Modeling Habitat Associations for the Common Loon (Gavia immer) at Multiple Scales in Northeastern North America

Kuhn, Anne; Copeland, Jane; Cooley, John H.; Vogel, Harry S.; Taylor, Kate M.; Nacci, Diane; August, Peter V.

doi:10.5751/ace-00451-060104

Cited by 17 publications

(15 citation statements)

References 80 publications

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“…Waterbirds also responded to roads and mines/settlements, albeit less frequently and with smaller effect sizes, although it must be considered that the study area was generally characterized by very low densities of roads or mines/settlements. In general, modeled responses were as expected, with a positive response by synanthropic species (Mallard, Canada Goose) and avoidance of roads by less tolerant species such as the Common Loon, in accordance with a recent long-term study in New Hampshire, USA (Kuhn et al 2011).…”

Section: Spatial Variation: Habitat Disturbancessupporting

confidence: 88%

Fire, humans, and climate: modeling distribution dynamics of boreal forest waterbirds

Börger

Nudds

2014

Ecological Applications

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Understanding the effects of landscape change and environmental variability on ecological processes is important for evaluating resource management policies, such as the emulation of natural forest disturbances. We analyzed time series of detection/nondetection data using hierarchical models in a Bayesian multi-model inference framework to decompose the dynamics of species distributions into responses to environmental variability, spatial variation in habitat conditions, and population dynamics and interspecific interactions, while correcting for observation errors and variation in sampling regimes. We modeled distribution dynamics of 14 waterbird species (broadly defined, including wetland and riparian species) using data from two different breeding bird surveys collected in the Boreal Shield ecozone within Ontario, Canada. Temporal variation in species occupancy (2000-2006) was primarily driven by climatic variability. Only two species showed evidence of consistent temporal trends in distribution: Ring-necked Duck (Aythya collaris) decreased, and Red-winged Blackbird (Agelaius phoeniceus) increased. The models had good predictive ability on independent data over time (1997-1999). Spatial variation in species occupancy was strongly related to the distribution of specific land cover types and habitat disturbance: Fire and forest harvesting influenced occupancy more than did roads, settlements, or mines. Bioclimatic and habitat heterogeneity indices and geographic coordinates exerted negligible influence on most species distributions. Estimated habitat suitability indices had good predictive ability on spatially independent data (Hudson Bay Lowlands ecozone). Additionally, we detected effects of interspecific interactions. Species responses to fire and forest harvesting were similar for 13 of 14 species; thus, forest-harvesting practices in Ontario generally appeared to emulate the effects of fire for waterbirds over timescales of 10-20 years. Extrapolating to all 84 waterbird species breeding on the Ontario Boreal Shield, however, suggested that up to 30 species may instead have altered (short-term) distribution dynamics due to forestry practices. Hence, natural disturbances are critical components of the ecology of the boreal forest and forest practices which aim to approximate them may succeed in allowing the maintenance of the associated species, but improved monitoring and modeling of large-scale boreal forest bird distribution dynamics will be necessary to resolve existing uncertainties, especially on less-common species.

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Section: Spatial Variation: Habitat Disturbancessupporting

confidence: 88%

Fire, humans, and climate: modeling distribution dynamics of boreal forest waterbirds

Börger

Nudds

2014

Ecological Applications

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“…Vertical lines identify the scales selected for single-scale models. tent with the results of previous studies, where ROC analyses suggested we could expect at most a 0.05 increase in the AUC for MS models over SS models (Boscolo and Metzger 2009, Graf et al 2005, Kuhn et al 2011. In previous studies, such differences were interpreted as support for MS over SS models; however, the wide variability between cross-validation trials in our study suggests we should not interpret our results in this manner.…”

Section: Comparison Of Single-scale and Multi-scale Models (Objective 2)supporting

confidence: 79%

“…Estimation of a scale of effect using empirical data is complicated when there are multiple landscape predictors because it is not clear whether the scale should be estimated independently for each predictor (hereafter referred to as ''multi-scale'' [MS] models) or if all landscape variables should be measured at a common scale (''single-scale'' [SS] models). Literature to date suggests that MS models can outperform SS models (Graf et al 2005, Boscolo and Metzger 2009, Kuhn et al 2011, implying that scales of effect are predictor specific. This may occur if different environmental conditions are important at different life stages; for example, one landscape feature may limit dispersal success while another may influence foraging opportunities within the home range, leading to a larger scale of effect for the first predictor.…”

Section: Introductionmentioning

confidence: 99%

Measuring and selecting scales of effect for landscape predictors in species–habitat models

Martin

Fahrig

2012

Ecological Applications

112

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Wildlife managers often use habitat models to determine species habitat requirements and to identify locations for conservation efforts, uses which depend on accurate specification of species-habitat relationships. Prior study suggests that model performance may be influenced by the way we measure environmental predictors. We hypothesized that species responses to landscape predictors are best represented by landscape composition-based measurements, rather than distance-based measurements. We also hypothesized that models using empirical data to select an appropriate scale of effect for each habitat predictor (multi-scale models) should perform better than models that assume a common scale of effect for all predictors (single-scale models). To test these hypotheses we constructed habitat models for three mammal species, Mephitis mephitis, Mustela erminea, and Procyon lotor, based on surveys conducted in 80 landscapes in southeastern Ontario, Canada. For each species we compared the performance of distance- and composition-based measurements, and we compared the performance of single- and multi-scale models. The composition-based measurement, measured at its empirically determined scale of effect, had greater explanatory power than the distance-based measurement of a given predictor more often than expected by chance, supporting our first hypothesis. Contrary to expectation, multi-scale models did not have better explanatory power or predictive performance relative to single-scale models. We identified and evaluated four potential mechanisms to explain this, and, depending on the species, we found that the best explanation was either that predictors have significant effects at a common scale or that, although the modeled effects were at multiple scales, they were of similar magnitude and direction at the scales modeled in single- and multi-scale models. Our results suggest that habitat modeling based on distance-based measurements could be improved by including composition-based measurements of landscape predictor variables, but that inclusion of predictor-specific scales of effect for composition-based measurements does not necessarily improve performance over models including composition-based measurements at a single scale. Conservation and wildlife management may be simplified when single-scale models perform as well as multi-scale models, as this suggests actions conducted at a single scale may address management objectives as well as actions taken at different scales for different landscape features.

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“…5). This result may partially explain why breeding Common Loons prefer to settle on larger compared to smaller lakes (e.g., Kuhn et al 2011) because larger lakes often have higher pHs and more food than smaller lakes (e.g., Rago and Wiener 1986, Matuszek and Beggs 1988, McNicol et al 1995.…”

Section: Discussionmentioning

confidence: 96%

Common Loon Reproductive Success in Canada: the West is Best but Not for Long

Tozer¹,

Falconer²,

Badzinski³

2013

ACE

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ABSTRACT. Reproductive success of Common Loons (Gavia immer) is a powerful indicator of aquatic ecosystem health, especially in relation to mercury and acid precipitation. We examined relationships between Common Loon reproductive success and longitude, year, lake area, and pH across southern Canada using data collected from 1992 to 2010 by participants in Bird Studies Canada's Canadian Lakes Loon Survey. Our goal was to indirectly describe the health of lakes in southern Canada with respect to mercury and acid precipitation. The overall model-predicted number of six-week-old young per pair per year was 0.59 (95% confidence limits: 0.56-0.62). Six-week-old young per pair per year decreased by 0.19 from west-to-east (−127° to −52° longitude), decreased by 0.14 between 1992 and 2010, increased by 0.22 as lake area increased from 10 to 3000 ha, and increased by 0.43 as acidity decreased from pH 5 to 9. The relationships were likely linked to acid-and temperature-mediated exposure to methylmercury and/or acid-induced reductions in forage fish. The temporal decrease

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Modeling Habitat Associations for the Common Loon (Gavia immer) at Multiple Scales in Northeastern North America

Cited by 17 publications

References 80 publications

Fire, humans, and climate: modeling distribution dynamics of boreal forest waterbirds

Fire, humans, and climate: modeling distribution dynamics of boreal forest waterbirds

Measuring and selecting scales of effect for landscape predictors in species–habitat models

Common Loon Reproductive Success in Canada: the West is Best but Not for Long

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