Nicholas S. Keuler

Predicting spatial patterns of fire on a southern California landscape

Humans influence the frequency and spatial pattern of fire and contribute to altered fire regimes, but fuel loading is often the only factor considered when planning management activities to reduce fire hazard. Understanding both the human and biophysical landscape characteristics that explain how fire patterns vary should help to identify where fire is most likely to threaten values at risk. We used human and biophysical explanatory variables to model and map the spatial patterns of both fire ignitions and fire frequency in the Santa Monica Mountains, a human-dominated southern California landscape. Most fires in the study area are caused by humans, and our results showed that fire ignition patterns were strongly influenced by human variables. In particular, ignitions were most likely to occur close to roads, trails, and housing development but were also related to vegetation type. In contrast, biophysical variables related to climate and terrain (January temperature, transformed aspect, elevation, and slope) explained most of the variation in fire frequency. Although most ignitions occur close to human infrastructure, fires were more likely to spread when located farther from urban development. How far fires spread was ultimately related to biophysical variables, and the largest fires in southern California occurred as a function of wind speed, topography, and vegetation type. Overlaying predictive maps of fire ignitions and fire frequency may be useful for identifying high-risk areas that can be targeted for fire management actions.

Housing is positively associated with invasive exotic plant species richness in New England, USA.

Understanding the factors related to invasive exotic species distributions at broad spatial scales has important theoretical and management implications, because biological invasions are detrimental to many ecosystem functions and processes. Housing development facilitates invasions by disturbing land cover, introducing nonnative landscaping plants, and facilitating dispersal of propagules along roads. To evaluate relationships between housing and the distribution of invasive exotic plants, we asked (1) how strongly is housing associated with the spatial distribution of invasive exotic plants compared to other anthropogenic and environmental factors; (2) what type of housing pattern is related to the richness of invasive exotic plants; and (3) do invasive plants represent ecological traits associated with specific housing patterns? Using two types of regression analysis (best subset analysis and hierarchical partitioning analysis), we found that invasive exotic plant richness was equally or more strongly related to housing variables than to other human (e.g., mean income and roads) and environmental (e.g., topography and forest cover) variables at the county level across New England. Richness of invasive exotic plants was positively related to area of wildland-urban interface (WUI), low-density residential areas, change in number of housing units between 1940 and 2000, mean income, plant productivity (NDVI), and altitudinal range and rainfall; it was negatively related to forest area and connectivity. Plant life history traits were not strongly related to housing patterns. We expect the number of invasive exotic plants to increase as a result of future housing growth and suggest that housing development be considered a primary factor in plans to manage and monitor invasive exotic plant species.

Improved estimates of forest vegetation structure and biomass with a LiDAR-optimized sampling design

[1]xa0LiDAR data are increasingly available from both airborne and spaceborne missions to map elevation and vegetation structure. Additionally, global coverage may soon become available with NASAs planned DESDynI sensor. However, substantial challenges remain to using the growing body of LiDAR data. First, the large volumes of data generated by LiDAR sensors require efficient processing methods. Second, efficient sampling methods are needed to collect the field data used to relate LiDAR data with vegetation structure. In this paper, we used low-density LiDAR data, summarized within pixels of a regular grid, to estimate forest structure and biomass across a 53,600 ha study area in northeastern Wisconsin. Additionally, we compared the predictive ability of models constructed from a random sample to a sample stratified using mean and standard deviation of LiDAR heights. Our models explained between 65 to 88% of the variability in DBH, basal area, tree height, and biomass. Prediction errors from models constructed using a random sample were up to 68% larger than those from the models built with a stratified sample. The stratified sample included a greater range of variability than the random sample. Thus, applying the random sample model to the entire population violated a tenet of regression analysis; namely, that models should not be used to extrapolate beyond the range of data from which they were constructed. Our results highlight that LiDAR data integrated with field data sampling designs can provide broad-scale assessments of vegetation structure and biomass, i.e., information crucial for carbon and biodiversity science.

Human and biophysical influences on fire occurrence in the United States

National-scale analyses of fire occurrence are needed to prioritize fire policy and management activities across the United States. However, the drivers of national-scale patterns of fire occurrence are not well understood, and how the relative importance of human or biophysical factors varies across the country is unclear. Our research goal was to model the drivers of fire occurrence within ecoregions across the conterminous United States. We used generalized linear models to compare the relative influence of human, vegetation, climate, and topographic variables on fire occurrence in the United States, as measured by MODIS active fire detections collected between 2000 and 2006. We constructed models for all fires and for large fires only and generated predictive maps to quantify fire occurrence probabilities. Areas with high fire occurrence probabilities were widespread in the Southeast, and localized in the Mountain West, particularly in southern California, Arizona, and New Mexico. Probabilities for large-fire occurrence were generally lower, but hot spots existed in the western and south-central United States The probability of fire occurrence is a critical component of fire risk assessments, in addition to vegetation type, fire behavior, and the values at risk. Many of the hot spots we identified have extensive development in the wildland--urban interface and are near large metropolitan areas. Our results demonstrated that human variables were important predictors of both all fires and large fires and frequently exhibited nonlinear relationships. However, vegetation, climate, and topography were also significant variables in most ecoregions. If recent housing growth trends and fire occurrence patterns continue, these areas will continue to challenge policies and management efforts seeking to balance the risks generated by wildfires with the ecological benefits of fire.

Analgesic effects of carprofen and liposome-encapsulated butorphanol tartrate in hispaniolan parrots (Amazona ventralis) with experimentally induced arthritis.

OBJECTIVEnTo evaluate the microcrystalline sodium urate (MSU) method for inducing arthritis in parrots and to compare the analgesic efficacy of long-acting liposome-encapsulated butorphanol (LEBT), carprofen, or a combination of both.nnnANIMALSn20 Hispaniolan parrots.nnnPROCEDURESnMSU was injected into a tibiotarsal-tarsometatarsal (intertarsal) joint to induce arthritis (time 0). Four treatments were compared (LEBT [15 mg/kg, SC] administered once at time 0; injections of carprofen [3 mg/kg, IM, q 12 h] starting at time 0; administration of LEBT plus carprofen; and a control treatment of saline [0.9% NaCl] solution). Weight load testing and behavioral scoring were conducted at 0, 2, 6, 26, and 30 hours.nnnRESULTSnInjection of MSU into the intertarsal joint induced arthritis, which resolved within 30 hours. Treatment with LEBT or LEBT plus carprofen resulted in significantly greater weight-bearing load on the limb with induced arthritis, compared with the control treatment. Treatment with carprofen alone caused a slight but nonsignificant improvement in weight-bearing load on the arthritic limb, compared with the control treatment. Behaviors associated with motor activity and weight bearing differed between the control and analgesic treatments.nnnCONCLUSIONS AND CLINICAL RELEVANCEnButorphanol was an effective treatment for pain associated with arthritis, but carprofen administered every 12 hours was insufficient. Injection of MSU to induce arthritis in a single joint was a good method for evaluating tonic pain in parrots, and measurement of the weight-bearing load was accurate for assessment of arthritic pain; however, behavioral changes associated with pain were subtle.

Prognostic Value of Clinicopathologic Variables Obtained at Admission and Effect of Antiendotoxin Plasma on Survival in Septic and Critically Ill Foals

This prospective study compared survival rates of critically ill and septic foals receiving 1 of 2 different types of commercial equine plasma and analyzed admission variables as possible predictors of survival. Standardized clinical, hematologic, biochemical, and hemostatic admission data were collected and foals received either conventional commercially available hyperimmune equine plasma or equine plasma specifically rich in antiendotoxin antibodies in a double-blinded, coded fashion. Sepsis was defined as true bacteremia or sepsis score >11. Overall survival rate to discharge was 72% (49/68). Foals that were nonbacteremic and demonstrated a sepsis score of < or = 11 at admission had a 95% (18/19) survival rate. The survival rate to discharge for septic foals was 28/49 (57%), with truly bacteremic foals having a survival rate of 58% (14/24), whereas that for nonbacteremic, septic foals was 56% (14/25). Sensitivity and specificity for sepsis score >11 as a predictor of bacteremia were 74 and 52%, respectively. For the entire study population, a higher survival rate to discharge was documented for those foals receiving hyperimmune plasma rich in antiendotoxin antibodies (P = .012, odds ratio [OR] 6.763, 95% confidence interval [CI]: 1.311, 34.903). Administration of plasma rich in antiendotoxin antibodies also was associated with greater survival in septic foals (P = .019, OR 6.267, 95% CI: 1.186, 33.109). Statistical analyses demonstrated that, among 53 clinical and clinicopathologic admission variables, high sepsis score (P < .001), low measured IgG concentration (P = .01), high fibrinogen concentration (P = .018), low segmented neutrophil count (P = .028), and low total red blood cell numbers (P = .048) were the most significant predictors of overall mortality.

Analgesic effects of intramuscular administration of meloxicam in Hispaniolan parrots (Amazona ventralis) with experimentally induced arthritis.

OBJECTIVE-To evaluate the analgesic efficacy of meloxicam in parrots with experimentally induced arthritis, with extent of weight bearing and rotational perch walking used as outcome measures. ANIMALS-15 adult Hispaniolan parrots (Amazona ventralis). PROCEDURES-Arthritis was experimentally induced via intra-articular injection of microcrystalline sodium urate suspension (MSU) into 1 intertarsal joint. Parrots were treated in a crossover design. Five treatments were compared as follows: meloxicam (4 dosages) at 0.05, 0.1, 0.5, and 1.0 mg/kg (IM, q 12 h, 3 times) and 0.03 mL of saline (0.9% NaCl) solution (IM, q 12 h, 3 times). The first treatment was given 6 hours following MSU administration. Lameness was assessed by use of a biomechanical perch to record weight-bearing load and a rotational perch to determine dexterity. Feces were collected to assay for occult blood. RESULTS-Parrots treated with meloxicam at 1.0 mg/kg had significantly better return to normal (baseline) weight bearing on the arthritic pelvic limb, compared with control parrots or parrots treated with meloxicam at 0.05, 0.1, and 0.5 mg/kg. All fecal samples collected from parrots following induction of arthritis and treatment with meloxicam had negative results for occult blood. CONCLUSIONS AND CLINICAL RELEVANCE-Meloxicam administered at 1.0 mg/kg, IM, every 12 hours effectively relieved arthritic pain in parrots.

Broadscale variability in tree data of the historical Public Land Survey and its consequences for ecological studies

Historical records provide valuable information on the prior conditions of ecological systems and species distribution, especially in the context of growing environmental change. However, historical records may have associated bias and error because their original purpose may not have been for scientific use. The Public Land Survey (PLS) of the U.S. General Land Office (GLO) conducted from the late 1700s to the early 1900s has been widely used to characterize historical vegetation in the United States prior to major Euro-American settlements. Studies have shown that variability and bias exist in the data. However, these studies have not typically encompassed a region large enough to adequately assess this variability across diverse landscapes, nor attempted to distinguish potential ecological significance from statistical differences. Here we do this by analyzing variability in PLS data across all of northern Wisconsin, USA, a 75 000-km 2 landscape. We found ecologically significant differences among survey point types for tree species, size, and the distance to survey points. Both corner and line trees show some level of bias for species and size, but corner trees are likely the best sample. Although statistical tests show significant differences in species composition, tree size, and distance by tree sequence and location, the differences in species composition and tree size are not ecologically significant. The species differences are probably caused by fine-scale variability in the forest communities. The value of the PLS data remains high; choice of spatial extent, methods of analyses, and bias significance need to be evaluated according to variables of interest and project purpose.

Rural housing is related to plant invasions in forests of southern Wisconsin, USA.

Forests throughout the US are invaded by non-native invasive plants. Rural housing may contribute to non-native plant invasions by introducing plants via landscaping, and by creating habitat conditions favorable for invaders. The objective of this paper was to test the hypothesis that rural housing is a significant factor explaining the distribution of invasive non-native plants in temperate forests of the Midwestern US. In the Baraboo Hills, Wisconsin, we sampled 105 plots in forest interiors. We recorded richness and abundance of the most common invasive non-native plants and measured rural housing, human-caused landscape fragmentation (e.g. roads and forest edges), forest structure and topography. We used regression analysis to identify the variables more related to the distribution of non-native invasive plants (best subset and hierarchical partitioning analyses for richness and abundance and logistic regression for presence/absence of individual species). Housing variables had the strongest association with richness of non-native invasive plants along with distance to forest edge and elevation, while the number of houses in a 1xa0km buffer around each plot was the variable most strongly associated with abundance of non-native invasive plants. Rhamnus cathartica and Lonicera spp. were most strongly associated with rural housing and fragmentation. Berberis thumbergii and Rosa multiflora were associated with gentle slopes and low elevation, while Alliaria petiolata was associated with higher cover of native vegetation and stands with no recent logging history. Housing development inside or adjacent to forests of high conservation value and the use of non-native invasive plants for landscaping should be discouraged.

Rapid declines of large mammal populations after the collapse of the Soviet Union

Anecdotal evidence suggests that socioeconomic shocks strongly affect wildlife populations, but quantitative evidence is sparse. The collapse of socialism in Russia in 1991 caused a major socioeconomic shock, including a sharp increase in poverty. We analyzed population trends of 8 large mammals in Russia from 1981 to 2010 (i.e., before and after the collapse). We hypothesized that the collapse would first cause population declines, primarily due to overexploitation, and then population increases due to adaptation of wildlife to new environments following the collapse. The long-term Database of the Russian Federal Agency of Game Mammal Monitoring, consisting of up to 50,000 transects that are monitored annually, provided an exceptional data set for investigating these population trends. Three species showed strong declines in population growth rates in the decade following the collapse, while grey wolf (Canis lupus) increased by more than 150%. After 2000 some trends reversed. For example, roe deer (Capreolus spp.) abundance in 2010 was the highest of any period in our study. Likely reasons for the population declines in the 1990s include poaching and the erosion of wildlife protection enforcement. The rapid increase of the grey wolf populations is likely due to the cessation of governmental population control. In general, the widespread declines in wildlife populations after the collapse of the Soviet Union highlight the magnitude of the effects that socioeconomic shocks can have on wildlife populations and the possible need for special conservation efforts during such times.