Predictive modelling and spatial mapping of freshwater fish and decapod assemblages using GIS and neural networks

  • Joy M
  • Death R
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Abstract

1. We used stream fish and decapod spatial occurrence data extracted
from a national

database and recent surveys with geospatial landuse data, geomorphologic,
climatic, and

spatial data in a geographical information system (GIS) to model fish
and decapod

occurrence in the Wellington Region, New Zealand.

2. To predict the occurrence of each species at a site from a common
set of predictor

variables we used a multi-response, artificial neural network (ANN),
to produce a single

model that predicted the entire fish and decapod assemblage in one
procedure.

3. The predictions from the ANN using this landscape scale data proved
very accurate

based on evaluation metrics that are independent of species abundance
or probability

thresholds. The important variables contributing to the predictions
included the latitudinal

and elevational position of the site reach, catchment area, average
air temperature, the

vegetation type, landuse proportions of the catchment, and catchment
geology.

4. Geospatial data available for the entire regional river network
were then used to create a

habitat-suitability map for all 14 species over the regional river
network using a GIS. This

prediction map has many potential uses including: monitoring and predicting
temporal

changes in fish communities caused by human activities and shifts
in climate, identifying

areas in need of protection, biodiversity hotspots, and areas suitable
for the reintroduction

of endangered or rare species.

Author-supplied keywords

  • New Zealand
  • artificial neural networks
  • diadromous fish
  • freshwater fish
  • geographical information system
  • prediction maps
  • presence/absence

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Authors

  • M Joy

  • R Death

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