential for abrupt changes in ES provision to occur in response to slight changes in underlying drivers. Despite the fluctuations in provision of different ES, overall economic activity increased almost linearly during the study interval, in line with the increase in agricultural productivity. Conclusions Such non-linear thresholds in ES will need to be avoided in the future by approaches aiming to deliver sustainable agricultural intensification. A number of positive feedback mechanisms are identified that suggest these thresholds could be considered as tipping points. However, further research into these feedbacks is required to fully determine the occurrence of tipping points in agricultural systems.

scholarly journals A Two-Stage Joint Model for Nonlinear Longitudinal Response and a Time-to-Event with Application in Transplantation Studies

2012 ◽  
Vol 2012 ◽  
pp. 1-18 ◽  
Author(s):  
Magdalena Murawska ◽  
Dimitris Rizopoulos ◽  
Emmanuel Lesaffre
Keyword(s):  
Empirical Bayes ◽  
Cox Model ◽  
Data Sets ◽  
Two Stage ◽  
Nonlinear Mixed Effects Model ◽  
Resistance Evolution ◽  
Longitudinal Response ◽  
Longitudinal Measurements ◽  
Subject Specific ◽  
The Impact

In transplantation studies, often longitudinal measurements are collected for important markers prior to the actual transplantation. Using only the last available measurement as a baseline covariate in a survival model for the time to graft failure discards the whole longitudinal evolution. We propose a two-stage approach to handle this type of data sets using all available information. At the first stage, we summarize the longitudinal information with nonlinear mixed-effects model, and at the second stage, we include the Empirical Bayes estimates of the subject-specific parameters as predictors in the Cox model for the time to allograft failure. To take into account that the estimated subject-specific parameters are included in the model, we use a Monte Carlo approach and sample from the posterior distribution of the random effects given the observed data. Our proposal is exemplified on a study of the impact of renal resistance evolution on the graft survival.

scholarly journals Benchmarking microbiome transformations favors experimental quantitative approaches to address compositionality and sampling depth biases

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Verónica Lloréns-Rico ◽  
Sara Vieira-Silva ◽  
Pedro J. Gonçalves ◽  
Gwen Falony ◽  
Jeroen Raes
Keyword(s):  
Microbial Communities ◽  
Quantitative Methods ◽  
Microbial Load ◽  
Metagenomic Sequencing ◽  
Sampling Depth ◽  
Microbiome Research ◽  
Microbiome Data ◽  
The Impact ◽  
Analytical Approaches ◽  
Quantitative Approaches

AbstractWhile metagenomic sequencing has become the tool of preference to study host-associated microbial communities, downstream analyses and clinical interpretation of microbiome data remains challenging due to the sparsity and compositionality of sequence matrices. Here, we evaluate both computational and experimental approaches proposed to mitigate the impact of these outstanding issues. Generating fecal metagenomes drawn from simulated microbial communities, we benchmark the performance of thirteen commonly used analytical approaches in terms of diversity estimation, identification of taxon-taxon associations, and assessment of taxon-metadata correlations under the challenge of varying microbial ecosystem loads. We find quantitative approaches including experimental procedures to incorporate microbial load variation in downstream analyses to perform significantly better than computational strategies designed to mitigate data compositionality and sparsity, not only improving the identification of true positive associations, but also reducing false positive detection. When analyzing simulated scenarios of low microbial load dysbiosis as observed in inflammatory pathologies, quantitative methods correcting for sampling depth show higher precision compared to uncorrected scaling. Overall, our findings advocate for a wider adoption of experimental quantitative approaches in microbiome research, yet also suggest preferred transformations for specific cases where determination of microbial load of samples is not feasible.

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