The use of artificial neural networks to assess fish abundance and spatial occupancy in the littoral zone of a mesotrophic lake

Brosse, Sébastien; Guégan, Jean‐François; Tourenq, J. N.; Lek, Sovan

doi:10.1016/s0304-3800(99)00110-6

Cited by 122 publications

(65 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Gevrey et al (2003), Dedecker et al (2005bDedecker et al ( , 2007 and Beauchard et al (2003) used the 'PaD' method Dimopoulos et al 1999) which consists in a calculation of the partial derivatives of the output according to the input variables. Several authors (Brosse et al 1999(Brosse et al , 2001Dedecker et al 2005bDedecker et al , 2007Gevrey et al 2003;Mastrorillo et al 1997b;Olden and Jackson 2002) applied Garson's algorithm (Garson 1991;Goh 1995). This algorithm is based on a computation using the connection weights.…”

Section: Model Interpretationmentioning

confidence: 99%

Applications of artificial neural networks predicting macroinvertebrates in freshwaters

et al. 2007

Self Cite

View full text Add to dashboard Cite

To facilitate decision support in freshwater ecosystem protection and restoration management, habitat suitability models can be very valuable. Data driven methods such as artificial neural networks (ANNs) are particularly useful in this context, seen their time-efficient development and relatively high reliability. However, specialized and technical literature on neural network modelling offers a variety of model development criteria to select model architecture, training procedure, etc. This may lead to confusion among ecosystem modellers and managers regarding the optimal training and validation methodology. This paper focuses on the analysis of ANN development and application for predicting macroinvertebrate communities, a species group commonly used in freshwater assessment worldwide. This review reflects on the different aspects regarding model development and application based on a selection of 26 papers reporting the use of ANN models for the prediction of macroinvertebrates. This analysis revealed that the applied model training and validation methodologies can often be improved and moreover crucial steps in the modelling process are often poorly documented. Therefore, suggestions to improve model development, assessment and application in ecological river management are presented. In particular, data pre-processing determines to a high extent the reliability of the induced models and their predictive relevance. This also counts for the validation criteria, that need to be better tuned to the practical simulation requirements. Moreover, the use of sensitivity methods can help to extract knowledge on the habitat preference of species and allow peer-review by ecological experts. The selection of relevant input variables remains a critical challenge as well. Model coupling is a missing crucial step to link human activities, hydrology, physical habitat conditions, water quality and ecosystem status. This last aspect is probably the most valuable aspect to enable decision support in water management based on ANN models.

show abstract

Section: Model Interpretationmentioning

confidence: 99%

Applications of artificial neural networks predicting macroinvertebrates in freshwaters

et al. 2007

Self Cite

View full text Add to dashboard Cite

show abstract

“…Any positive relationship between habitat and abundance determined from research surveys would suggest that the amount of habitat is related to juvenile production. To explore the empirical relationships between suitable habitat availability and fishery productivity (e.g., Brosse et al, 1999;Holbrook et al, 2000;VanDerWal et al, 2009) we plotted the relationship between SHbased shares and commercial landings in recent and historical fisheries (abundance-based shares [landings]). This approach allowed us to test the feasibility of using the 1:1 baseline and to model suitable habitat shares as a proxy for production and to explore the potential for similarities between adult and juvenile halibut distributions.…”

Section: Discussionmentioning

confidence: 99%

“…In practice, this concept has been documented in multiple marine systems (Swain and Wade, 1993;Marshall and Frank, 1995;Rangeley and Kramer, 1998;Shackell et al, 2005). There is also extensive evidence that range size and abundance of a species are correlated, and that availability of suitable habitat is strongly and positively correlated with total abundance (Gaston and Blackburn, 1996;Brosse et al, 1999;Holbrook et al, 2000;VanDerWal et al, 2009).…”

Section: Figurementioning

confidence: 99%

Untitled

2018

View full text Add to dashboard Cite

Abstract-Atlantic halibut (Hippoglossus hippoglossus) is an increasingly valued commercial fish and its recent population growth has attracted the attention of fisheries scientists and managers both within and outside Canada. With renewed transnational interests in this species, fishery managers have relied on existing, but limited, ecological information for multilateral discussions on shared allocations of catch. To address this dearth of information, we modeled species distribution using maximum entropy, where by survey catch data were related to environmental data to quantify habitat suitability. We then calculated

show abstract

“…Fishing procedures and equipment differ depending upon the water depth and wetted width of the sampling site. Point abundance sampling by electrofishing (PASE) is a frequently used sampling method to define fish habitat; however, size selectivity and fish escapement patterns might be of concern (e.g., Persat and Copp 1988;Brosse et al 1999).…”

Section: Empirical and Statistical Techniques For Estimating Habitat mentioning

confidence: 99%

Aquatic Habitat Modeling in Running Waters

Melcher

Hauer

Zeiringer

2018

Riverine Ecosystem Management

View full text Add to dashboard Cite

The use of artificial neural networks to assess fish abundance and spatial occupancy in the littoral zone of a mesotrophic lake

Cited by 122 publications

References 27 publications

Applications of artificial neural networks predicting macroinvertebrates in freshwaters

Applications of artificial neural networks predicting macroinvertebrates in freshwaters

Untitled

Aquatic Habitat Modeling in Running Waters

Contact Info

Product

Resources

About