Miller L.M.,980 Folwell Avenue |
Miller L.M.,University of Minnesota |
Mero S.W.,1201 East Highway 2 |
Younk J.A.,114 Bemidji Avenue
Transactions of the American Fisheries Society | Year: 2012
Fish stocking, often from multiple source populations, is a common management practice frequently conducted without the means or effort to determine the reproductive contributions of stocked fish. Historically, the Minnesota Department of Natural Resources (MNDNR) has stocked four strains of muskellunge Esox masquinongy, but the contribution of these strains to current populations was unknown. Two strains came fromMinnesota lakes, Shoepack Lake and Leech Lake, and the other strains came fromWisconsin and Iowa hatcheries and were of uncertain origin. The MNDNR discontinued stocking the Shoepack strain in the 1980s when that strain displayed poor growth in stocked waters. Managers were concerned that ancestry from this strain might be limiting the genetic potential for muskellunge to attain trophy size in stocked populations. Using 13 microsatellite DNA markers, we determined the ancestry of muskellunge in 10 supplemented native populations and 10 introduced populations. The ancestry from each of the four stocked strains of muskellunge was detected in some populations, but the level of ancestry was unrelated to the amount of stocking of a strain. Ancestry from native populations persisted in six of the supplemented populations despite years of stocking. The potential effects of Shoepack strain ancestry on fish size were limited in most lakes because of its low persistence. All stocked strains reproduced in at least some of the lakes, but some lakes had no evidence of reproduction by any stocked strain. Our results will help MNDNR manage genetic diversity among muskellunge populations and direct efforts toward appropriate actions to improve size structure. This study reinforces how genetic data are often useful for evaluating ancestry in stocked fish populations, whereas stocking histories may be poor indicators of current genetic composition. © American Fisheries Society 2012.
Martin D.J.,Minnesota State University, Mankato |
Martin D.J.,Wildlife Research Center |
Mcmillan B.R.,Minnesota State University, Mankato |
Mcmillan B.R.,Brigham Young University |
And 4 more authors.
Journal of Mammalogy | Year: 2010
An understanding of activity patterns of wildlife in relation to abiotic and biotic factors enables biologists to better understand the ecology of species, manage resources, standardize survey methods, and serve as an index of the relative density of a species. River otters (Lontra canadensis) were radiotracked between June 2002 and October 2003. Using radiotracking data, we conducted exploratory analyses to determine relative influence of abiotic and biotic factors on 2 measures of activity of otters. Abiotic factors included air temperature, barometric pressure, lunar phase, biological season, and time of day; the biotic factor was sex. Activity was measured indirectly via movement rates and directly as the proportion of location attempts recorded as active (PLA). Movement rate was defined as the distance traveled by an otter between consecutive location estimates. Generalized linear mixed models were used to explore the influence of covariates on both measures of otter activity. The model best explaining variation in movement rate included biological season, sex, a season*sex interaction, and time of day. Males moved at greater rates than females during breeding and winter seasons but moved at similar rates to females during summer. Covariates found to account for most variation in the PLA of otters included time of day, season, and temperature. Otters were active throughout the day but with bimodal peaks in the PLA during late evening and early morning hours. The PLA of otters was highest during breeding season, lowest during winter, and intermediate in summer months. In addition, the PLA of otters decreased slightly with increasing temperature. Overall, the PLA of otters in our study area was influenced by abiotic factors, and movement rates of otters were influenced by abiotic and biotic factors. © 2010 American Society of Mammalogists.
Pierce R.B.,1201 East Highway 2 |
Carlson A.J.,601 Minnesota Drive |
Carlson B.M.,University of Michigan |
Hudson D.,U.S. Geological Survey |
Staples D.F.,463 C West Broadway
Transactions of the American Fisheries Society | Year: 2013
We monitored depths and temperatures used by large (>71-cm) versus small Northern Pike Esox lucius in three north-central Minnesota lakes with either acoustic telemetry or archival tags. Individual Northern Pike demonstrated flexibility in depths used within a season and between years. The fish had some tolerance for low levels of dissolved oxygen (<3 mg/L), but depth selection was generally constrained by low dissolved oxygen in summer and winter. The fish more fully exploited all available depths during winter and thermal turnover periods. During July and August, large Northern Pike tended to follow the thermocline into cooler water as upper water layers warmed. Selection ratios indicated that large Northern Pike preferred water temperatures of 16-21°C during August when temperatures up to 28°C were available. In two lakes providing dense overhead cover from water lilies in shallow water, small Northern Pike used warmer, shallower water compared with large fish during summer. In a third lake providing no such cover, small fish were more often in deeper, cooler water. For small Northern Pike, temperature seemed to be a secondary habitat consideration behind the presence of shallow vegetated cover. This study provided detailed temperature selection information that will be useful when considering temperature as an ecological resource for different sizes of Northern Pike. Received March 27, 2013; accepted June 27, 2013. © 2013 Copyright Taylor and Francis Group, LLC.
Pierce R.B.,1201 East Highway 2
North American Journal of Fisheries Management | Year: 2010
The effects of maximum, minimum, and slot length limits on the sizes and relative abundance of northern pike Esox lucius were evaluated in 22 Minnesota lakes. The regulations were implemented in 1989-1998 and lasted 9-15 years. As preregulation information was available back to the 1970s, the evaluation periods covered 21-37 years. Comparisons were made with reference populations from 47 ecologically similar lakes during the same extended period. Although the regulations did not achieve management objectives in every lake, the broader-scale, statewide finding was that they improved the size structure of northern pike populations but produced no consistent trends in relative abundance. The improvements were detected against the backdrop of reference populations that initially appeared to have similar sizes and relative abundances. Maximum length limits protecting fish over 20, 22, and 24 in produced significant long-term increases in the percentages of northern pike 24 in and longer and 30 in and longer compared with the reference populations. Lakes with 30-in minimum length limits had increased percentages of northern pike 20 in and longer, but the improvements did not carry over to fish 30 in and longer. A mix of slot length limits produced results that are more difficult to interpret but generally improved size structure. A metaanalysis incorporating all of the length regulations indicated that the changes in northern pike size structure in regulated lakes were very large for an ecological experiment. Length limits protected large northern pike, with the expectation that lower yields were an acceptable trade-off for producing larger fish for recreational fisheries. This study reveals the range and magnitude of responses we can reasonably expect from length limits as well as the substantial value of conserving large fish when the goal is improved population size structure. © American Fisheries Society 2010.
Giudice J.H.,Biometrics Unit |
Fieberg J.R.,Biometrics Unit |
Lenarz M.S.,1201 East Highway 2
Journal of Wildlife Management | Year: 2012
Sightability models are binary logistic-regression models used to estimate and adjust for visibility bias in wildlife-population surveys. Like many models in wildlife and ecology, sightability models are typically developed from small observational datasets with many candidate predictors. Aggressive model-selection methods are often employed to choose a best model for prediction and effect estimation, despite evidence that such methods can lead to overfitting (i.e., selected models may describe random error or noise rather than true predictor-response curves) and poor predictive ability. We used moose (Alces alces) sightability data from northeastern Minnesota (2005-2007) as a case study to illustrate an alternative approach, which we refer to as degrees-of-freedom (df) spending: sample-size guidelines are used to determine an acceptable level of model complexity and then a pre-specified model is fit to the data and used for inference. For comparison, we also constructed sightability models using Akaike's Information Criterion (AIC) step-down procedures and model averaging (based on a small set of models developed using df-spending guidelines). We used bootstrap procedures to mimic the process of model fitting and prediction, and to compute an index of overfitting, expected predictive accuracy, and model-selection uncertainty. The index of overfitting increased 13% when the number of candidate predictors was increased from three to eight and a best model was selected using step-down procedures. Likewise, model-selection uncertainty increased when the number of candidate predictors increased. Model averaging (based on R = 30 models with 1-3 predictors) effectively shrunk regression coefficients toward zero and produced similar estimates of precision to our 3-df pre-specified model. As such, model averaging may help to guard against overfitting when too many predictors are considered (relative to available sample size). The set of candidate models will influence the extent to which coefficients are shrunk toward zero, which has implications for how one might apply model averaging to problems traditionally approached using variable-selection methods. We often recommend the df-spending approach in our consulting work because it is easy to implement and it naturally forces investigators to think carefully about their models and predictors. Nonetheless, similar concepts should apply whether one is fitting 1 model or using multi-model inference. For example, model-building decisions should consider the effective sample size, and potential predictors should be screened (without looking at their relationship to the response) for missing data, narrow distributions, collinearity, potentially overly influential observations, and measurement errors (e.g., via logical error checks). © 2011 The Wildlife Society. Copyright © The Wildlife Society, 2011.