Appendix A. ΔAIC values for the first set of models and the best model within this set (model 22).

ΔAIC values for the first set of models and the best model within this set (model 22)

Appendix A. Derivation of Eqs. 4 and 7.

Derivation of Eqs. 4 and 7

Appendix B. Model selection results for Eurasian Collared Dove and House Finch case studies.

Model selection results for Eurasian Collared Dove and House Finch case studies

Does Bioelectrical Impedance Analysis Accurately Estimate the Physiological Condition of Threatened and Endangered Desert Fish Species?

<p>Bioelectrical impedance analysis (BIA) is a nonlethal tool with which to estimate the physiological condition of animals that has potential value in research on endangered species. However, the effectiveness of BIA varies by species, the methodology continues to be refined, and incidental mortality rates are unknown. Under laboratory conditions we tested the value of using BIA in addition to morphological measurements such as total length and wet mass to estimate proximate composition (lipid, protein, ash, water, dry mass, energy density) in the endangered Humpback Chub <i>Gila cypha</i> and Bonytail <i>G. elegans</i> and the species of concern Roundtail Chub <i>G. robusta</i> and conducted separate trials to estimate the mortality rates of these sensitive species. Although Humpback and Roundtail Chub exhibited no or low mortality in response to taking BIA measurements versus handling for length and wet-mass measurements, Bonytails exhibited 14% and 47% mortality in the BIA and handling experiments, respectively, indicating that survival following stress is species specific. Derived BIA measurements were included in the best models for most proximate components; however, the added value of BIA as a predictor was marginal except in the absence of accurate wet-mass data. Bioelectrical impedance analysis improved the <i>R</i>² of the best percentage-based models by no more than 4% relative to models based on morphology. Simulated field conditions indicated that BIA models became increasingly better than morphometric models at estimating proximate composition as the observation error around wet-mass measurements increased. However, since the overall proportion of variance explained by percentage-based models was low and BIA was mostly a redundant predictor, we caution against the use of BIA in field applications for these sensitive fish species.</p> <p>Received November 7, 2016; accepted March 1, 2017 Published online July 5, 2017</p

Appendix A. Description of flow, biological, and other predictor variables used in generalized linear mixed models of rainbow and brown trout recruitment and mean adult length.

Description of flow, biological, and other predictor variables used in generalized linear mixed models of rainbow and brown trout recruitment and mean adult length

Appendix B. Result of Principal Component Analyses for data on the seasonal proportion of flow in rainbow and brown trout generalized linear mixed models.

Result of Principal Component Analyses for data on the seasonal proportion of flow in rainbow and brown trout generalized linear mixed models

Appendix D. Coefficient estimates for detection portion of model.

Coefficient estimates for detection portion of model

Appendix B. Estimates of detection components of model taken from best model in the fourth set.

Estimates of detection components of model taken from best model in the fourth set

Appendix B. Graphical summary of simulation study.

Graphical summary of simulation study

Appendix A. Selected estimated coefficients for the top four models from the fourth model set.

Selected estimated coefficients for the top four models from the fourth model set