Geology topics

E. L. Lindquist

Publications and source records attributed to E. L. Lindquist.

4 recordsLinked to original sources

USGS1996

Coexistence and community structure of tropical trees in a Hawaiian montane rain forest

We measured the diameter at breast height of all trees and shrubs > 5 meters in height, including standing dead trees, on 68 0.04-hectare study plots in a montane, subtropical rain forest on Mauna Loa, Hawai`i. The canopy species consisted of 88 percent Metrosideros polymorpha (ohia) and 12 percent Acacia koa (koa). Negative associations were found between the densities of koa and ohia, the density of koa and the total basal area of ohia, and the total basal areas of koa and ohia. The two-species lottery competition model, a stochastic model in which the coexistence of two species in a space-limited community results from temporal variation in recruitment and death rates, predicts a quadratic-beta distribution for the proportion of space occupied by each species. A discrete version of the quadratic-beta distribution, the quadratic-beta binomial distribution, was fit to the live koa and ohia densities and assessed with goodness-of-fit tests. Likelihood ratio tests provided evidence that the mean adult death rates of the two species were equal but that the relative competitive abilities of the two species favored ohia. These tests were corroborated by a contingency table analysis of death rates based on standing dead trees and growth rate studies which report that koa grows much faster than ohia. The lottery model predicts a positive covariance between death rates and ohia recruitment when mean death rates are equal and koa has a higher growth rate than ohia. We argue that the competitive advantage of ohia is due to its superior dispersal ability into large gaps, which would yield the positive covariance described above, and it is this positive covariance term that skews the occupation of space in favor of ohia.

Hawaii↗

USGS1996

Detecting trends in raptor counts: power and type I error rates of various statistical tests

We conducted simulations that estimated power and type I error rates of statistical tests for detecting trends in raptor population count data collected from a single monitoring site. Results of the simulations were used to help analyze count data of bald eagles (Haliaeetus leucocephalus) from 7 national forests in Michigan, Minnesota, and Wisconsin during 1980-1989. Seven statistical tests were evaluated, including simple linear regression on the log scale and linear regression with a permutation test. Using 1,000 replications each, we simulated n = 10 and n = 50 years of count data and trends ranging from -5 to 5% change/year. We evaluated the tests at 3 critical levels (alpha = 0.01, 0.05, and 0.10) for both upper- and lower-tailed tests. Exponential count data were simulated by adding sampling error with a coefficient of variation of 40% from either a log-normal or autocorrelated log-normal distribution. Not surprisingly, tests performed with 50 years of data were much more powerful than tests with 10 years of data. Positive autocorrelation inflated alpha-levels upward from their nominal levels, making the tests less conservative and more likely to reject the null hypothesis of no trend. Of the tests studied, Cox and Stuart's test and Pollard's test clearly had lower power than the others. Surprisingly, the linear regression t-test, Collins' linear regression permutation test, and the nonparametric Lehmann's and Mann's tests all had similar power in our simulations. Analyses of the count data suggested that bald eagles had increasing trends on at least 2 of the 7 national forests during 1980-1989.

Wildlife Society Bulletin↗

USGS1995

Assessing ground-based counts of nestling bald eagles in northeastern Minnesota

We present evidence that the bald eagle (Haliaeetus leucocephalus) productivity survey in the Boundary Waters Canoe Area Wilderness of northeastern Minnesota may have underestimated the number of nestlings during 1986-1988. Recommendations are provided to achieve more accurate ground-based counts. By conducting ground-based observations for up to 1 hour/nest, an accurate count of the number of bald eagle nestlings can be obtained. If nests are only observed for up to 30 minutes/nest, an accurate determination of nest success can be made. The effort that managers put into counts should be based on the intended use of the productivity data. If small changes in mean productivity would trigger management action, the less acurate ground-based counts should be conducted with caution. Prior to implementing ground-based counts, a study like ours should estimate bias associated with different survey procedures and the observation time needed to achieve accurate results.

Wildlife Society Bulletin↗

USGS1991

Application and partial validation of a habitat model for moose in the Lake Superior region

A modified version of the dormant-season portion of a Habitat Suitability Index (HSI) model developed for assessing moose ( Alces alces ) habitat in the Lake Superior Region was incorporated in a Geographic Information System (GIS) for 490 km 2 of Minnesota's Superior National Forest. Moose locations (n=235) were plotted during aerial surveys conducted in December 1988 and January 1990-1991. Dormant-season forage and cover quality for 1,000-m, 500-m, and 200-m radii plots around random points and moose locations were compared using U.S. Forest Service stand examination data. Cover quality indices were lower than forage quality indices within all plots. The median value for the average cover quality index was greater (P=0.003) within 200-m plots around cow moose locations than for plots around random points for the most severe winter of the study. The proportion of highest-quality winter cover, such as mixed stands dominated by mid-age class white spruce ( Picea glauca ) and balsam fir ( Abies balsanea ), was greater within 500-m and 200-m plots around cow moose than within similar plots around random points during the two most severe winters. These results indicate that suboptimum ratings of winter habitat quality used in the GIS for dormant-season forage >100 m from cover, as suggested in the original HSI model, are reasonable. Integrating the habitat model with forest stand data using a GIS permitted analysis of moose habitat within a relatively large geographic area. Simulation of habitat quality indicated a potential shortage of late-winter cover in the study area. The effects of forest management actions on moose habitat quality can be simulated without collecting additional data.

Lake Superior region↗