Freeman—Tukey chi-squared goodness-of-fit statistics

Read, Campbell B.

doi:10.1016/0167-7152(93)90015-b

Cited by 12 publications

(7 citation statements)

References 2 publications

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“…Data were analyzed using Student’s t test, one-way ANOVA or two-way ANOVA followed by Tukey’s multiple comparison tests [11]. Differences were considered statistically significant when p < 0.05.…”

Section: Methodsmentioning

confidence: 99%

Response of the Renal Inner Medulla to Hypoxia: Possible Defense Mechanisms

Kim

Voicu²,

Hare³

et al. 2012

Nephron Physiol

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Background/Aims: Owing to the precarious blood supply to the renal medulla and the high metabolic requirement of the medullary thick ascending limb of Henle’s loop, this nephron segment should be especially vulnerable when its supply of O₂ declines. Methods: Rats were exposed to 8 or 21% O₂ at different time points up to 5 h, and samples were collected for urine flow rate, urine (U_osm) and renal papillary (RP_osm) osmolality, urinary excretion of Na⁺, Cl^–, K⁺ and Mg²⁺, blood gases, erythropoietin and vasopressinase activity in plasma. Other groups of rats were pretreated with desmopressin acetate (dDAVP) or underwent bilateral nephrectomy (BNX) 1 h prior to the exposure. Results: Hypoxic rats had water diuresis (WD) within 2.5 h, as evidenced by lower U_osm (333 ± 42 mosm/l) and RP_osm (869 ± 57 mosm/l), thus suggesting that hypoxia led to a failure to achieve osmotic equilibrium within the renal papilla. Circulating vasopressinase activity increased, which was partially renal in origin because it was lower after BNX. The renal concentrating ability during hypoxia was maintained with dDAVP pretreatment, suggesting that dDAVP may have improved O₂ delivery and the active reabsorption of Na⁺ in the renal medullary region. Conclusions: WD or high vasopressinase activity may be valuable diagnostic tools to assess renal medullary hypoxia. Pretreatment with dDAVP may prevent these changes during hypoxia.

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Section: Methodsmentioning

confidence: 99%

Response of the Renal Inner Medulla to Hypoxia: Possible Defense Mechanisms

Kim

Voicu²,

Hare³

et al. 2012

Nephron Physiol

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“…We assessed and ranked the various models based on different combinations of the explanatory variables using multi‐model inferencing and Akaike's information criterion (AIC). To test for the fit of the leading models to the data, the leading models were further assessed by Freeman‐Tukey χ 2 goodness‐of‐fit statistics (Read ); full code can be found in .…”

Section: Methodsmentioning

confidence: 99%

Long‐term reevaluation of spatially explicit models as a means for adaptive wildlife management

Cohen

Shamon

Dolev

et al. 2020

Ecological Applications

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2020. Longterm reevaluation of spatially explicit models as a means for adaptive wildlife management. Ecological Applications 30(4):Abstract. We evaluated a 20-yr-old spatially explicit model (SEM) that predicted the spatial expansion of reintroduced Persian fallow deer in northern Israel. Using the current distribution of the deer and based on multi-model inference we assessed the accuracy of the SEM's prediction and what other factors affected the population's current distribution. If the SEM's projection was still valid, the leading model in the multi-model inference would include only the SEM's projection as an explanatory variable with a good fit. Different leading models would reveal key variables overlooked when the SEM was constructed or changes in the landscape unforeseen at the time, thus assisting adaptive management and decisionmaking. We assessed deer presence from camera trap encounter counts analyzed using Nmixture models. Models included various combinations of seven predictors: the 20-yr predictions of an SEM developed during the initial phases of the reintroduction, three key landscape characteristics on which the SEM was originally based but updated to reflect current conditions, distance from the release site, elevation, and the distribution of gray wolves (a predator that was absent from the area when the SEM was developed). Competing models were ranked by Akaike information criterion (AIC). Wolf distribution was the key predictor explaining the current deer distribution, appearing in all three leading models (ΔAIC < 2.0) and carrying 71% of the AIC weight (coefficient = À14.86 AE 5.6 [mean AE SE]). Of these three models, the SEM 20-yr prediction appeared in two, but explained only a fraction of the variance (coefficient = 0.001 AE 0.08). The contribution of all other predictors was negligible. While the SEM failed to accurately predict the 20-yr deer distribution, the divergence between its projection and reality pointed to the probable cause (wolves) of this discrepancy. The inclusion of the SEM prediction in the leading models indicates that had the wolves not spread to the study area, the predictions would still have merit suggesting that long-term SEMs can potentially be robust. Long-term reevaluation of SEMs can be beneficial even if model projections fail, as the process can uncover the specific factors driving this failure, supporting adaptive management procedures.

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“…We considered covariates with summed model weights greater than 0.5 to be the most statistically important (Erb et al ). Last, we evaluated quality of the leading models using Freeman–Tukey chi‐square goodness‐of‐fit tests based on 1,000 bootstrapped samples (Read ).…”

Section: Methodsmentioning

confidence: 99%

Cattle grazing effects on mountain gazelles in Mediterranean natural landscapes

Shamon¹,

Dayan²,

Saltz

2017

J Wildl Manag

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Frequently, Mediterranean natural and semi‐natural areas will undergo cattle grazing as a form of fire fuel reduction management. We used a multi‐species approach to understand effects of cattle grazing on mountain gazelle (Gazella gazella) in relation to 2 potential predators of gazelle neonates: golden jackal (Canis aureus) and wild boar (Sus scrofa). We used light detection and ranging (LiDAR) data and a fine‐scale camera trap design (0.01‐km2 grid) in Ramat Hanadiv Nature Park in the Mediterranean region of Israel to determine gazelle habitat and patch preferences in response to grazing using N‐mixture models. Cattle grazing decreased female gazelle detectability and activity and attracted potential predators during the most sensitive time of the year for gazelle: parturition and the critical first 5 weeks of fawns' lives. Grazing management acts as a disturbance for gazelles and increases risk for neonates. Our results show the importance of understanding the broader mechanism behind predator–prey dynamics and how indirect human‐mediated management actions and direct predation may have adverse effects on wild populations. We recommend that cattle be allowed to enter natural areas only after the spring birth peak. © 2017 The Wildlife Society.

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Freeman—Tukey chi-squared goodness-of-fit statistics

Cited by 12 publications

References 2 publications

Response of the Renal Inner Medulla to Hypoxia: Possible Defense Mechanisms

Response of the Renal Inner Medulla to Hypoxia: Possible Defense Mechanisms

Long‐term reevaluation of spatially explicit models as a means for adaptive wildlife management

Cattle grazing effects on mountain gazelles in Mediterranean natural landscapes

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