Fitting Population Models to Multiple Sources of Observed Data

White, Gary C.; Lubow, Bruce C.

doi:10.2307/3803162

Cited by 77 publications

(73 citation statements)

References 24 publications

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“…However, in spite of a heavy reliance on AIC for model selection in statistical time series analysis, only recently have population ecologists applied model selection to quantify support for competing explanations [23], an approach that appears to be promising as a way to infer mechanisms that control natural fluctuations in population size.…”

Section: Trends In Ecology and Evolution Vol19 No2 February 2004mentioning

confidence: 99%

Model selection in ecology and evolution

Johnson¹,

Omland²

2004

Trends in Ecology & Evolution

3,192

2,660

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Recently, researchers in several areas of ecology and evolution have begun to change the way in which they analyze data and make biological inferences. Rather than the traditional null hypothesis testing approach, they have adopted an approach called model selection, in which several competing hypotheses are simultaneously confronted with data. Model selection can be used to identify a single best model, thus lending support to one particular hypothesis, or it can be used to make inferences based on weighted support from a complete set of competing models. Model selection is widely accepted and well developed in certain fields, most notably in molecular systematics and markrecapture analysis. However, it is now gaining support in several other areas, from molecular evolution to landscape ecology. Here, we outline the steps of model selection and highlight several ways that it is now being implemented. By adopting this approach, researchers in ecology and evolution will find a valuable alternative to traditional null hypothesis testing, especially when more than one hypothesis is plausible.Science is a process for learning about nature in which competing ideas about how the world works are evaluated against observations [1]. These ideas are usually expressed first as verbal hypotheses, and then as mathematical equations, or models. Models depict biological processes in simplified and general ways that provide insight into factors that are responsible for observed patterns. Hence, the degree to which observed data support a model also reflects the relative support for the associated hypothesis.Two basic approaches have been used to draw biological inferences. The dominant paradigm is to generate a null hypothesis (typically one with little biological meaning [2]) and ask whether the hypothesis can be rejected in light of observed data. Rejection occurs when a test statistic generated from observed data falls beyond an arbitrary probability threshold (usually P , 0.05), which is interpreted as tacit support for a biologically more meaningful alternative hypothesis. Hence, the actual hypothesis of interest (the alternative hypothesis) is accepted only in the sense that the null hypothesis is rejected. By contrast, model selection offers a way to draw inferences from a set of multiple competing hypotheses. Model selection is grounded in likelihood theory, a robust framework that supports most modern statistical approaches. Moreover, this approach is rapidly gaining support across several fields in ecology and evolution as a preferred alternative to null hypothesis testing [1,3,4]. Advocates of model selection argue that it has three primary advantages. First, practitioners are not restricted to evaluating a single model where significance is measured against some arbitrary probability threshold. Instead, competing models are compared to one another by evaluating the relative support in the observed data for each model. Second, models can be ranked and weighted, thereby providing a quantitative measure of relative sup...

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Section: Trends In Ecology and Evolution Vol19 No2 February 2004mentioning

confidence: 99%

Model selection in ecology and evolution

Johnson¹,

Omland²

2004

Trends in Ecology & Evolution

3,192

2,660

View full text Add to dashboard Cite

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“…1). The Piceance Basin supports ones of the largest migratory populations of mule deer in North America (White and Lubow, 2002) (Fig. 1).…”

Section: Study Areamentioning

confidence: 99%

“…More locally, numbers of mule deer in western Colorado declined in the late 1980s up until the early 2000s (White and Lubow 2002;Bergman et al 2011). Whilst population numbers have since begun to increase in the region, populations remain low such that the 7 Piceance regional population currently represents about half of historic highs from the late 1970s-early 1980s (from CPW quadrat mark-resight survey data, C. Anderson unpublished data).…”

Section: Introductionmentioning

confidence: 99%

Asynchronous vegetation phenology enhances winter body condition of a large mobile herbivore

et al. 2015

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Article (refereed) -postprintSearle, Kate R.; Rice, Mindy B.; Anderson, Charles R.; Bishop, Chad; Hobbs, N.T. 2015. Asynchronous vegetation phenology enhances winter body condition of a large mobile herbivore. Oecologia, 179 (2). 377-391. 10.1007/s00442-015-3348-9 Contact CEH NORA team at noraceh@ceh.ac.ukThe NERC and CEH trademarks and logos ('the Trademarks') are registered trademarks of NERC in the UK and other countries, and may not be used without the prior written consent of the Trademark owner. AbstractUnderstanding how spatial and temporal heterogeneity influence ecological processes forms a central challenge in ecology. Individual responses to heterogeneity shape population dynamics, therefore understanding these responses is central to sustainable population management. Emerging evidence has shown that herbivores track heterogeneity in nutritional quality of vegetation by responding to phenological differences in plants. We quantified the benefits mule deer (Odocoileus hemionus) accrue from accessing habitats with asynchronous plant phenology in northwest Colorado over three years. Our analysis examined both the direct physiological and indirect environmental effects of weather and vegetation phenology on mule deer winter body condition. We identified several important effects of annual weather patterns and topographical variables on vegetation phenology in the home ranges of mule deer. Crucially, temporal patterns of vegetation phenology were linked with differences in body condition, with deer tending to show poorer body condition in areas with less asynchronous vegetation green-up and later vegetation onset. The direct physiological effect of previous winter precipitation on mule deer body condition was much less important than the indirect effect mediated by vegetation phenology. Additionally, the influence of vegetation phenology on body fat was much stronger than that of overall vegetation productivity. In summary, changing annual weather patterns, particularly in relation to seasonal precipitation, have the potential to alter body condition of this important ungulate species during the critical winter period. This finding highlights the importance of maintaining large contiguous areas of spatially and temporally variable resources to allow animals to compensate behaviourally for changing climate-driven resource patterns.

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“…Estimates of kill rates have increased our understanding of dynamic species interactions in complex multispecies communities, including apparent competition affecting rare prey (Holt andLawton 1994, Wittmer et al 2013), predation-mediated Allee effects (McLellan et al 2010), and the effects of kleptoparasitism on the fitness of subordinate competitors (Gorman et al 1998, Elbroch andWittmer 2013a). Accurate estimates of kill rates are also essential for managers charged with setting sustainable harvest quotas for game species coexisting with native predators (e.g., White and Lubow 2002), and developing conservation strategies for species negatively affected by predation, including those in reintroduction programs (e.g., Rominger et al 2004.…”

Section: Introductionmentioning

confidence: 99%

The difference between killing and eating: ecological shortcomings of puma energetic models

et al. 2014

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Abstract. Bioenergetic modeling is employed to estimate the energetic demands of many cryptic carnivores and their kill rates needed to meet their energetic requirements. We tested two prevalent assumptions driving energetic modeling of predator kill rates: (1) morphological and physiological information (weight, energetic demands of activity patterns) of individual predators are sufficient to accurately predict their kill rates, and (2) kill and consumption rates are equivalent (meaning that carnivores consume all of what they kill). We did this by testing whether two independent energetic models accurately predicted puma (Puma concolor) kill and consumption rates in three study systems in North and South America with variable ecology, including climate and prey assemblages. Our results demonstrated that current puma energetic models drastically underestimate actual puma kill rates quantified through intensive field monitoring. We concluded that puma energetic models more realistically estimate puma consumption rates needed to meet metabolic requirements. Puma kill rates determined from field efforts were not explained by puma weight (in kg) or activity patterns (in distance traveled), which were the variables used in energetic models. Our kill rates in kg/day determined from field investigations of GPS clusters were the highest reported to date and statistically equivalent across three distinct ecosystems, a range of puma characteristics, variable lengths of monitoring, variable daily distances traveled, and across systems with 1-3 ungulate prey. In contrast, puma kill rates in ungulates/ week differed across study areas, suggesting that kill rates described in kilograms per day are better suited for comparing puma kill rates across systems while kill rates in terms of ungulates per unit time are better suited for modeling predator-prey dynamics for a particular ecosystem. Based on these results we concluded that energetic models using morphological and physiological variables alone were insufficient to predict kill rates, and proposed that rather than focusing future research on refining current energetic models, future research should be directed at understanding the behavioral ecology driving carnivore kill rates.

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Fitting Population Models to Multiple Sources of Observed Data

Cited by 77 publications

References 24 publications

Model selection in ecology and evolution

Model selection in ecology and evolution

Asynchronous vegetation phenology enhances winter body condition of a large mobile herbivore

The difference between killing and eating: ecological shortcomings of puma energetic models

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