A graphical framework for model selection criteria and significance tests: refutation, confirmation and ecology

Aho, Ken; Derryberry, DeWayne R.; Peterson, Teri

doi:10.1111/2041-210x.12648

Cited by 22 publications

(20 citation statements)

References 72 publications

(107 reference statements)

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“…While AIC c is recommended to be used as a standard approach (Burnham and Anderson ), more recently BIC has been advocated when comparing models to test hypotheses (i.e., linked to our treatments) and when models are based on controlled experiments (Aho et al. , ), as we did in current study. Differences in AIC c or BIC between two models larger than 2 can be seen as more support for the model with the lowest value.…”

Section: Methodsmentioning

confidence: 94%

Pace of life syndrome under warming and pollution: integrating life history, behavior, and physiology across latitudes

Debecker

Stoks

2018

Ecological Monographs

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To fully comprehend and predict the impact of drivers of global change such as climate warming and pollution, integrated multi‐trait approaches are needed. As organismal traits are often correlated, responses to stressors are expected to induce coordinated changes in many traits. A promising framework to study this is the pace of life syndrome (POLS), which predicts the integration of life history, behavioral, and physiological traits along a fast‐slow continuum. Using an integrative multi‐trait approach, we evaluated the presence of a POLS both within and across latitudes and how POLS patterns are affected by warming and metal pollution. We studied this in Ischnura elegans damselfly larvae of replicated low‐ and high‐latitude populations that strongly differ in voltinism (three to four generations per year vs. one generation every two years) reared in a common‐garden experiment at two temperatures. Across latitudes, life history, behavior, and physiology covaried in accordance with the POLS, with the fast‐paced low‐latitude damselflies characterized by a fast growth rate, high activity, and more explorative and risk‐taking behavior, fast metabolic rate, and low investment in immune function (activity of phenoloxidase). This fast POLS strategy was associated with a higher sensitivity to metal exposure and a higher vulnerability to predation. Warming caused opposite responses between the latitudes consistent with differential thermal adaptation in growth rate, behavior, and oxidative stress parameters. Despite this, damselflies of both latitudes showed a consistent pattern in phenotypic correlations among traits that, moreover, was not affected by warming and metal exposure. Within latitudes, there was no full support for the POLS. More active larvae were more explorative and risk taking, which aligned with the fast‐slow life history axis, but less strong than at the across‐latitude level. Physiological traits were also integrated within latitudes, yet there was no unambiguous coupling with the fast‐slow life history continuum. The consistent syndrome structure, if underpinned by genetic correlations, may restrict the independent evolution of individual traits, yet may not necessarily constrain adaptive evolution of integrated trait sets. This is because the covariance pattern was to a large extent similar across latitudes and within latitudes, suggesting adaptive trait integration guiding adaptive evolution of trait sets along the fast‐slow continuum.

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Section: Methodsmentioning

confidence: 94%

Pace of life syndrome under warming and pollution: integrating life history, behavior, and physiology across latitudes

Debecker

Stoks

2018

Ecological Monographs

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“…Additionally, BIC is a stringent method for carrying out confirmatory hypothesis‐testing and is therefore appropriate for testing the various comparisons listed in Table (Aho et al . ). The best‐supported model was identified as either (1) the top‐ranked model with a BIC at least −2 below that of the second‐ranked model ( M 2 ) (Burnham & Anderson ), or (2) the most parsimonious of those models within 2 BIC units of the top‐ranked model (Burnham & Anderson ), as inclusion of additional variables or sub‐group levels did not result in a better‐performing model.…”

Section: Methodsmentioning

confidence: 97%

Comparative migration strategies of wild and captive‐bred Asian Houbara Chlamydotis macqueenii

Burnside

Collar

Dolman

2017

Ibis

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For migratory species, the success of population reintroduction or reinforcement through captive-bred released individuals depends on survivors undertaking appropriate migrations. We assess whether captive-bred Asian Houbara Chlamydotis macqueenii from a breeding programme established with locally sourced individuals and released into suitable habitat during spring or summer undertake similar migrations to those of wild birds. Using satellite telemetry, we compare the migrations of 29 captive-bred juveniles, 10 wild juveniles and 39 wild adults (including three birds first tracked as juveniles), examining migratory propensity (proportion migrating), timing, direction, stopover duration and frequency, efficiency (route deviation), and wintering and breeding season locations. Captive-bred birds initiated autumn migration an average of 20.6 (AE4.6 se) days later and wintered 470.8 km (AE76.4) closer to the breeding grounds, mainly in Turkmenistan, northern Iran and Afghanistan, than wild birds, which migrated 1217.8 km (AE76.4), predominantly wintering in southern Iran and Pakistan (juveniles and adults were similar). Wintering locations of four surviving captive-bred birds were similar in subsequent years (median distance to first wintering site = 70.8 km, range 6.56-221.6 km), suggesting that individual captive-bred birds (but not necessarily their progeny) remain faithful to their first wintering latitude. The migratory performance of captive-bred birds was otherwise similar to that of wild juveniles. Although the long-term fitness consequences for captivebred birds establishing wintering sites at the northern edge of those occupied by wild birds remain to be quantified, it is clear that the pattern of wild migrations established by long-term selection is not replicated. If the shorter migration distance of young captivebred birds has a physiological rather than a genetic basis, then their progeny may still exhibit wild-type migration. However, as there is a considerable genetic component to migration, captive breeding management must respect migratory population structure as well as natal and release-site fidelity.

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“…We used the MuMIn package in R (Barton, ) to select the top model from among all possible model combinations, with summer and winter ranges evaluated independently (Barton, ). All models within 0–2 AICc units of the top model were evaluated for the presence of uninformative parameters (Arnold, ); a parameter was considered uninformative if the parameter improved model performance by a negligible amount (ΔAIC < 2; Arnold, ; Aho, Derryberry, & Peterson, ). We assessed model performance and predictive skill using cross‐validation (see below).…”

Section: Methodsmentioning

confidence: 99%

A machine‐learning approach for extending classical wildlife resource selection analyses

Shoemaker

Heffelfinger

Jackson

et al. 2018

Ecology and Evolution

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Resource selection functions (RSFs) are tremendously valuable for ecologists and resource managers because they quantify spatial patterns in resource utilization by wildlife, thereby facilitating identification of critical habitat areas and characterizing specific habitat features that are selected or avoided. RSFs discriminate between known‐use resource units (e.g., telemetry locations) and available (or randomly selected) resource units based on an array of environmental features, and in their standard form are performed using logistic regression. As generalized linear models, standard RSFs have some notable limitations, such as difficulties in accommodating nonlinear (e.g., humped or threshold) relationships and complex interactions. Increasingly, ecologists are using flexible machine‐learning methods (e.g., random forests, neural networks) to overcome these limitations. Herein, we investigate the seasonal resource selection patterns of mule deer (Odocoileus hemionus) by comparing a logistic regression framework with random forest (RF), a popular machine‐learning algorithm. Random forest (RF) models detected nonlinear relationships (e.g., optimal ranges for slope and elevation) and complex interactions which would have been very challenging to discover and characterize using standard model‐based approaches. Compared with standard RSF models, RF models exhibited improved predictive skill, provided novel insights about resource selection patterns of mule deer, and, when projected across a relevant geographic space, manifested notable differences in predicted habitat suitability. We recommend that wildlife researchers harness the strengths of machine‐learning tools like RF in addition to “classical” tools (e.g., mixed‐effects logistic regression) for evaluating resource selection, especially in cases where extensive telemetry data sets are available.

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A graphical framework for model selection criteria and significance tests: refutation, confirmation and ecology

Cited by 22 publications

References 72 publications

Pace of life syndrome under warming and pollution: integrating life history, behavior, and physiology across latitudes

Pace of life syndrome under warming and pollution: integrating life history, behavior, and physiology across latitudes

Comparative migration strategies of wild and captive‐bred Asian Houbara Chlamydotis macqueenii

A machine‐learning approach for extending classical wildlife resource selection analyses

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