Modelling invasive species-landscape interactions using high resolution, spatially explicit models

Invasive species can cause a wide range of damages from destruction of indigenous and productive ecosystems to introduction of vectors to human and animal diseases. In many countries, measures taken to prevent the establishment of invasive species are known to significantly reduce the potential damage that might be caused. As part of those measures, species distribution models (SDMs) are used to predict suitable habitats for highly invasive species so that appropriate strategies to prevent their establishment and further spread can be designed. When species distribution models (SDMs) are used for practical applications, accounting for their uncertainty becomes a priority. However, despite their wide use, reporting the uncertainty of SDM predictions is not well practiced. The primary aim of the research in this thesis was to identify and quantify uncertainty associated with model predictions of species distributions. The major research question was, why do different models give dissimilar predictions for the same species and/or location? Discrepancy among model results is one of the major issues that affects the perception of their reliability and their capacity to inform policy decisions. In this thesis, the effect of factors considered to influence model performance and drive uncertainty in model predictions, was investigated. The particular factors were, 1) pseudo absence selection, 2) the individual and combined effect of predictor data, dimension reduction methods, and model types on model performance, and, 3) variation within the occurrence data for a given species. Following these investigations, improved procedures developed in this research were used to, 1) investigate the use of a simple mechanistic model to enhance results of correlative species distribution models in a hybrid approach and 2) improve a dispersal model that can be used to research the potential spread of an invasive species once it has established in a new habitat. A multi-factor study to investigate the effect of pseudo-absence selection on model performance showed that not only pseudo-absences affect individual models but also consensus among model predictions. To improve individual model performance as well as model consensus, an improved pseudo-absence selection method was developed that balances the geographic and environmental space for selecting pseudo-absences. The investigation of the individual and combined effect of predictor data, dimension reduction methods and model types on model performance, showed that the type of model is a major factor that affects model performance. The results of this research showed that the combination of appropriate explanatory variables and dimension reduction could increase individual model performance as well as model consensus. Additionally, novel indices that can be used to assess internal characteristics of the environmental predictors and data-pre-processing methods for optimized model performance, were developed. Another important factor that contributes to model uncertainty is the reliability of the species occurrence data. While the precision of geographical references used for such data and its effect on model predictions and associated uncertainty, has been well studied, however, variation within the occurrence or presence data for a given species has been less investigated. Two case-studies were used to determine the effects of local adaptation within a species, on model predictions. It was found that apparent local adaptations resulting in ecotypes within a species could affect model predictions. As a result, methods are proposed to detect the effect of within presence data variation and an appropriate method to model potential distributions of species with such variable data, is illustrated. Following improved procedures proposed in this research, the use of a simple mechanistic model to enhance results from correlative species distribution models was investigated. While a well parameterised mechanistic model for species distribution modelling is the ideal, such models need detailed biological data that are most often not available, especially for many invasive insects. In this study, a simple generalized mechanistic model was used to complement correlative distribution predictions. The resulting predictions from the hybrid model were shown to facilitate the identification of under- or over-predicted areas by correlative models such that its use resulted in improved overall prediction. The enhanced protocols developed in this thesis were finally used to improve a dispersal model that can be used to project the spread of an invasive species once it has established in a new habitat by the integration of multiple scale suitability layers to represent a realistic landscape over which the dispersal of a given species can be studied. Selective landscape recoding was used to customize the landscape based on specific species-landscape interactions, to improve dispersal rate estimation and dispersal pattern determination. This thesis presents novel methods that can be implemented to significantly increase model consensus for species distribution predictions. More important, however, the research highlights the need for implementing multi-model and multi-scenario modelling frameworks to reduce model uncertainty that can result from inappropriate use of modelling components. The findings in this thesis form the basis for research aimed at further improvement of species distribution models to provide more reliable tools for applications in invasive species management, biodiversity protection, environmental sustainability and climate change management

Multi-Scenario Species Distribution Modeling

Correlative species distribution models (SDMs) are increasingly being used to predict suitable insect habitats. There is also much criticism of prediction discrepancies among different SDMs for the same species and the lack of effective communication about SDM prediction uncertainty. In this paper, we undertook a factorial study to investigate the effects of various modeling components (species-training-datasets, predictor variables, dimension-reduction methods, and model types) on the accuracy of SDM predictions, with the aim of identifying sources of discrepancy and uncertainty. We found that model type was the major factor causing variation in species-distribution predictions among the various modeling components tested. We also found that different combinations of modeling components could significantly increase or decrease the performance of a model. This result indicated the importance of keeping modeling components constant for comparing a given SDM result. With all modeling components, constant, machine-learning models seem to outperform other model types. We also found that, on average, the Hierarchical Non-Linear Principal Components Analysis dimension-reduction method improved model performance more than other methods tested. We also found that the widely used confusion-matrix-based model-performance indices such as the area under the receiving operating characteristic curve (AUC), sensitivity, and Kappa do not necessarily help select the best model from a set of models if variation in performance is not large. To conclude, model result discrepancies do not necessarily suggest lack of robustness in correlative modeling as they can also occur due to inappropriate selection of modeling components. In addition, more research on model performance evaluation is required for developing robust and sensitive model evaluation methods. Undertaking multi-scenario species-distribution modeling, where possible, is likely to mitigate errors arising from inappropriate modeling components selection, and provide end users with better information on the resulting model prediction uncertainty

Distortionary Agricultural Policies: Their Productivity, Location and Climate Variability Implications for South Africa During the 20th Century

During the first half of the 20th century, the policy stance towards South African agriculture swung from suppression to support. More recently, the agricultural support policies were eliminated. Using newly constructed, long-run (1918-2015) data concerning maize production, yield and average price, we show these switching agricultural policy regimes had significant production, productivity, and climate risk implications for the maize sector. At its peak, this policy-induced movement reduced maize productivity by between 7.9 and 15.3 percent. The removal of the distortions coincided with a contraction in the total area planted to maize, but some spatial productivity perturbations still persist

Status of Invasive Plants and Management Techniques in Minnesota: Results from a 2018 Survey

Invasive weeds are an ongoing concern in Minnesota. Despite broad interest in addressing invasive plant-related problems in the state, there are relatively few datasets regarding species-specific concerns, costs, and management efforts. In this study, we address this knowledge gap using a questionnaire-survey approach. We asked landowners, stakeholders, and land managers a series of questions regarding thirteen invasive weeds in Minnesota, including both buckthorn species (Rhamnus cathartica, Frangula alnus) and wild parsnip (Pastinaca sativa). Respondents (249 total) shared their concerns, cost information, and information regarding recent and planned management efforts for these weeds. Frequently-cited concerns varied considerably by species and type of respondent, but broadly included the potential impacts of weeds on conservation and ecology, weed-related impacts on forest regeneration, and weed-related impacts on recreation. Reported costs and management approaches varied depending on respondent type (private landowner or public lands professional), with public land professionals generally more willing and able to implement more expensive management approaches (i.e., mechanical removal, controlled burn) than private landowners. The broad results and data from this survey may be of interest to a number of researchers and natural resource professionals, as it provides some foundational context for further analyses

Pseudo-absence points from the four pseudo-absence selection methods.

<p>Pseudo-absence points plotted with presence points on the first three principal components of the training dataset (Species: <i>D. v. virgifera),</i> (A) SM1, (B) SM2, (C) SM3, and (D) SM4.</p

List of variables selected using four pseudo-absence selection methods for the two target species.

*<p>aa = <i>Aedes albopictus</i>, dvv = <i>Diabrotica v. virgifera</i>, SM1 = random pseudo-absence selection method, SM2 = spatially constrained random pseudo-absence selection method, SM3 = 2-step environmental profiling pseudo-absence selection method, SM4 = 3-step environmental profiling with spatial constraint pseudo-absence selection method.</p

Map of global presence data for <i>A. albopictus</i> and <i>D. v. virgifera.</i>

<p>Map of global presence data for <i>A. albopictus</i> and <i>D. v. virgifera.</i></p

Percentages of predicted presences and respective model consensus on predictions in New Zealand.

<p>(A), Asian tiger mosquito (<i>A. albopictus).</i> (B), Western corn rootworm (<i>D. v. virgifera</i>).</p

Change in heathland fire sizes inside vs. outside the Bale Mountains National Park, Ethiopia, over 50 years of fire-exclusion policy: lessons for REDD+

In flammable shrublands fire size often depends on local management. Policy and land use change can drastically alter fire regimes, affecting livelihoods, biodiversity, and carbon storage. In Ethiopia, burning of vegetation is banned, but the burn ban is more strongly enforced inside the Bale Mountains National Park. We investigated if and how policy and land use change have affected fire regimes inside/outside the park. The park was established in 1969, and both studied areas have been part of a new REDD+ project since 2013. Our hypothesis is that burnt heathland stands are nonflammable and act as fuel breaks, and hence that reduced ignition rates leads to larger fires. To quantify change we analyzed remote-sensed imagery from 10 fire-seasons between 1968 and 2017, quantifying sizes of resprouting Erica stands and recording their postfire age. To elucidate underlying mechanisms of change we interviewed 41 local smallholders. There was a five order of magnitude variation in patch size ( 1000 ha). A significant interaction was found between year and site (inside/outside park) in explaining patch size, indicating that the park establishment has affected fire size. Inside the park there was a tendency of patch size increase and outside a clear decrease. Especially the largest fires (> 100 ha) increased in numbers inside the park but not outside. Respondents confirmed that large fires have increased in frequency and attributed this mainly to lack of fuel breaks and the fact that fires today are ignited in a more uncontrolled manner later in the dry season. Outside the park respondents explained that fires have become smaller because of increased ignition and intensified grazing. Both situations degrade pasture and threaten Erica shrub survival. For flammable ecosystems, REDD+ fire-exclusion policies need updating, and in this case complemented with a community-based fire management program making use of the vivid local traditional fire knowledge

Kappa values of models for the four pseudo-absence selection methods and two species datasets.

<p><i>Aa = A. albopictus</i>, Dvv = <i>D. v. virgifera</i>, values above the red broken line are in the excellent band of the kappa index.</p