Modelling Distributions of Rove Beetles in Mountainous Areas Using Remote Sensing Data

Mountain ecosystems are biodiversity hotspots that are increasingly threatened by climate and land use/land cover changes. Long-term biodiversity monitoring programs provide unique insights into resulting adverse impacts on plant and animal species distribution. Species distribution models (SDMs) in combination with satellite remote sensing (SRS) data offer the opportunity to analyze shifts of species distributions in response to these changes in a spatially explicit way. Here, we predicted the presence probability of three different rove beetles in a mountainous protected area (Gran Paradiso National Park, GPNP) using environmental variables derived from Landsat and Aster Global Digital Elevation Model data and an ensemble modelling approach based on five different model algorithms (maximum entropy, random forest, generalized boosting models, generalized additive models, and generalized linear models). The objectives of the study were (1) to evaluate the potential of SRS data for predicting the presence of species dependent on local-scale environmental parameters at two different time periods, (2) to analyze shifts in species distributions between the years, and (3) to identify the most important species-specific SRS predictor variables. All ensemble models showed area under curve (AUC) of the receiver operating characteristics values above 0.7 and true skills statistics (TSS) values above 0.4, highlighting the great potential of SRS data. While only a small proportion of the total area was predicted as highly suitable for each species, our results suggest an increase of suitable habitat over time for the species Platydracus stercorarius and Ocypus ophthalmicus, and an opposite trend for Dinothenarus fossor. Vegetation cover was the most important predictor variable in the majority of the SDMs across all three study species. To better account for intra- and inter-annual variability of population dynamics as well as environmental conditions, a continuation of the monitoring program in GPNP as well as the employment of SRS with higher spatial and temporal resolution is recommended

FACILITATING AQUATIC INVASIVE SPECIES MANAGEMENT USING SATELLITE REMOTE SENSING AND MACHINE LEARNING FRAMEWORKS

The urgent decision-making needs of invasive species managers can be better met by the integration of biodiversity big data with large-domain models and environmental data products in the form of new workflows and tools that facilitate data utilization across platforms. Timely risk assessments allow for the spatial prioritization of monitoring that could streamline invasive species management paradigms and invasive species’ ability to prevent irreversible damage, such that decision makers can focus surveillance and intervention efforts where they are likely to be most effective under budgetary and resource constraints. I present a workflow that generates rapid spatial risk assessments on aquatic invasive species by combining occurrence data, spatially explicit environmental data, and an ensemble approach to species distribution modeling using five machine learning algorithms. For proof of concept and validation, I tested this workflow using extensive spatial and temporal occurrence data from Rainbow Trout (RBT; Oncorhynchus mykiss) invasion in the upper Flathead River system in northwestern Montana, USA. Due to this workflow’s high performance against cross-validated datasets (87% accuracy) and congruence with known drivers of RBT invasion, I developed a tool that generates agile risk assessments based on the above workflow and suggest that it can be generalized to broader spatial and taxonomic scales in order to provide data-driven management information for early detection of potential invaders. I then use this tool as technical input for a management framework that provides guidance for users to incorporate and synthesize the component features of the workflow and toolkit to derive actionable insight in an efficient manner

Impacts of anthropogenic climate change on tropical montane forests: an appraisal of the evidence

This is the final version. Available from Wiley via the DOI in this record. In spite of their small global area and restricted distributions, tropical montane forests (TMFs) are biodiversity hotspots and important ecosystem services providers, but are also highly vulnerable to climate change. To protect and preserve these ecosystems better, it is crucial to inform the design and implementation of conservation policies with the best available scientific evidence, and to identify knowledge gaps and future research needs. We conducted a systematic review and an appraisal of evidence quality to assess the impacts of climate change on TMFs. We identified several skews and shortcomings. Experimental study designs with controls and long-term (≥10 years) data sets provide the most reliable evidence, but were rare and gave an incomplete understanding of climate change impacts on TMFs. Most studies were based on predictive modelling approaches, short-term (<10 years) and cross-sectional study designs. Although these methods provide moderate to circumstantial evidence, they can advance our understanding on climate change effects. Current evidence suggests that increasing temperatures and rising cloud levels have caused distributional shifts (mainly upslope) of montane biota, leading to alterations in biodiversity and ecological functions. Neotropical TMFs were the best studied, thus the knowledge derived there can serve as a proxy for climate change responses in under-studied regions elsewhere. Most studies focused on vascular plants, birds, amphibians and insects, with other taxonomic groups poorly represented. Most ecological studies were conducted at species or community levels, with a marked paucity of genetic studies, limiting understanding of the adaptive capacity of TMF biota. We thus highlight the long-term need to widen the methodological, thematic and geographical scope of studies on TMFs under climate change to address these uncertainties. In the short term, however, in-depth research in well-studied regions and advances in computer modelling approaches offer the most reliable sources of information for expeditious conservation action for these threatened forests.Natural Environment Research Council (NERC)Mexican National Council of Science and Technology (CONACyT)University of Southampto

An analysis of the influence of thermal heterogeneity within mountain environments on the spatial distribution and thermal biological characteristics of surface-dwelling arthropods

Preliminary findings from previous research into the thermal biological characteristics of mountain arthropods have determined that temperature plays a critical role in the emergence timing, growth and development rates, dispersal, fecundity, and mortality of these taxa. However, a disconnect currently exist with regards to the application of this lab derived data to real world environments as current meteorological models lack the sophistication and resolution to describe the thermal conditions experienced by surface-dwelling arthropods. The aim of this thesis is to investigate thermal distribution across a temperate mountain landscape in southern Alberta with the goal of contextualizing this information in a manner which was relevant to these taxa. As a result, average air and surface temperature models were created on annual and monthly time scales using in-situ measurements as input data for a variety of interpolation methods employed in Arc GIS Pro (version 2.9.1). Temperature models were then used to delineate thermally defined ecosystems in which overwintering suitability was determined as a by-product of the severity, duration, and frequency of cold exposure events while summer suitability was determined by the number of net growing days experienced under threshold temperatures of 5 °C, 8 °C, and 10 °C. In this environment, surface temperatures ranged from -21.17 °C to 51.18 °C, sub zero temperatures persisted for as many as 214 consecutive days, as many as 45 freeze-thaw cycles occurred within a single calendar year and a range of 107 to 172 average net growing days over 5 °C were recorded. To compliment this meteorological investigation, this document also serves as a first-hand account of the composition, abundance, and distribution of 35 families of surface-dwelling arthropods present within Castle Provincial Park. Several rare and endemic species belonging to these families such as Cyphoderris monstrosa and Grylloblatta Camodeiformis were also documented. Finally, inferences as to the thermal biological characteristics of these taxa are made based on the locations where they were collected, and estimations are made as to how the distribution of thermally suitable habitat for these creatures may change in accordance with future climate projections

Biology, ecology and management of "Monochamus galloprovincialis" (Olivier): vector of the pine wood nematode

La reciente introducción del “nematodo del pino” en Europa, obliga al desarrollo y aplicación urgente de medidas para la contención de la enfermedad. La actividad humana ha resultado ser el principal mecanismo de dispersión de la enfermedad a larga distancia, pero el conocimiento de la dispersión a escala local es de similar importancia para la toma decisiones sobre las medidas a aplicar. El conocimiento de las dinámicas poblacionales del vector por tanto, aquí juega un papel clave. Conocer cuál es su preferencia de hospedantes, cómo y cuánto se va a dispersar y a qué distancia sería capaz de llegar, aun cuando se encuentre sin alimento, junto con un buen dispositivo de captura, posibilitarán la detección temprana de la enfermedad o incluso el desarrollo de técnicas de control biotécnico como la captura masiva.Departamento de Producción Vegetal y Recursos Forestale

Biology, ecology and management of "Monochamus galloprovincialis" (Olivier): vector of the pine wood nematode

La reciente introducción del “nematodo del pino” en Europa, obliga al desarrollo y aplicación urgente de medidas para la contención de la enfermedad. La actividad humana ha resultado ser el principal mecanismo de dispersión de la enfermedad a larga distancia, pero el conocimiento de la dispersión a escala local es de similar importancia para la toma decisiones sobre las medidas a aplicar. El conocimiento de las dinámicas poblacionales del vector por tanto, aquí juega un papel clave. Conocer cuál es su preferencia de hospedantes, cómo y cuánto se va a dispersar y a qué distancia sería capaz de llegar, aun cuando se encuentre sin alimento, junto con un buen dispositivo de captura, posibilitarán la detección temprana de la enfermedad o incluso el desarrollo de técnicas de control biotécnico como la captura masiva.Departamento de Producción Vegetal y Recursos Forestale