Matteo Marcantonio

Potential of remote sensing to predict species invasions A modelling perspective

Understanding the causes and effects of species invasions is a priority in ecology and conservation biology. One of the crucial steps in evaluating the impact of invasive species is to map changes in their actual and potential distribution and relative abundance across a wide region over an appropriate time span. While direct and indirect remote sensing approaches have long been used to assess the invasion of plant species, the distribution of invasive animals is mainly based on indirect methods that rely on environmental proxies of conditions suitable for colonization by a particular species. The aim of this article is to review recent efforts in the predictive modelling of the spread of both plant and animal invasive species using remote sensing, and to stimulate debate on the potential use of remote sensing in biological invasion monitoring and forecasting. Specifically, the challenges and drawbacks of remote sensing techniques are discussed in relation to: i) developing species distribution models, and ii) studying life cycle changes and phenological variations. Finally, the paper addresses the open challenges and pitfalls of remote sensing for biological invasion studies including sensor characteristics, upscaling and downscaling in species distribution models, and uncertainty of results.

Robust rectification of aerial photographs in an open source environment

Aerial photographs provide the basis for developing indices of landscape composition and structure as sensitive measures of large-scale environmental change over relatively long periods of time. In view of this, proper image rectification is needed to enable geometrically unbiased application of landscape metrics in order to obtain meaningful results. It is also particularly important to provide researchers with image rectification tools within an open source environment, in order to: (i) guarantee free and robust tools for processing remote sensing data, (ii) facilitate customization, and (iii) provide useful support via forums and email lists. In this paper we provide a complete description of a robust and freely licensed toolchain for orthorectifying images, which is available in the open source software GRASS GIS. We will first sketch the theoretical background behind rectification and then illustrate the workflow of the orthorectification procedure in GRASS GIS.

Identifying the environmental conditions favouring West Nile Virus outbreaks in Europe

West Nile Virus (WNV) is a globally important mosquito borne virus, with significant implications for human and animal health. The emergence and spread of new lineages, and increased pathogenicity, is the cause of escalating public health concern. Pinpointing the environmental conditions that favour WNV circulation and transmission to humans is challenging, due both to the complexity of its biological cycle, and the under-diagnosis and reporting of epidemiological data. Here, we used remote sensing and GIS to enable collation of multiple types of environmental data over a continental spatial scale, in order to model annual West Nile Fever (WNF) incidence across Europe and neighbouring countries. Multi-model selection and inference were used to gain a consensus from multiple linear mixed models. Climate and landscape were key predictors of WNF outbreaks (specifically, high precipitation in late winter/early spring, high summer temperatures, summer drought, occurrence of irrigated croplands and highly fragmented forests). Identification of the environmental conditions associated with WNF outbreaks is key to enabling public health bodies to properly focus surveillance and mitigation of West Nile virus impact, but more work needs to be done to enable accurate predictions of WNF risk.

Impact of alien species on dune systems: a multifaceted approach

We applied a multifaceted approach, in terms of taxonomic, phylogenetic and functional diversity, to study at fine scale how three plant communities occurring in a Mediterranean dune have been affected by the encroachment of alien species. We sampled 81 sites in a Site of Community Importance in Central Italy. Past and present land use/cover data have been derived using GIS and remote sensing tools. Information on plants phylogenesis and functional traits has been gathered from several databases. Ecological variables have been collected. GLMs in conjunction with an Information Based approach were used to model species composition, richness and phylogenetic diversity. Multivariate analysis has been used to study functional diversity. The results outlined how total species richness is related to recent land transformations and to a set of environmental factors. The analyses of functional and phylogenetic diversity support the idea that alien species significantly affect the functional and phylogenetic characteristics of the native plant communities. Habitat filtering seems to be predominant in not-invaded plots, whereas limiting similarity/niche differentiation is predominant in driving community assembly of invaded communities. The attained scenario depicts the spread of a reduced group of alien species phylogenetically and functionally well-differentiated, able to reduce the abundance of native species, not to exclude them though. Ultimately, the multifaceted approach assisted in understanding the community assembly of dune vegetation, and to discern the relative impact of alien species on native plant communities. Such approach represents a crucial step to achieve an efficient management of dune habitats, as useful tool to monitor and to effectively protect their biodiversity and functioning.

Anticipating species distributions: Handling sampling effort bias under a Bayesian framework

Anticipating species distributions in space and time is necessary for effective biodiversity conservation and for prioritising management interventions. This is especially true when considering invasive species. In such a case, anticipating their spread is important to effectively plan management actions. However, considering uncertainty in the output of species distribution models is critical for correctly interpreting results and avoiding inappropriate decision-making. In particular, when dealing with species inventories, the bias resulting from sampling effort may lead to an over- or under-estimation of the local density of occurrences of a species. In this paper we propose an innovative method to i) map sampling effort bias using cartogram models and ii) explicitly consider such uncertainty in the modeling procedure under a Bayesian framework, which allows the integration of multilevel input data with prior information to improve the anticipation species distributions.

A multi-temporal approach in MaxEnt modelling: A new frontier for land use/land cover change detection

Abstract Land-cover change, a major driver of the distribution and functioning of ecosystems, is characterized by a high diversity of patterns of change across space and time. Thus, a large amount of information is necessary to analyse change and develop plans for proper management of natural resources. In this work we tested MaxEnt algorithm in a completely remote land-cover classification and change analysis. In order to provide an empirical example, we selected south-eastern Italian Alps, manly Trentino-South Tyrol, as test region. We classified two Landsat images (1976 and 2001) in order to forecast probability of occurrence for unsampled locations and to determine the best subset of predictors (spectral bands). A difference map for each land cover class, representing the difference between 1976 and 2001 probability of occurrence values, was built. In order to better address the patterns of change analysis, we put together difference maps and topographic variables. The latter are considered, at least in the study area, as the main environmental drivers of land-use change, in connection with climate change. Our results indicate that the selected algorithm, applied to land cover classes, can provide reliable data, especially when referring to classes with homogeneous texture properties and surface reflectance. The performed models had satisfactory predictive performance, showing relatively clear patterns of difference between the two considered time steps. The development of a methodology that, in the absence of field data, allow to obtain data on land use change dynamics, is of extreme importance for land planning and management.

Spatio-ecological complexity measures in GRASS GIS

Good estimates of ecosystem complexity are essential for a number of ecological tasks: from biodiversity estimation, to forest structure variable retrieval, to feature extraction by edge detection and generation of multifractal surface as neutral models for e.g. feature change assessment. Hence, measuring ecological complexity over space becomes crucial in macroecology and geography. Many geospatial tools have been advocated in spatial ecology to estimate ecosystem complexity and its changes over space and time. Among these tools, free and open source options especially offer opportunities to guarantee the robustness of algorithms and reproducibility. In this paper we will summarize the most straightforward measures of spatial complexity available in the Free and Open Source Software GRASS GIS, relating them to key ecological patterns and processes.

Soil depth shapes plant functional diversity in granite outcrops vegetation of Southwestern Australia

Background: The arid and nutrient-poor Southwestern Australia is one of the global biodiversity hotspots. Embedded in this landscape, granite outcrops are considered terrestrial insular habitats supporting habitat heterogeneity when compared to the more homogenous surrounds. Ecology of plant species and communities on granite outcrops has been addressed in numerous studies. However, functional diversity (FD) in context of the environmental heterogeneity remained unexplored. Aims: We tested whether mesic deep-soil habitats on granite outcrops can sustain larger FD than dry shallow-soil habitats. Methods: We calculated FD for dominant species for five single traits (leaf dry matter content, foliar δ13C, foliar C:N ratio, plant height and specific leaf area) and their combinations. We employed Generalized Additive Mixed Models to quantify the relationship between selected climate and soil depth variables, and FD. Results: More benign (deep-soil) habitats supported larger FD for foliar C:N, plant height and for multiple traits than did shallow-soil habitats. Conclusions: We suggest that: (1) functional diversification, likely aimed at avoiding intra- and interspecific competition for light and nutrients acquisition, might be the important factor in deep-soil habitats; (2) deep-soils patches on and around granite outcrops may serve as ecological microrefugia for biota associated with resource-rich environments.

Measuring β-diversity by remote sensing: a challenge for biodiversity monitoring

Biodiversity includes multiscalar and multitemporal structures and processes, with different levels of functional organization, from genetic to ecosystemic levels. One of the mostly used methods to infer biodiversity is based on taxonomic approaches and community ecology theories. However, gathering extensive data in the field is difficult due to logistic problems, overall when aiming at modelling biodiversity changes in space and time, which assumes statistically sound sampling schemes. In this view, airborne or satellite remote sensing allow to gather information over wide areas in a reasonable time. Most of the biodiversity maps obtained from remote sensing have been based on the inference of species richness by regression analysis. On the contrary, estimating compositional turnover (beta-diversity) might add crucial information related to relative abundance of different species instead of just richness. Presently, few studies have addressed the measurement of species compositional turnover from space. Extending on previous work, in this manuscript we propose novel techniques to measure beta-diversity from airborne or satellite remote sensing, mainly based on: i) multivariate statistical analysis, ii) the spectral species concept, iii) self-organizing feature maps, iv) multi- dimensional distance matrices, and the v) Raos Q diversity. Each of these measures allow to solve one or several issues related to turnover measurement. This manuscript is the first methodological example encompassing (and enhancing) most of the available methods for estimating beta-diversity from remotely sensed imagery and potentially relate them to species diversity in the field.

Mapping of Aedes albopictus Abundance at a Local Scale in Italy

Given the growing risk of arbovirus outbreaks in Europe, there is a clear need to better describe the distribution of invasive mosquito species such as Aedes albopictus. Current challenges consist in simulating Ae. albopictus abundance, rather than its presence, and mapping its simulated abundance at a local scale to better assess the transmission risk of mosquito-borne pathogens and optimize mosquito control strategy. During 2014–2015, we sampled adult mosquitoes using 72 BG-Sentinel traps per year in the provinces of Belluno and Trento, Italy. We found that the sum of Ae. albopictus females collected during eight trap nights from June to September was positively related to the mean temperature of the warmest quarter and the percentage of artificial areas in a 250 m buffer around the sampling locations. Maps of Ae. albopictus abundance simulated from the most parsimonious model in the study area showed the largest populations in highly artificial areas with the highest summer temperatures, but with a high uncertainty due to the variability of the trapping collections. Vector abundance maps at a local scale should be promoted to support stakeholders and policy-makers in optimizing vector surveillance and control.