Influence of Land Use on Mallard Nest-Structure Occupancy

Zicus, Michael C.; Rave, David P.; Das, Abhik; Riggs, Michael R.; Buitenwerf, M. L.

doi:10.2193/0022-541x(2006)70[1325:ioluom]2.0.co;2

Cited by 6 publications

(13 citation statements)

References 6 publications

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“…Fixed date Field N/A (Luttschwager, Higgins, & Jenks, 1994) Fixed date Field N/A (Greenwood, Pietruszewski, & Crawford, 1998) Fixed date Field N/A (Lapointe, Giroux, Belanger, & Filion, 2000) Fixed date Field N/A (Piest & Sowls, 1985) Fixed date Field N/A (Shaffer et al, 2006) Fixed date Field N/A (West & Messmer, 2006) Fixed date Field N/A (Bélanger & Picard, 1999) Fixed date Field N/A (Zicus, Rave, Das, Riggs, & Buitenwerf, 2006) Fixed date Field N/A (Carroll, Arnold, & Beam, 2007) Fixed date Field N/A (Kantrud, 1993) Fixed date Field N/A (Haffele, Eichholz, & Dixon, 2013) Fixed date Field N/A (Devries & Armstrong, 2011) Fixed date Field Positive effect-artificial nests (Butler & Rotella, 1998) No…”

Section: Timing Of Measurement Site Measured Effect On Nest Success Smentioning

confidence: 99%

Vegetation phenology and nest survival: Diagnosing heterogeneous effects through time

Ringelman

Skaggs

2019

Ecology and Evolution

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Birds should select nest sites that minimize predation risk, but understanding the influence of vegetation on nest survival has proven problematic. Specifically, the common practice of measuring vegetation on nest fate date can overestimate its effect on nest survival, simply because vegetation at hatched nests grows for a longer period of time than vegetation at nests that were depredated. Here, we sampled the literature to determine the prevalence of this bias in studies of duck breeding ecology. We then used survival data collected from ~2,800 duck nests to empirically evaluate evidence of bias in four different vegetation metrics: vegetation density measured when the nest was found, density when the nest was fated, and date‐corrected regression residuals of these two. We also diagnosed the magnitude of vegetation effects on nest survival by restricting our analysis to only nests which were fated contemporaneously (thereby removing potential bias in the timing of measurement). Finally, we examined whether systematic phenological differences exist between vegetation at hatched and depredated nests that have the potential to further obfuscate the relationship between vegetation and nest survival. We found evidence for a true‐positive effect of vegetation density on nest survival that appeared to be inflated when using raw vegetation measurements collected at fate date. However, taken in combination with the literature review, our results suggest that the majority of duck nesting studies have evaluated the role of vegetation on nest survival using a relatively less biased metric—vegetation density when the nest was found. Finally, we found that over the course of a nesting attempt, vegetation increased in density at successful nests, but decreased in density at depredated nests. As a consequence, duck researchers using vegetation data collected when the nest was found may actually be underestimating the magnitude of the effect. This seasonal change potentially points to an important new metric for understanding predation risk, but further experimental research is required to fully eliminate potential biases in the timing of vegetation measurements.

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Section: Timing Of Measurement Site Measured Effect On Nest Success Smentioning

confidence: 99%

Vegetation phenology and nest survival: Diagnosing heterogeneous effects through time

Ringelman

Skaggs

2019

Ecology and Evolution

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“…We used a data set of mallard nest structures and occupancy that was originally presented in Zicus, Fieberg & Rave (), Zicus et al . () and then used by Fieberg et al . () to illustrate the attenuation effect in marginal logistic regression.…”

Section: Example: Mallard Nest Structuresmentioning

confidence: 99%

“…This introductory example aims to illustrate differences between conditional and marginal model parameters for clustered data and to show how model parameters depend on the (cluster-specific) covariates included as fixed effects in the regression. We used a data set of mallard nest structures and occupancy that was originally presented in Zicus, Fieberg & Rave (2003), Zicus et al (2006) and then used by Fieberg et al (2009) to illustrate the attenuation effect in marginal logistic regression. The scope of the original study was to investigate factors that explained mallard nest structure occupancy in Western Minnesota, using a binary response that indicated whether a nest structure was used (1) or not (0).…”

Section: Example: Mallard Nest Structuresmentioning

confidence: 99%

Marginal or conditional regression models for correlated non‐normal data?

2016

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11. Correlated data are ubiquitous in ecological and evolutionary research, and appropriate statistical analysis requires that these correlations are taken into account. For regressions with correlated, non-normal outcomes, two main approaches are used: conditional and marginal modelling. The former leads to generalized linear mixed models (GLMMs), while the latter are estimated using generalized estimating equations (GEEs), or marginalized multilevel regression models. Differences, advantages and drawbacks of conditional and marginal models have been discussed extensively in the statistical and applied literature, and there is some agreement that the choice of the model must depend on the question under study.Yet, there still appears to be a lot of confusion and disagreement over when to choose which model. 2.We start with a review of conditional and marginal models, and the differences in the interpretation of the resulting parameter estimates. We highlight that the two types of models propagate different linear relations between the covariates and the response.Moreover, while conditional models explicitly account for heterogeneity among clustered observations, marginal models yield averages over such heterogeneities and are therefore often interpreted as population-averaged models. Therefore, marginal model parameters should not be loosely interpreted as population-averaged parameters. In addition, we explain 2 how marginal modelling is mathematically analogous to deliberately omitting covariates with explanatory power, and to deliberately introducing a Berkson measurement error into covariates. We also reiterate that marginal modelling is related to a well-known statistical phenomenon, the Simpson's paradox.4. In most cases, therefore, we regard the conditional model as the more powerful choice to explain how covariates are associated with a non-normal response. Still, marginal models can be useful, given that the scientific question explicitly requires such a model formulation.

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“…Cost-effectiveness could be related to the size of the open-water area in which a structure was erected because size of water area has been reported to influence structure occupancy rates (Zicus et al 2006). If structure failure also was related to size of open-water area, then higher structure occupancy might not yield greater costeffectiveness.…”

Section: Cost-effectiveness-effect Of Size Of Open-water Areamentioning

confidence: 99%

“…Occupancy rates and nest success, together with installation, maintenance, and repair costs will determine structure effectiveness. We evaluated 2 types of structures, those with a single versus a double grass-lined nest cylinder (Zicus et al 2006). Waterfowl managers in Minnesota, USA, perceived double-cylinder structures to be more cost-effective than single-cylinder structures, but they were uncertain (K. B. Kotts, Minnesota Department of Natural Resources, personal communication; L. Lewis, U.S.…”

mentioning

confidence: 99%

Cost-Effectiveness of Single- Versus Double-Cylinder Over-Water Nest Structures

Zicus¹,

Rave²,

Fieberg³

2006

Wildlife Society Bulletin

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Minnesota waterfowl management plans prescribe widespread deployment of mallard (Anas platyrhynchos) nest structures. We compared 53 single‐ and 57 double‐cylinder nest structures from 1996 to 2003 because managers used both structure types but were uncertain about their respective cost‐effectiveness. More nests occurred in double‐cylinder structures, but numbers of successful nests and hatched ducklings were comparable for both types. Nest success in single‐ and double‐cylinder structures was 92.8% and 79.4%, respectively, with nest abandonment being >4.5 times greater in doubles. Structure damage occurred only at ice‐out and was greater for doubles. However, relative risk of failure for double‐ versus single‐cylinder structures was similar (1.26; 95% CI = 0.91–1.75) and increased with size of the open‐water area containing the structure. Modeling indicated approximately 95% of recruits from nest structures were additional recruits. A case‐history approach indicated doubles produced an additional recruit for $23.11 versus $23.25 for singles. However, these estimates were sensitive to assumptions used to apportion costs between structure types and ignored structure‐placement influences. Placement affected cost‐effectiveness significantly, with structures placed in open‐water areas >10 ha being more cost‐effective. Results also suggested singles might be more effective than doubles when placement is considered. Lower nest abandonment alone might make single‐cylinder structures the better choice.

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Influence of Land Use on Mallard Nest-Structure Occupancy

Cited by 6 publications

References 6 publications

Vegetation phenology and nest survival: Diagnosing heterogeneous effects through time

Vegetation phenology and nest survival: Diagnosing heterogeneous effects through time

Marginal or conditional regression models for correlated non‐normal data?

Cost-Effectiveness of Single- Versus Double-Cylinder Over-Water Nest Structures

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