David O. Wallin

Inferring landscape effects on gene flow: a new model selection framework

Populations in fragmented landscapes experience reduced gene flow, lose genetic diversity over time and ultimately face greater extinction risk. Improving connectivity in fragmented landscapes is now a major focus of conservation biology. Designing effective wildlife corridors for this purpose, however, requires an accurate understanding of how landscapes shape gene flow. The preponderance of landscape resistance models generated to date, however, is subjectively parameterized based on expert opinion or proxy measures of gene flow. While the relatively few studies that use genetic data are more rigorous, frameworks they employ frequently yield models only weakly related to the observed patterns of genetic isolation. Here, we describe a new framework that uses expert opinion as a starting point. By systematically varying each model parameter, we sought to either validate the assumptions of expert opinion, or identify a peak of support for a new model more highly related to genetic isolation. This approach also accounts for interactions between variables, allows for nonlinear responses and excludes variables that reduce model performance. We demonstrate its utility on a population of mountain goats inhabiting a fragmented landscape in the Cascade Range, Washington.

Landscape Pattern Response to Changes in Pattern Generation Rules: Land‐Use Legacies in Forestry

The Pacific Northwest of the United States is currently embroiled in an acrimonious debate over the management of federal forest lands. Constructive resolution of this debate will require better information on a broad range of forest management issues. This study focuses on one such issue: the development of landscape pattern in response to alternative forest cutting plans and the degree to which established landscape patterns can be changed. Dispersed cutting has been conducted on federal lands in the western United States for > 40 yr, but alternative cutting plans are now being considered. To assess the effects of different disturbance processes on the development of landscape pattern, we compare dispersed- and aggregated-cutting plans using a simple, rule-based simulation model that incorporates realistic regulatory and logistic constraints. Our results indicate that, once established, the landscape pattern created by dispersed disturbances is difficult to erase without a substantial reduction in the disturbance rate or a reduction in the minimum stand age eligible for disturbance. Change in landscape pattern can lag substan- tially behind change in the rules governing pattern generation.

Coral reef habitat discrimination using multivariate spectral analysis and satellite remote sensing

Coral reefs are important both biologically and economically, but are under increasing pressure from pollution and human-induced disturbance. Coral reefs are often remote, relatively large and shallow. Thus, optical remote sensing techniques provide an efficient and cost-effective approach to mapping and monitoring the condition of reefs. We tested the hypothesis that in-situ spectral signatures, together with water column attenuation measurements, satellite imagery and Geographical Information System, can effectively differentiate reef substrates. In situ upwelling radiances were collected for coarse descriptive habitat types near Lee Stocking Island, Bahamas between 17 July and 27 July, 1998. Down-looking spectral scans from 300-750 nm were taken 1 m above the substrate and 1 m below the water surface, using a LiCor-1800 underwater spectroradiometer. Principal component analysis of upwelling radiances indicated that wavelengths from 515-580 nm are most useful in distinguishing between substrates. Landsat Thematic Mapper (TM) multi-spectral imagery was geo-registered and an ISODATA unsupervised classification was used to delineate reef features and associated communities. Using this method, we achieved an overall accuracy of 74% for coarse descriptive resolution mapping of seven substrate types (i.e. ocean, deep sand, shallow sand, deep coral, shallow coral, seagrass, and mixed sand and seagrass) within an area of 590 km 2 near Lee Stocking Island.

Separating the effects of environmental, spatial and disturbance factors on forest community structure in the Russian Far East

We used partial canonical correspondence analysis to decipher the relationships among abiotic environmental variables, disturbance history and spatial gradients and the composition of the forest communities in the Sikhote-alin Mountains in the Russian Far East. Through a series of partial canonical ordinations, we decomposed the variance in the forest tree community that is explainable by these three sets of factors into its seven discrete components of partial effects and interactions. We determined that the forests of the Sikhote-alin Range are structured primarily by the interaction of three things: elevation, physiographical position and disturbance history. Over the past 100 years, the disturbance regime in the study area has changed dramatically, with vast increases in the frequency of catastrophic wildfires. The effects these changes in disturbance regime have had are seen at different strengths across the altitudinal gradient. At lower elevations, the change in disturbance regime occurred very early on, and the community is now in a fire-maintained condition that is dominated by Quercus mongolica. Mature Pinus koraiensis, which formerly was co-dominant, has been nearly eliminated from the lowland forest. The increase in fire disturbance has moved in a wave across the study area and now is impacting upper-elevation primary forest as well. In the near-future, it is likely that P. koraiensis forest remnants at middle elevations will be drastically reduced and that large proportions of the upper-elevation Picea and Abies primary forest will be converted to Betula, Larix and Populus tremula.

Ecological and Evolutionary Consequences of Patchiness: A Marine-Terrestrial Perspective

A quantitative description of patchiness and the assessment of its effects on ecological and evolutionary processes represents a major research focus as well as a challenge for ecologists and evolutionary biologists (e.g., Pickett and White 1985, Shorrocks and Swingland 1990, Kolasa and Pickett 1991). Patchiness is neither unique in origin nor characteristic of particular temporal or spatial scales; rather, patchiness emerges from the interactions between physical and biotic processes (Levin 1976, 1978) and is apparent at any scale of resolution. The scale dependency of patchiness and the complexity it generates calls attention to the need for new modeling approaches where spatial and temporal heterogeneity is explicitly incorporated (e.g., Hassell et al. 1991, Deutschman et al., this volume) and for new methodological tools to deal with problems of scale (e.g., Milne 1992, Garcia-Moliner et al., this volume).

Satellite Remote Sensing of Breeding Habitat for an African Weaver-Bird

Data derived from the Advanced Very High Resolution Radiometer (AVHRR) on board the NOAA series of operational, polar orbiting, meteorological satellites have previously been shown to be quite useful for monitoring vegetation dynamics at scales ranging from regional (104 km2) to global. In this report, we demonstrate that these same data can be used to monitor potential breeding habitat for a highly mobile, granivorous African weaver-bird, the red-billed quelea (Quelea quelea). This species is often considered to be an agricultural pest, affecting cereal production throughout sub-Saharan Africa. The temporal resolution and very large (continental) spatial coverage provided by these data can provide a unique context within which to examine species distribution and abundance patterns.

Introduction to Markov Models

Models of landscape change are important tools for understanding the forces that shape landscapes. One motivation for modeling is to examine the implications of extrapolating short-term landscape dynamics over the longer term. This extrapolation of the status quo can serve as a frame of reference against which to assess alternative management scenarios or test hypotheses. There is a spectrum of ways to consider landscape change, ranging from simple and readily interpretable, to more realistic and less tractable. The goals of this lab are to.

Multi scale habitat relationships of Martes americana in northern Idaho, U.S.A.

We used bivariate scaling and logistic regression to investigate multiple-scale habitat selection by American marten (Martes americana). Bivariate scaling reveals dramatic differences in the apparent nature and strength of relationships between marten occupancy and a number of habitat variables across a range of spatial scales. These differences include reversals in the direction of an observed association from positive to negative and frequent dramatic changes in the apparent importance of a habitat variable as a predictor of marten occurrence. Logistic regression on the optimally scaled input variables suggests that at the scale of home ranges, marten select landscapes with high average canopy closure and low fragmentation. Within these low fragmented landscapes, marten select foraging habitat at a fine scale within late-seral, middle-elevation mesic forests. In northern Idaho, optimum American marten habitat, therefore, consists of landscapes with low road density, low density of non-forest patches with high canopy closure, and large areas of middle-elevation, late successional mesic forest. Comparison of current landscape conditions to those expected under the historic range of variability indicates that road building and timber harvest in the past century may have substantially reduced the amount of suitable marten habitat in northern Idaho. Our results are generally consistent with previous research in the Rocky Mountains, with additional insights related to the relative importance, functional form, and scale at which each habitat variable has the largest influence on marten occurrence.

GPS Bias Correction and Habitat Selection by Mountain Goats

Abstract: In Washington State, USA, mountain goats ( Oreamnos americanus ) have experienced a long-term population decline. To assist management, we created annual and seasonal (summer and winter) habitat models based on 2 years of data collected from 38 GPS-collared (GPS plus collar v6, Vectronic-Aerospace GmbH, Berlin, Germany) mountain goats in the western Cascades. To address GPS bias of position acquisition, we evaluated habitat and physiographic effects on GPS collar performance at 543 sites in the Cascades. In the western Cascades, total vegetation cover and the quadratic mean diameter of trees were shown to effect GPS performance. In the eastern Cascades, aspect and total vegetation cover were found to influence GPS performance. To evaluate the influence of bias correction on the analysis of habitat selection, we created resource selection functions with and without bias correction for mountain goats in the western Cascades. We examined how well the resultant habitat models performed with reserved data (25% of fixes from 38 study animals) and with data from 9 other GPS-collared mountain goats that were both temporally and spatially independent. The statistical properties of our GPS bias correction model were similar to those previously reported explaining between 20 and 30% of the variation, however, application of bias correction improved the accuracy of the mountain goat habitat model by only 1–2% on average and did not alter parameter estimates in a meaningful, or consistent manner. Despite statistical limitations, our habitat models, most

Use of Remote Sensing to Model Land Use Effects on Carbon Flux in Forests of the Pacific Northwest, USA

Reducing the uncertainty in the global carbon (C) budget will require better information on regional C budgets. We discuss the use of a simple “metamodel,” in conjunction with satellite data, to quantify C flux from a 12,000-km2 forestland study area in Oregon. The model tracks C storage in living, detrital and forest products pools. Between 1972 and 1991, total C flux from this study area to the atmosphere was estimated to average 1.13 Mg ha−1 yr−1, with values ranging from −4.7 to +15.8 Mg ha−1 yr−1. This spatial variability was related to site quality, land use and historical factors. These results are used to illustrate the natural and anthropogenic sources of heterogeneity that can influence C budgets at the regional scale and to demonstrate how remotely sensed data can be used to help quantify this heterogeneity.