Incorporating the disturbance process of fire into invasive species habitat suitability models

Department Head: Michael J. Manfredo.2008 Fall.Includes bibliographical references (pages 120-131).This study is motivated by the difficulties land managers face while attempting to simultaneously maintain the natural role of fire in ecosystems and prevent the spread and proliferation of invasive plants. I developed habitat suitability models to predict the responses of three invasive species to fire and other environmental variables: one species in each of three National Parks. For each species, model comparisons tested whether the inclusion of nationally-available data on burn severity, time since fire, and fire occurrence could improve habitat suitability models relative to non-burn data alone. Each species demonstrated significant responses to fire, although incorporation of fire information into the models improved model performance for some species more than for others

Modeling Eurasian watermilfoil (Myriophyllum spicatum) habitat with geographic information systems

Eurasian watermilfoil (Myriophyllum spicatum) habitat was predicted at multiple scales, including a lake, regional, and national level. This dissertation illustrates how habitat can be predicted for M. spicatum using publically-available data for both presence and environmental variables. Models were generated using statistical procedures and quantative methods to determine where the greatest likelihood of presence was located. For the single lake, presence and absence data were available, but the larger-scale models used presence-only methods of prediction. These models were paired with a Geographic Information System so that data could be visualized on a map. For the selected lake, Pend Oreille (Idaho), spatial analysis using general linear mixed models was used to show that depth and fetch could be used to predict habitat, although differences were seen in their importance between the littoral and pelagic zones. For the states of Minnesota and Wisconsin, Mahalanobis distance and maximum entropy methods were used to demonstrate that available habitat will not always mean presence of M. spicatum. The differing approaches to management in these states illustrated how an aggressive public education campaign can limit spread of M. spicatum, even when habitat is available. Bass habitat appeared to be the largest predictor of M. spicatum in Minnesota, although this was due to the similar environmental preferences by these species. Using maximum entropy, on a national level, presence of M. spicatum appeared to be best predicted by annual precipitation. Again, results showed that habitat is colonized as time permits, and not necessarily as conditions permit

A habitat assessment to locate tree of heaven [Ailanthus altissima, (Mill.) Swingle] in Mammoth Cave National Park

Invasive, nonnative plants pose a significant threat to national parks. Effective and efficient tools are needed to help managers detect, prioritize and target nonnative plants for control. I used spatial modeling techniques to predict the occurrence of tree of heaven (Ailanthus altissima, (Mill.) Swingle) in Mammoth Cave National Park (MACA), Kentucky. Tree of heaven is known to be a problematic invasive, nonnative plant species and was identified as a priority for control at MACA. I developed a multivariate habitat model to determine optimal habitat for tree of heaven within MACA. Habitat characteristics of 135 known tree of heaven locations were used in combination with seven environmental variables to calculate the predicted probability of occurrence of tree of heaven in MACA using logistic regression analysis. Variables for predicting habitat were created from public records, MACA databases, and a geographic information system (GIS).Twenty-seven a priori models were developed based on the biological requirements of the species and observations of invasion pattern in MACA and the most parsimonious model was selected using Akaike̕s Information Criteria. The seven variables included in the optimal model were derived from soil, site classification, geology, topography, and canopy coverage. I tested the predictive power of the model with independently collected presence and absence data. Ninety seven percent of test locations for tree of heaven were associated with predicted probabilities in the 0-0.30 range. The model improved the probability of finding tree of heaven compared with random searches by approximately 10%. It had poor discrimination (false positive = 0.31, false negative = 0.38, overall reliability = 0.41) and was not well calibrated. Based on its low predictive power, this habitat model could not be recommended for use in managing tree of heaven populations at MACA.Model failure could be attributed to a number of factors and/or combinations of factors including insufficient data, inappropriate scale and the generalist nature of the species. However, results from this study elucidate areas for future research into the applicability of habitat modeling to invasive, nonnative species at local scales

Use of Geospatial Methods to Characterize Dispersion of the Emerald Ash Borer in Southern Ontario, Canada

Since the introduction of the Asian Emerald Ash Borer beetle (EAB, Agrilus planipennis) to Southern Ontario in 2002, the condition of all species of Ash trees (Fraxinus) in the province is currently at risk. In this research, the effects of positive spatial autocorrelation on the EAB data as a result of sampling bias was addressed by applying a filtering distance threshold. To analyze the impact of environmental and anthropogenic predictors on the distribution of the EAB, logistic regression, Random Forest (RF) and a hybrid of Random Forest and GLM known as the Random Generalized Linear Model (RGLM) were applied to EAB data from 2006-2012 across Ontario. Ultimately, three risk maps were created from the 2006-2012 EAB data to validate the prediction dataset from 2013. In terms of model transferability, RGLM had the best extrapolation accuracy (84%), followed by stepwise backward logistic regression (70%), and Random Forest (52%)

Using Remote Sensing and GIS to Assess the Effects of Land Use/Cover Change and Geographic Variables on the Spread of Poisonous Invasive Giant Hogweed in Latvia

Land-use land-cover change (LULCC) especially those caused by human activities, is one of the most important components of global environmental change (Jessen, 2005). This dissertation analyses the effects of geographic, biographical, and demographic factors on LULCC and how LULCC and geographic variables influence the spread of invasive Giant Hogweed in northeastern Latvia. Data sets used in this study include: remote sensing images (Landsat Thematic Mapper acquired in 1992 and 2007), global positioning system (GPS data), census data, and data from public participation geographic information systems (PPGIS). These data were processed and analysed in a geographic information system (GIS). Six categories of land-cover were studied to determine land-cover change (LCC) and the relationship to population change between 1992 and 2007. Classification and analyis of the 1992 and 2007 Landsat images revealed that land-cover changing to forest is the most common type of change (17.1% of pixels) followed by changes to agriculture (8.6% of pixels) and the least was changes to urban/suburban (0.8% of pixels). Integration of the census data and land-cover classification revealed interesting patterns, for example, that population density is positively correlated with percent change to forest, agriculture and urban. Modeling the spread of Giant Hogweed was achieved using logistic regression and a novel cluster analysis approach. The logistic regression model was used to model the spread of Giant Hogweed using presence and pseudo-absence data of Giant Hogweed, while cluster analysis used only Giant Hogweed presence data. Both models were run using data from a series of GIS layers including topographic and LULCC information. The results from logistic regression and cluster analysis show that Giant Hogweed is more likely to grow near roads, near rivers, in proximity to urban centers and in low elevation areas. Habitat suitability maps produced from both models indicate where Giant Hogweed is more likely to spread in the future and can serve as useful tools for policy makers and land managers to focus their efforts to manage weed invasions, and identify similar habitats where Giant Hogweed may occur in the future

Evaluating risk for current and future Bromus tectorum invasion and large wildfires at multiple spatial scales in Colorado and Wyoming, USA

Includes bibliographical references.2015 Summer.The Western United States is experiencing rapid ecologic change. These changes are driven largely by anthropogenic factors including introduction of alien invasive species, wildfire ignition and suppression, climate change, and feedbacks between these occurrences. Average temperatures in some areas of the Western U.S. increased as much as 1.1 °C between 2000 and 2006. The advancement of spring also provides evidence for climate change in the region; earlier snowmelt and runoff has been documented in recent decades for areas of the Intermountain West. These rapid changes will certainly affect the distribution of the alien invasive B. tectorum and large wildfires in Colorado and Wyoming as well as their associated feedbacks and cascading ecosystem effects. Prompted and inspired by natural resource manager concerns, this research evaluates these ecological phenomena at three spatial scales: Rocky Mountain National Park, Colorado; a wildfire disturbance in Medicine Bow National Forest, Wyoming; and the area encompassed by these two states. The products from this research are maps that can be incorporated into decision support systems for land management and vulnerability assessments for climate change preparedness. An evaluation of the current and future suitable habitat for B. tectorum in Rocky Mountain National Park was conducted at a 90 m² spatial resolution using a MaxEnt model fit with climatic, vegetation cover, and anthropogenic covariates (i.e. distance to roads as a surrogate for propagule pressure). One of the important considerations of this research was spatial scale; 250 m² and 1 km² resolution climate surfaces cannot capture climate refugia in a small area such as Rocky Mountain National Park (1,076 km²) with high topographic heterogeneity (2,300 m to 4,345 m elevation). Based on model results, the suitable habitat for B. tectorum in the Park increases more than 150 km2 through the year 2050. Four multi-temporal and multiscale species distribution models were developed for B. tectorum in the Squirrel Creek Wildfire post-burn area of Medicine Bow National Forest using eight spectral indices derived from five months of 30 m² Landsat 8 imagery corresponding to changes in species phenology and time of field data collection. These models were improved using an iterative approach in which a threshold for abundance (i.e. ≥40% foliar cover) was established from an independent dataset, and produced highly accurate maps of current B. tectorum distribution in Squirrel Creek burn with independent AUC values of 0.95 to 0.97. The most plausible model based on field observations showed the distribution of B. tectorum has increased 30% from pre-disturbance observations in the area. This model was incorporated in a habitat suitability model for B. tectorum in the same area using topographic covariates with inclusion of propagule dispersal limitations to provide an estimate of future potential distribution. Three historic (years 1991 – 2000) environmental suitability models for large wildfires (i.e. > 400 ha) in Colorado and Wyoming were developed at a 1 km² spatial resolution and tested using an independent dataset of large wildfire occurrence in the same area from the subsequent decade (years 2001 – 2010). The historic models classified points of known fire occurrence exceptionally well using decadal climate averages corresponding to the temporal resolution of wildfire occurrence and topographic covariates. When applied to an independent dataset, the test sensitivity was 0.91 for the best model (i.e. MaxEnt). We then applied the model to future climate space for the 2020s (years 2010-2039) and 2050s (years 2040-2069) using two future climate ensembles (i.e. two representative concentration pathways; RCP 4.5 and RCP 8.5 with ensemble average projections from 15 global circulation models) to rank areas for large wildfire risk in the future

Factors affecting the distribution of three non-indigenous riparian weeds in north-east England

The work presented uses a multi-disciphnary approach to examine the factors important in determining the distribution of the non-indigenous species, Impatiens glandulifera, Heracleum mantegazzianum and Fallopia japonica at a river catchment level. Distribution data for all three species along the Wear catchment, Co. Durham, were initially collected and the distribution of the species, in terms of density and abundance in different zones and habitats of the riparian system, were investigated. This work concluded that zones of the riverbank were used to differing extents by the three species. For all three species the lower riparian zone was the most important for the occurrence of populations. Data extracted from the Environment Agency's River Corridor Survey were used to provide information on characteristics of two river catchments. Examination of these data in association with the alien species distribution data highlighted differences in distribution patterns related to factors such as woodland, ruderal vegetation and bank management. Modelling species occurrences using the RCS data produced good predictive models for the two seed producing species {Impatiens and Heracleum) within a catchment but only poor models for Fallopia, with its solely vegetative method of spread. However testing such models on alternative catchments resulted in a reduction in predictive ability; the best overall models being derived from data amalgamated from both catchments. Variables selected in the models were found to concord with habitat preferences given elsewhere and also highlighted the importance of climate. Increasing the resolution of the collected data from 500m to 50m sections was found not to greatly improve the ability to predict species presence, though these data did allow predictions of Impatiens abundance to be made. Demographic analyses in different habitat types emphasised the importance of herb/ruderal vegetation, though all three species were found to persist in woodland areas despite reduced productivity. Other experiments examining the effects of climate, as represented by altitude, on the performance of the study species indicated that factors such as seed production and plant biomass varied with altitude, whereas germination did not. The thesis highlights potential shortfalls in producing predictive models for non-indigenous species based on non-equilibrium distributions and demonstrates interesting scale- dependent phenomena. It is suggested that whilst Impatiens may be largely climatically limited, Heracleum and Fallopia are more likely to be dispersal limited

Predicting tree distributions in an East African biodiversity hotspot : model selection, data bias and envelope uncertainty

The Eastern Arc Mountains (EAMs) of Tanzania and Kenya support some of the most ancient tropical rainforest on Earth. The forests are a global priority for biodiversity conservation and provide vital resources to the Tanzanian population. Here, we make a first attempt to predict the spatial distribution of 40 EAM tree species, using generalised additive models, plot data and environmental predictor maps at sub 1 km resolution. The results of three modelling experiments are presented, investigating predictions obtained by (1) two different procedures for the stepwise selection of predictors, (2) down-weighting absence data, and (3) incorporating an autocovariate term to describe fine-scale spatial aggregation. In response to recent concerns regarding the extrapolation of model predictions beyond the restricted environmental range of training data, we also demonstrate a novel graphical tool for quantifying envelope uncertainty in restricted range niche-based models (envelope uncertainty maps). We find that even for species with very few documented occurrences useful estimates of distribution can be achieved. Initiating selection with a null model is found to be useful for explanatory purposes, while beginning with a full predictor set can over-fit the data. We show that a simple multimodel average of these two best-model predictions yields a superior compromise between generality and precision (parsimony). Down-weighting absences shifts the balance of errors in favour of higher sensitivity, reducing the number of serious mistakes (i.e., falsely predicted absences); however, response functions are more complex, exacerbating uncertainty in larger models. Spatial autocovariates help describe fine-scale patterns of occurrence and significantly improve explained deviance, though if important environmental constraints are omitted then model stability and explanatory power can be compromised. We conclude that the best modelling practice is contingent both on the intentions of the analyst (explanation or prediction) and on the quality of distribution data; generalised additive models have potential to provide valuable information for conservation in the EAMs, but methods must be carefully considered, particularly if occurrence data are scarce. Full results and details of all species models are supplied in an online Appendix. (C) 2008 Elsevier B.V. All rights reserved

Improving Species Distribution Models of Continental Shelf Fishes off California

Species distribution models (SDMs) relate species occurrences, or abundances, with oceanographic or bathymetric data to predict species distributions. SDMs are used to forecast the effects of climate change on species distributions, evaluate existing protected areas and identify locations for future protected areas, estimate the effects of invasive species on a community, and determine how community assemblages change spatially or temporally. Articles describing marine SDMs are becoming increasingly common in the scientific literature; however, their efficacy for predicting the distribution and relative abundance of continental shelf fishes off California is not universally accepted. SDM performance can be affected by the inclusion of seascape variables, different spatial resolutions, the number of years of data, and the choice of statistical model. I examined how SDM performances changed with inclusion of these other factors. I combined multibeam bathymetry maps with remotely operated vehicle surveys to obtain remotely-sensed habitat data and the abundances and presence/absence of four continental shelf fishes: Blue Rockfish (Sebastes mystinus), Gopher Rockfish (Sebastes carnatus), Lingcod (Ophiodon elongatus), and Vermilion Rockfish (Sebastes miniatus). Using this information, I examined the strength of species-habitat associations and the spatial extent to which different SDM models were able to accurately predict presence/absence. Species-habitat associations for each species were determined from generalized linear models (GLMs) at three widely-spaced locations: a Northern California location (Bodega Bay or the Farallon Islands), a Central location (Point Sur or Point Buchon), and a Southern location (Harris Point). Model performance was evaluated using information about the overall accuracy, area under the curve (AUC), and Cohen’s Kappa statistics. Seascape characteristics (habitat patch area, perimeter:area ratio, and distance to habitat edge) at Point Sur were used in GLMs to determine if those variables increased model performance. Remotely-sensed habitat variables were created at three different resolutions (2, 5, and 10 m) at Bodega Bay and used in GLMs to evaluate the effect of spatial resolution on model performance. The effect of the number of years of data on model performance was evaluated at Harris Point, with GLMs and independent years of data. Additionally, at Harris Point, the choice of statistical model on model performance was evaluated with GLMs, generalized additive models, random forest, and boosted regression tree models. A coastwide model that optimized model performance was generated and tested against an independent site, Point Lobos. Lastly, an optimized model was created at Point Sur and tested against the independent sites of Point Lobos and Big Creek. Each species exhibited differences in habitat associations among the three regions. For example, Blue Rockfish distributions were positively associated with eastness at the Farallon Islands and Harris Point but negatively associated with eastness at Point Sur. Additionally, Gopher Rockfish distributions were positively associated with broad-scale BPI at each of the three locations, but exhibited differences in associations with other metrics, such as vector ruggedness measure and eastness. The inclusion of seascape models and the different spatial resolutions either decreased or did not affect model performance. In general, model performance increased with more years of data, but was dependent on the number of observations in a test year. Furthermore, model performance increased with machine learning methods such as random forest or boosted regression tree models. However, the coastwide models that were tested at Point Lobos exhibited poor performance metrics when predicting presence/absence, except for Lingcod. Additionally, models created at Point Sur and tested at Point Lobos or Big Creek were only successful for Lingcod at Point Lobos and Gopher Rockfish at Big Creek. These results suggest that SDMs for continental shelf fishes can be improved through different methods, but their efficacy in predicting presence/absence in unsurveyed areas of the coast remains poor