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B. Griffith

Publications and source records attributed to B. Griffith.

4 recordsLinked to original sources
USGS2013

Efficacy of calf:cow ratios for estimating calf production of arctic caribou

Caribou (Rangifer tarandus granti) calf:cow ratios (CCR) computed from composition counts obtained on arctic calving grounds are biased estimators of net calf production (NCP, the product of parturition rate and early calf survival) for sexually-mature females. Sexually-immature 2-year-old females, which are indistinguishable from sexually-mature females without calves, are included in the denominator, thereby biasing the calculated ratio low. This underestimate increases with the proportion of 2-year-old females in the population. We estimated the magnitude of this error with deterministic simulations under three scenarios of calf and yearling annual survival (respectively: low, 60 and 70%; medium, 70 and 80%; high, 80 and 90%) for five levels of unbiased NCP: 20, 40, 60, 80, and 100%. We assumed a survival rate of 90% for both 2-year-old and mature females. For each NCP, we computed numbers of 2-year-old females surviving annually and increased the denominator of CCR accordingly. We then calculated a series of hypothetical “observed” CCRs, which stabilized during the last 6 years of the simulations, and documented the degree to which each 6-year mean CCR differed from the corresponding NCP. For the three calf and yearling survival scenarios, proportional underestimates of NCP by CCR ranged 0.046–0.156, 0.058–0.187, and 0.071–0.216, respectively. Unfortunately, because parturition and survival rates are typically variable (i.e., age distribution is unstable), the magnitude of the error is not predictable without substantial supporting information. We recommend maintaining a sufficient sample of known-age radiocollared females in each herd and implementing a regular relocation schedule during the calving period to obtain unbiased estimates of both parturition rate and NCP.

Rangifer↗
USGS2006

A Bayesian random effects discrete-choice model for resource selection: Population-level selection inference

Modeling the probability of use of land units characterized by discrete and continuous measures, we present a Bayesian random-effects model to assess resource selection. This model provides simultaneous estimation of both individual- and population-level selection. Deviance information criterion (DIC), a Bayesian alternative to AIC that is sample-size specific, is used for model selection. Aerial radiolocation data from 76 adult female caribou (Rangifer tarandus) and calf pairs during 1 year on an Arctic coastal plain calving ground were used to illustrate models and assess population-level selection of landscape attributes, as well as individual heterogeneity of selection. Landscape attributes included elevation, NDVI (a measure of forage greenness), and land cover-type classification. Results from the first of a 2-stage model-selection procedure indicated that there is substantial heterogeneity among cow-calf pairs with respect to selection of the landscape attributes. In the second stage, selection of models with heterogeneity included indicated that at the population-level, NDVI and land cover class were significant attributes for selection of different landscapes by pairs on the calving ground. Population-level selection coefficients indicate that the pairs generally select landscapes with higher levels of NDVI, but the relationship is quadratic. The highest rate of selection occurs at values of NDVI less than the maximum observed. Results for land cover-class selections coefficients indicate that wet sedge, moist sedge, herbaceous tussock tundra, and shrub tussock tundra are selected at approximately the same rate, while alpine and sparsely vegetated landscapes are selected at a lower rate. Furthermore, the variability in selection by individual caribou for moist sedge and sparsely vegetated landscapes is large relative to the variability in selection of other land cover types. The example analysis illustrates that, while sometimes computationally intense, a Bayesian hierarchical discrete-choice model for resource selection can provide managers with 2 components of population-level inference: average population selection and variability of selection. Both components are necessary to make sound management decisions based on animal selection.

Journal of Wildlife Management↗
USGS1989

Translocation as a species conservation tool: Status and strategy

Surveys of recent (1973 to 1986) intentional releases of native birds and mammals to the wild in Australia, Canada, Hawaii, New Zealand, and the United States were conducted to document current activities, identify factors associated with success, and suggest guidelines for enhancing future work. Nearly 700 translocations were conducted each year. Native game species constituted 90 percent of translocations and were more successful (86 percent) than were translocations of threatened, endangered, or sensitive species (46 percent). Knowledge of habitat quality, location of release area within the species range, number of animals released, program length, and reproductive traits allowed correct classification of 81 percent of observed translocations as successful or not.

Science↗