Developing a Standardized Sampling Program

Hayes, Daniel B.; Baker, E. A.; Bednarz, R.; Borgeson, David P.; Braunscheidel, J.; Harrington, A. D.; Hay, Rachel; Waybrant, J.; Breck, James E.; Nuhfer, Andrew J.; Bremigan, Mary T.; Lockwood, Roger N.; Schneider, James W.; Seelbach, Paul W.; Zorn, Troy G.

doi:10.1577/1548-8446(2003)28[18:dassp]2.0.co;2

Cited by 31 publications

(34 citation statements)

References 2 publications

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“…Recruitment variability can also obscure population trends (e.g., Peterman and Bradford 1987) and responses to management actions (e.g., Allen and Pine 2000). At the same time, measuring recruitment of hundreds to thousands of individual populations to set lake-specific management targets is impossible (Hayes et al 2003;Lester et al 2003;Fayram et al 2009). The capacity to predict recruitment accurately from widely available lake characteristics across a landscape can improve fisheries management by targeting management actions to locations where they are most likely to succeed.…”

Section: Introductionmentioning

confidence: 99%

Predicting walleye recruitment as a tool for prioritizing management actions

Hansen

Carpenter

Gaeta

et al. 2015

Can. J. Fish. Aquat. Sci.

155

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Abstract:We classified walleye (Sander vitreus) recruitment with 81% accuracy (recruitment success and failure predicted correctly in 84% and 78% of lake-years, respectively) using a random forest model. Models were constructed using 2779 surveys collected from 541 Wisconsin lakes between 1989 and 2013 and predictor variables related to lake morphometry, thermal habitat, land use, and fishing pressure. We selected predictors to minimize collinearity while maximizing classification accuracy and data availability. The final model classified recruitment success based on lake surface area, water temperature degree-days, shoreline development factor, and conductivity. On average, recruitment was most likely in lakes larger than 225 ha. Low degree-days also increased the probability of successful recruitment, but primarily in lakes smaller than 150 ha. We forecasted the probability of walleye recruitment in 343 lakes considered for walleye stocking; lakes with high probability of natural reproduction but recent history of recruitment failure were prioritized for restoration stocking. Our results highlight the utility of models designed to predict recruitment for guiding management decisions, provided models are validated appropriately.Résumé : Un modèle de forêt aléatoire pour la catégorisation du recrutement de dorés jaunes (Sander vitreus) s'est avéré exact dans 81 % des cas (prédiction correcte du succès ou de l'échec du recrutement pour 84 % et 78 % des années-lac, respectivement). Des modèles ont été élaborés à partir de 2779 évaluations obtenues pour 541 lacs du Wisconsin de 1989 à 2013, et de variables prédictives reliées à la morphométrie des lacs, à l'habitat thermique, à l'utilisation du sol et à la pression de pêche. Nous avons sélectionné les variables prédictives de manière à minimiser la colinéarité tout en maximisant l'exactitude de la catégorisation et la disponibilité des données. Le modèle final catégorisait le succès de recrutement en fonction de la superficie du lac, des degrés-jours de température du lac, d'un facteur d'aménagement des berges et de la conductivité. En moyenne, le recrutement était plus probable dans les lacs de plus de 225 ha. De faibles degrés-jours se traduisaient également par une probabilité accrue de succès du recrutement, mais principalement dans les lacs de moins de 150 ha. Nous avons prédit la probabilité de recrutement de dorés jaunes dans 343 lacs considérés comme candidats pour l'ensemencement de dorés; la priorité en ce qui concerne l'ensemencement aux fins de rétablissement a été donnée aux lacs présentant une forte probabilité de reproduction naturelle, mais un historique récent d'échec du recrutement. Nos résultats soulignent l'utilité de modèles conçus pour prédire le recrutement pour ce qui est d'orienter les décisions de gestion, pourvu que ces modèles soient validés adéquatement. [Traduit par la Rédaction]

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Section: Introductionmentioning

confidence: 99%

Predicting walleye recruitment as a tool for prioritizing management actions

Hansen

Carpenter

Gaeta

et al. 2015

Can. J. Fish. Aquat. Sci.

155

View full text Add to dashboard Cite

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“…Probability sampling, where every unit (e.g., lake) has a known and nonzero probability of selection, provides a representative sample of the target population and allows for statistically valid extrapolations to the entire population of interest (e.g., all lakes in the Northeast USA, Olsen et al 1999;Brown et al 2005). The advantages of PB sampling have been recognized and subsequently adopted in recent times by several state and federal management agencies to meet a variety of natural resource management needs (McDonald 2000;Hayes et al 2003), including the fulfillment of CWA mandates. The development and implementation of PB surveys is a shift away from the historical reliance on NPB surveys, which are often biased and may result in an inaccurate assessment of the target population (Peterson et al 1998(Peterson et al , 1999.…”

Section: Introductionmentioning

confidence: 99%

Quantifying sample biases of inland lake sampling programs in relation to lake surface area and land use/cover

Wagner

Soranno

Cheruvelil

et al. 2007

Environ Monit Assess

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We quantified potential biases associated with lakes monitored using non-probability based sampling by six state agencies in the USA (Michigan, Wisconsin, Iowa, Ohio, Maine, and New Hampshire). To identify biases, we compared state-monitored lakes to a census population of lakes derived from the National Hydrography Dataset. We then estimated the probability of lakes being sampled using generalized linear mixed models. Our two research questions were: (1) are there systematic differences in lake area and land use/land cover (LULC) surrounding lakes monitored by state agencies when compared to the entire population of lakes? and (2) after controlling for the effects of lake size, does the probability of sampling vary depending on the surrounding LULC features? We examined the biases associated with surrounding LULC because of the established links between LULC and lake water quality. For all states, we found that larger lakes had a higher probability of being sampled compared to smaller lakes. Significant interactions between lake size and LULC prohibit us from drawing conclusions about the main effects of LULC; however, in general lakes that are most likely to be sampled have either high urban use, high agricultural use, high forest cover, or low wetland cover. Our analyses support the assertion that data derived from non-probability-based surveys must be used with caution when attempting to make generalizations to the entire population of interest, and that probability-based surveys are needed to ensure unbiased, accurate estimates of lake status and trends at regional to national scales.

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“…Therefore, it will be necessary for management agencies to have well defined goals with respect to the target species and the spatial scale of management prior to the development of a classification system. Furthermore, for regional management to be effective, agencies must design and implement statistically valid sampling programs with standardized sampling protocols (Hayes et al, 2003). Our analysis also demonstrates that ecoregions or watersheds (i.e., HUs), are not effective in grouping lakes with similar fish growth rates.…”

Section: Discussionmentioning

confidence: 91%

“…Also, the clear identification of mechanisms and processes responsible for the observed patterns is not possible in our study (Peters et al, 1991). Given these limitations, however, we were still able to account for significant withinecoregion variation in mean length at age using lake morphometry and water quality characteristics in 11 out of 14 analyses, suggesting that the use of data collected from a statistically valid sampling program (Hayes et al, 2003) will likely provide further insight into the effects of lake morphometry and water quality on fish growth. Although there are limitations to the use of historic data, this approach also has advantages.…”

Section: Study Limitationsmentioning

confidence: 92%

A Multilevel Modeling Approach to Assessing Regional and Local Landscape Features for Lake Classification and Assessment of Fish Growth Rates

Wagner

Bremigan

Cheruvelil

et al. 2006

Environ Monit Assess

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The ecoregion and watershed frameworks are landscape-based classifications that have been used to group waterbodies with respect to measures of community structure; however, they have yet to be evaluated for grouping lakes for demographic characteristics of fish populations. We used a multilevel modeling approach to determine if variability in mean fish length at age could be partitioned by ecoregions and watersheds. For the ecoregions analysis, we then examined if within-ecoregion variability could be explained by local water quality and lake morphometry characteristics. We used data from agency surveys conducted during 1974-1984 for age 2 and 3 fish of seven common warm and coolwater fish species. Variance in mean length at age between ecoregions for all species was not significant, and between-watershed variance estimates were only significant in 3 out of 14 analyses; however, the total amount of variation between watersheds was very small (ranging from 1.8% to 3.7% of the total variance), indicating that ecoregions and watersheds were ineffective in partitioning variability in mean length at age. Within ecoregions, water quality and lake morphometric characteristics accounted for 2%-23% of the variation in mean length at age. Measures of lake productivity were the most common significant covariates, with mean length at age increasing with increasing lake productivity. Much of the variability in mean length at age was not accounted for, suggesting that other local factors such as biotic interactions, fish density, and exploitation are important. The results indicate that the development of an effective regional framework for managing inland lakes will require a substantial effort to understand sources of demographic variability and that managers should not rely solely on ecoregions or watersheds for grouping lakes with similar growth rates.

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Developing a Standardized Sampling Program

Cited by 31 publications

References 2 publications

Predicting walleye recruitment as a tool for prioritizing management actions

Predicting walleye recruitment as a tool for prioritizing management actions

Quantifying sample biases of inland lake sampling programs in relation to lake surface area and land use/cover

A Multilevel Modeling Approach to Assessing Regional and Local Landscape Features for Lake Classification and Assessment of Fish Growth Rates

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