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Wagner, Rüdiger; Dapper, Thomas; Schmidt, Hans-Heinrich

doi:10.1023/a:1017047022207

Cited by 32 publications

(5 citation statements)

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“…Several of the biological factors described above may also affect streams in a site-specific manner because streams may vary in their vulnerability to infections by parasites or invasions my non-native species (e.g., Kohler and Wiley 1997; Kohler and Hoiland 2001). Inter-annual variability may interact with seasonal variability by altering emergence times, and hastening or prolonging seasonal changes in flow and water chemistry (e.g., Wagner and others 2000). Although seasonal and spatial interactions were not addressed in this study, they may operate in a similar manner to inter-annual and spatial interactions, with first order sites being more acutely affected by summer drought than sites draining larger watersheds.…”

Section: Discussionmentioning

confidence: 99%

Long-Term Variability in Bioassessments: A Twenty-Year Study from Two Northern California Streams

Mazor

Purcell

Resh

2009

Environmental Management

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Long-term variability of bioassessments has not been well evaluated. We analyzed a 20-year data set (1984–2003) from four sites in two northern California streams to examine the variability of bioassessment indices (two multivariate RIVPACS-type O/E scores and one multimetric index of biotic integrity, IBI), as well as eight metrics. All sites were sampled in spring; one site was also sampled in summer. Variability among years was high for most metrics (coefficients of variation, CVs ranging from 16% to 246% in spring) but lower for indices (CVs of 22–26% for the IBI and 21–32% for O/E scores in spring), which resulted in inconsistent assessments of biological condition. Variance components analysis showed that the time component explained variability in all metrics and indices, ranging from 5% to 35% of total variance explained. The site component was large (i.e., >40%) for some metrics (e.g., EPT richness), but nearly absent from others (e.g., Diptera richness). Seasonal analysis at one site showed that variability among seasons was small for some metrics or indices (e.g., Coleoptera richness), but large for others (e.g., EPT richness, O/E scores). Climatic variables did not show consistent trends across all metrics, although several were related to the El Niño Southern Oscillation Index at some sites. Bioassessments should incorporate temporal variability during index calibration or include climatic variability as predictive variables to improve accuracy and precision. In addition, these approaches may help managers anticipate alterations in reference streams caused by global climate change and high climatic variability.

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Section: Discussionmentioning

confidence: 99%

Long-Term Variability in Bioassessments: A Twenty-Year Study from Two Northern California Streams

Mazor

Purcell

Resh

2009

Environmental Management

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“…Although ANN models are recognised to provide reliable predictions of river invertebrates [25, 36-38, 42, 43,48,49], a disadvantage of ANN models is their lack of explanations regarding the relative importance of each independent variable considered [35]. In this way, ANN models have been labelled as 'black box'.…”

Section: Discussionmentioning

confidence: 99%

Selecting Variables for Habitat Suitability of Asellus (Crustacea, Isopoda) by Applying Input Variable Contribution Methods to Artificial Neural Network Models

Mouton

Dedecker

Lek

et al. 2009

Environ Model Assess

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This study aimed to compare different methods to analyse the contribution of individual river characteristics to predict the abundance of Asellus (Crustacea, Isopoda). Six methods which provide the relative contribution and/or the contribution profile of the input variables of artificial neural network models were therefore compared: (1) the 'partial derivatives' method; (2) the 'weights' method; (3) the 'perturb' method; (4) the 'profile' method; (5) the 'classical stepwise' method; (6) the 'improved stepwise' method. Consequently, the key variables which affect the habitat preferences of Asellus could be identified. To evaluate the performance of the artificial neural network model, the model predictions were compared with the results of a multiple linear regression analysis. The dataset consisted of 179 samples, collected over a 3-year period in the Zwalm catchment in Flanders, Belgium. Twenty-four environmental variables as well as the log-transformed abundance of Asellus were used in this study. The different contribution methods seemed to give similar results concerning the order of importance of the input variables. Nevertheless, their diverse computation led to differences in sensitivity and stability of the methods and the derived outcomes on the habitat preferences. From an ecological point of view, the environmental variables describing the stream type (width, depth, stream order and distance to mouth) were the most significant variables for Asellus in the Zwalm catchment during the period 2000-2002 for all applied methods. Indirectly, one can conclude that the water quality is not the limiting factor for the survival of Asellus in the Zwalm catchment.

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“…One of the most effective currently used methods for processing experimental data is the neural network method [11]. It has been found in various biological and environmental studies that markedly better results can be achieved using a reasonable neural network model than using MLR [12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

“…ANNs have also been developed to predict the abundances of fish [23] and insects [24], including predicting population densities [12,15,17]. Many of these studies have produced good results [14,25]. Convolutional neural networks (CNNs) have recently been developed to overcome some of the disadvantages of traditional machine learning models.…”

Section: Introductionmentioning

confidence: 99%

Improved Prediction of Aquatic Beetle Diversity in a Stagnant Pool by a One-Dimensional Convolutional Neural Network Using Variational Autoencoder Generative Adversarial Network-Generated Data

Hu,

Jiang,

Jia

et al. 2023

Applied Sciences

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Building a reasonable model for predicting biodiversity using limited data is challenging. Expanding limited experimental data using a variational autoencoder generative adversarial network (VAEGAN) to improve biodiversity predictions for a region is a new strategy. Aquatic beetle diversity in a large >30-year-old artificial pool that had not had human interference in Nanshe Village (Dapeng Peninsula, Shenzhen City, Guangdong Province, China) was investigated. Eight ecological factors were considered. These were water temperature, salinity, pH, water depth, proportional area of aquatic plants, proportional area of submerged plants, water area, and water level. Field sampling was performed for 1 or 2 days in the middle or late part of each month for a year. A type D net was swept 10 times in the same direction in each ~1 m × ~1 m sample square, generating 132 datasets (experimental data). In total, 39 aquatic beetle species were collected, 19 of which were assigned to Hydrophilidae, 16 to Dytiscidae, 3 to Noteridae, and 1 to Gyrinidae. A one-dimensional convolutional neural network (1-D CNN) was used to assess and predict the grade of the number of individuals and the number of aquatic beetle species. The Bayesian-optimized 1-D CNN established using 112 experimental datasets as the training set and the other 20 datasets as validation and testing sets gave a 74.0% prediction accuracy for the grade of the number of individuals and a 70.0% prediction accuracy for the number of species. The impact of insufficient sample data on the model was assessed using a VAEGAN to expand the training set from 112 to 512 samples, and then the Bayesian-optimized 1-D CNN-based VAEGAN prediction model was re-established. This improved prediction accuracy for the grade of the number of individuals to 86.0% and for the number of species to 85.0%. The grade of the number of individuals’ prediction accuracy was 88.0% and the number of species’ prediction accuracy was 85.0% when the random effects of only obtaining a single individual of a species were excluded. The results indicated that the accuracy of the 1-D CNN in predicting the aquatic beetle species number and abundance from relevant environmental factors can be improved using a VAEGAN to expand the experimental data.

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Cited by 32 publications

References 0 publications

Long-Term Variability in Bioassessments: A Twenty-Year Study from Two Northern California Streams

Long-Term Variability in Bioassessments: A Twenty-Year Study from Two Northern California Streams

Selecting Variables for Habitat Suitability of Asellus (Crustacea, Isopoda) by Applying Input Variable Contribution Methods to Artificial Neural Network Models

Improved Prediction of Aquatic Beetle Diversity in a Stagnant Pool by a One-Dimensional Convolutional Neural Network Using Variational Autoencoder Generative Adversarial Network-Generated Data

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