Three Axes of Ecological Studies

Reynolds‐Hogland, Melissa J.; Mitchell, Michael S.

doi:10.1007/978-0-387-45447-4_11

Cited by 7 publications

(19 citation statements)

References 24 publications

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“…Scale effects could be larger than expected and completely change the conclusions and significance of your results (Long et al. ; Reynolds‐Hogland and Mitchell ). Don't ignore organismal extent.…”

Section: Recommendations For Research Involving Dietary Datamentioning

confidence: 83%

“…Most proxies are also used out of operational necessity not for a preferred temporal scale of investigation (Reynolds‐Hogland and Mitchell ). While feeding observations are commonly employed to study extant animals, the proxy is impossible to apply to fossil species excepting what can be gleaned from prehistoric cave art (Guthrie ).…”

Section: Toward An Explicit Cross‐scale Dietary Frameworkmentioning

confidence: 99%

“…Diagrammatic example based off of Reynolds‐Hogland and Mitchell () showing how changes in the temporal grain of an investigation can completely alter the conclusions of a study. (A) Logging seems to have no long‐term effect on American black bear ( Ursus americanus ) foraging patterns but a better understanding of the study system shows that yearly averages are not the correct scale to capture the biological and behavioral changes that logging brings.…”

Section: Toward An Explicit Cross‐scale Dietary Frameworkmentioning

confidence: 99%

“…This will dictate the necessary proxy extent, organismal extent, and grain of your dietary proxies (Reynolds‐Hogland and Mitchell ; Bennington et al. ; Thomas and Crowther ).…”

Section: Recommendations For Research Involving Dietary Datamentioning

confidence: 99%

“…) (Reynolds‐Hogland and Mitchell ). Often, we do not know at what scale an ecological phenomena operates over a priori or we are handed the scales we must use by taphonomic or logistical realities (Reynolds‐Hogland and Mitchell ; Bennington et al. ).…”

Section: Recommendations For Research Involving Dietary Datamentioning

confidence: 99%

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The temporal scale of diet and dietary proxies

Davis

Pineda‐Munoz

2016

Ecology and Evolution

129

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Diets estimated from different proxies such as stable isotopes, stomach contents, and dental microwear often disagree, leading to nominally well‐supported but greatly differing estimates of diet for both extinct and extant species that complicate our understanding of ecology. We show that these perceived incongruences can be caused by proxies recording diet over vastly different timescales. Field observations reveal a diet averaged over minutes or hours, whereas dental morphology may reflect the diet of a lineage over millions of years of evolution. Failing to explicitly consider the scale of proxies and the potentially large temporal variability in diet can cause erroneous predictions in any downstream analyses such as conservation planning or paleohabitat reconstructions. We propose a cross‐scale framework for conceptualizing diet suitable for both modern ecologists and paleontologists and provide recommendations for any studies involving dietary data. Treating diet in this temporally explicit framework and matching the scale of our questions with the scale of our data will lead to a much richer and clearer understanding of ecological and evolutionary processes.

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Section: Recommendations For Research Involving Dietary Datamentioning

confidence: 83%

Section: Toward An Explicit Cross‐scale Dietary Frameworkmentioning

confidence: 99%

Section: Toward An Explicit Cross‐scale Dietary Frameworkmentioning

confidence: 99%

“…This will dictate the necessary proxy extent, organismal extent, and grain of your dietary proxies (Reynolds‐Hogland and Mitchell ; Bennington et al. ; Thomas and Crowther ).…”

Section: Recommendations For Research Involving Dietary Datamentioning

confidence: 99%

Section: Recommendations For Research Involving Dietary Datamentioning

confidence: 99%

See 3 more Smart Citations

The temporal scale of diet and dietary proxies

Davis

Pineda‐Munoz

2016

Ecology and Evolution

129

View full text Add to dashboard Cite

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Mapping and quantifying land cover dynamics using dense remote sensing time series with the user-friendly pyNITA software

Alonzo

Hoek

Murillo-Sandoval

et al. 2021

Environmental Modelling & Software

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Testing Huston's dynamic equilibrium model along fire and forest productivity gradients using avian monitoring data

Janousek

Dreitz

2020

Diversity and Distributions

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Aim Many studies investigating the response of wildlife to disturbance focus predominantly on the effects of the disturbance alone but fail to account for the influence of ecosystem productivity in moderating the response of species and thus the resulting biodiversity patterns. We use Huston's dynamic equilibrium model (DEM) to explore the relationship between avian diversity and fire across the greater Rocky Mountain region of the western United States. This model provides the theoretical foundation to understand the distinct and interactive effects disturbance and productivity have on regulating species richness. Location We used avian monitoring data collected at 120 sites across Colorado, Idaho, Montana and Wyoming in the western United States from 2008 to 2016. Methods We used a Bayesian hierarchical multispecies occupancy model to predict species richness across fire and productivity gradients. Hierarchical models enable inference at the community and species level, accounting for imperfect detection, and providing a more accurate assessment of the ecological relationships. Results We found support of the hypotheses described by the DEM Avian species richness changed little across the fire severity gradient alone, though this relationship varies considerably when including the interaction of fire and ecosystem productivity. At lower latitudes, richness is greatest at intermediate levels of fire severity and productivity following the DEM. However, as latitude increases, the productivity at which richness is greatest also increases. Mean productivity increases with latitude across the study area, and we argue the changing regional levels of ecosystem productivity alters the relationships predicted by the DEM. Main conclusions Our results provide an important example of how relationships between richness and disturbance may be missed if viewed outside the context of other environmental factors, mainly ecosystem productivity. This highlights the importance of accounting for changing ecological context across broad spatial scales to ensure accurate assessments of disturbance–diversity relationships.

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Three Axes of Ecological Studies

Cited by 7 publications

References 24 publications

The temporal scale of diet and dietary proxies

The temporal scale of diet and dietary proxies

Mapping and quantifying land cover dynamics using dense remote sensing time series with the user-friendly pyNITA software

Testing Huston's dynamic equilibrium model along fire and forest productivity gradients using avian monitoring data

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