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Updated: Feb 7, 2026

Necropsy-based Wild Fish Health Assessment
Published on: September 11, 2018
A habitat-based framework to predict the effects of agricultural drain maintenance on imperiled fishes
F A Montgomery1, S M Reid2, N E Mandrak1
1University of Toronto Scarborough, 1265 Military Trail, Scarborough, ON, M1C 1A4, Canada.
Abstract:
One third of the total global land viable for agricultural production has artificial drainage systems. These drainage systems can provide important habitat for fishes and, in some cases, imperiled fish species vulnerable to impact by drainage maintenance activities. A framework to provide quantitative assessments of the effects of maintenance activities on imperiled fish species is needed. In this study, a six-step habitat-based framework was developed to predict suitable habitat for two at-risk species in an agricultural drain: the Endangered Pugnose Shiner (Notropis anogenus) and the Special Concern Blackstripe Topminnow (Fundulus notatus). Using the framework, spatial models were developed to assess the effects of proposed drain maintenance on the overall amount of suitable habitat, habitat patch size, and connectivity of habitat patches. Maintenance had a significant impact on habitat connectivity, but did not significantly reduce the habitat size of isolated patches. The amount of suitable habitat available after maintenance fell below the minimum area for population viability (MAPV) for the Pugnose Shiner, but not the Blackstripe Topminnow. Future impact assessments of drain maintenance should incorporate population viability analysis, coupled with habitat patch analysis (patch size and connectivity), to quantitatively test consequences of proposed alteration to the viability of spatially structured populations.
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Prediction Intervals
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.

