Francesco Tonini, PhD, GISP

Scientists and land managers are increasingly monitoring forest microclimate environments to better understand ecosystem processes, such as carbon sequestration and the population dynamics of species. Obtaining reliable time-series measurements of microclimate conditions is often hindered by missing and erroneous values. In this study, we compare spatio-temporal techniques, space–time kriging (probabilistic) and empirical orthogonal functions (deterministic), for reconstructing hourly time… Show more

Scientists and land managers are increasingly monitoring forest microclimate environments to better understand ecosystem processes, such as carbon sequestration and the population dynamics of species. Obtaining reliable time-series measurements of microclimate conditions is often hindered by missing and erroneous values. In this study, we compare spatio-temporal techniques, space–time kriging (probabilistic) and empirical orthogonal functions (deterministic), for reconstructing hourly time series of near-surface air temperature recorded by a dense network of 200 forest understory sensors across a heterogeneous 349 km2 region in northern California. The reconstructed data were also aggregated to daily mean, minimum, and maximum in order to understand the sensitivity of model predictions to temporal scale of measurement. Empirical orthogonal functions performed best at both the hourly and daily time scale. We analyzed several scenarios to understand the effects that spatial coverage and patterns of missing data may have on model accuracy: (a) random reduction of the sample size/density by 25%, 50%, and 75% (spatial coverage); and (b) random removal of either 50% of the data, or three consecutive months of observations at randomly chosen stations (random and seasonal temporal missingness, respectively). Here, space–time kriging was less sensitive to scenarios of spatial coverage, but more sensitive to temporal missingness, with less marked differences between the two approaches when data were aggregated on a daily time scale. This research contextualizes trade-offs between techniques and provides practical guidelines, with free source code, for filling data gaps depending on the spatial density and coverage of measurements. Show less

The Formosan subterranean termite, Coptotermes formosanus Shiraki, is a pest of major
economic concern. This termite is particularly known for its tendency to establish populations in nonendemic areas via maritime vessels as well as human-aided transport of infested materials. The natural spread of this species after new introductions occurs in part by dispersal flights originating from mature colonies. Dispersal flight activity is also the primary variable for the evaluation of area-wide… Show more

The Formosan subterranean termite, Coptotermes formosanus Shiraki, is a pest of major
economic concern. This termite is particularly known for its tendency to establish populations in nonendemic areas via maritime vessels as well as human-aided transport of infested materials. The natural spread of this species after new introductions occurs in part by dispersal flights originating from mature colonies. Dispersal flight activity is also the primary variable for the evaluation of area-wide management programs. Few studies exist describing the dynamics and distribution of a typical dispersal flight for this species. The present study used data collected by mark–recapture of C. formosanus alates over 12 individual evenings of dispersal flights in the New Orleans French Quarter. In this study, we found that for one selected flight dispersal location, which was not affected by a high density of trap locations nearby, alates flew on average 621m from their parent colony. A new record of a 1,300-m dispersal flight was recorded. Spatial analysis showed that neither wind nor light affected the direction of flight, which may, however, be attributed to scarce light and wind measurements in the study region. Show less

The type of data an individual contributor adds to OpenStreetMap (OSM) varies by region. The local knowledge of a data contributor allows for the collection and editing of detailed features such as small trails, park benches or fire hydrants, as well as adding attribute information that can only be accessed locally. As opposed to this, satellite imagery that is provided as background images in OSM data editors, such as ID, Potlatch or JOSM, facilitates the contribution of less detailed data… Show more

The type of data an individual contributor adds to OpenStreetMap (OSM) varies by region. The local knowledge of a data contributor allows for the collection and editing of detailed features such as small trails, park benches or fire hydrants, as well as adding attribute information that can only be accessed locally. As opposed to this, satellite imagery that is provided as background images in OSM data editors, such as ID, Potlatch or JOSM, facilitates the contribution of less detailed data through on-screen digitizing, oftentimes for areas the contributor is less familiar with. Knowing whether an area is part of a contributor’s home region or not can therefore be a useful predictor of OSM data quality for a geographic region. This research explores the editing history of nodes and ways for 13 highly active OSM members within a two-tiered clustering process to delineate an individual mapper’s home region from remotely mapped areas. The findings are evaluated against those found with a previously introduced method which determines a contributor’s home region solely based on spatial clustering of created nodes. The comparison shows that both methods are able to delineate similar home regions for the 13 contributors with some differences. Show less

Predicting the potential habitat of species under both current and future climate change scenarios is crucial for monitoring invasive species and understanding a species' response to different environmental conditions. Frequently, the only data available on a species is the location of its occurrence (presence-only data). Using occurrence records only, two models were used to predict the geographical distribution of two destructive invasive termite species, Coptotermes gestroi (Wasmann) and… Show more

Predicting the potential habitat of species under both current and future climate change scenarios is crucial for monitoring invasive species and understanding a species' response to different environmental conditions. Frequently, the only data available on a species is the location of its occurrence (presence-only data). Using occurrence records only, two models were used to predict the geographical distribution of two destructive invasive termite species, Coptotermes gestroi (Wasmann) and Coptotermes formosanus Shiraki. The first model uses a Bayesian linear logistic regression approach adjusted for presence-only data while the second one is the widely used maximum entropy approach (Maxent). Results show that the predicted distributions of both C. gestroi and C. formosanus are strongly linked to urban development. The impact of future scenarios such as climate warming and population growth on the biotic distribution of both termite species was also assessed. Future climate warming seems to affect their projected probability of presence to a lesser extent than population growth. The Bayesian logistic approach outperformed Maxent consistently in all models according to evaluation criteria such as model sensitivity and ecological realism. The importance of further studies for an explicit treatment of residual spatial autocorrelation and a more comprehensive comparison between both statistical approaches is suggested. Show less

Spatially-explicit simulation models can help state and local regulatory agencies to predict both the rate and direction of the spread of an invasive species from a set of surveyed locations. Such models can be used to develop successful early detection, quarantine, or eradication plans based on the predicted areas of infestation. Individual-based models (IBMs) are often used to replicate the dynamics of complex systems and are both able to incorporate individual differences and local… Show more

Spatially-explicit simulation models can help state and local regulatory agencies to predict both the rate and direction of the spread of an invasive species from a set of surveyed locations. Such models can be used to develop successful early detection, quarantine, or eradication plans based on the predicted areas of infestation. Individual-based models (IBMs) are often used to replicate the dynamics of complex systems and are both able to incorporate individual differences and local interactions among organisms, as well as spatial details. In this work, we introduce a new stochastic lattice-based model for simulating the spread of invasive termites over a landscape and compare it to a recently published stochastic individual-based approach, based on the same ecological parameters, with the goal of improving its computational efficiency. The two modeling frameworks were tested over a homogeneous landscape with randomly located sources of infestation. Further, the setting of a case-study of an invasive termite, Nasutitermes corniger (Motschulsky), was used to simulate the spread of the species in Dania Beach, Florida, U.S.A., and the results of the proposed model were compared with an earlier application of the IBM over the same area. The results show that the extent of the infested areas predicted by the new lattice-based model is similar, thus comparable, to the individual-based model while improving the computation time significantly. The simulation presented in this work could be used by the regulatory authorities to draw one or more areas of intervention instead of wasting resources by randomly surveying unknown perimeters. Show less

The ability to manage geospatial data has made Geographic Information Systems (GIS) an important tool for a wide range of applications over the past decades, including management of natural resources, analysis of wildlife movement, ecological niche modeling, or land records management. This paper illustrates, using invasive termite species as examples, how GIS can assist in identifying their potential sources of infestations and model their spread in urban South Florida. The first case study… Show more

The ability to manage geospatial data has made Geographic Information Systems (GIS) an important tool for a wide range of applications over the past decades, including management of natural resources, analysis of wildlife movement, ecological niche modeling, or land records management. This paper illustrates, using invasive termite species as examples, how GIS can assist in identifying their potential sources of infestations and model their spread in urban South Florida. The first case study shows that the Formosan subterranean termite, Coptotermes formosanus Shiraki, and the Asian subterranean termite, Coptotermes gestroi (Wasmann) (Isoptera: Rhinotermitidae), were introduced into and dispersed across South Florida by sailboats and yachts. The second case study shows an agent-based model to simulate the natural spread of Nasutitermes corniger (Motschulsky) (Isoptera: Termitidae) in Dania Beach, FL. This paper provides an overview of basic functionalities in GIS and demonstrates how they can be customized for advanced modeling and simulation. Show less

The analysis and forecasting of extreme climatic events has become increasingly relevant to planning effective financial and food-related interventions in third-world countries. This presentation illustrates the steps the authors have taken to build a customized toolbox within ArcGIS for the assessment of drought risk over a given area, making use of the extreme value theory. The Python programming language was used to customize the toolbox, while R, an open source programming language for… Show more

The analysis and forecasting of extreme climatic events has become increasingly relevant to planning effective financial and food-related interventions in third-world countries. This presentation illustrates the steps the authors have taken to build a customized toolbox within ArcGIS for the assessment of drought risk over a given area, making use of the extreme value theory. The Python programming language was used to customize the toolbox, while R, an open source programming language for statistical computing, was integrated to run the statistical analysis. As data input, users are asked to provide historical raster maps of a chosen drought indicator. A case study is illustrated, where return levels of absolute NDVI variations were estimated over the Ethiopian sub-region of South Tigray in order to identify the extent of the areas prone to severe drought conditions. The toolbox can also be used with other drought related indicators. Show less

Invasive termites are destructive insect pests that cause billions of dollars in property damage every year. Termite species can be transported overseas by maritime vessels. However, only if the climatic conditions are suitable will the introduced species flourish. Models predicting the areas of infestation following initial introduction of an invasive species could help regulatory agencies develop successful early detection, quarantine, or eradication efforts. At present, no model has been… Show more

Invasive termites are destructive insect pests that cause billions of dollars in property damage every year. Termite species can be transported overseas by maritime vessels. However, only if the climatic conditions are suitable will the introduced species flourish. Models predicting the areas of infestation following initial introduction of an invasive species could help regulatory agencies develop successful early detection, quarantine, or eradication efforts. At present, no model has been developed to estimate the geographic spread of a termite infestation from a set of surveyed locations. In the current study, we used actual field data as a starting point, and relevant information on termite species to develop a spatially-explicit stochastic individual-based simulation to predict areas potentially infested by an invasive termite, Nasutitermes corniger (Motschulsky), in Dania Beach, FL. The Monte Carlo technique is used to assess outcome uncertainty. A set of model realizations describing potential areas of infestation were considered in a sensitivity analysis, which showed that the model results had greatest sensitivity to number of alates released from nest, alate survival, maximum pheromone attraction distance between heterosexual pairs, and mean flight distance. Results showed that the areas predicted as infested in all simulation runs of a baseline model cover the spatial extent of all locations recently discovered. The model presented in this study could be applied to any invasive termite species after proper calibration of parameters. The simulation herein can be used by regulatory authorities to define most probable quarantine and survey zones. Show less

The analysis and forecasting of extreme climatic events has become increasingly relevant to planning effective financial and food-related interventions in third-world countries. Natural disasters and climate change, both large and small scale, have a great impact on non-industrialized populations who rely exclusively on activities such as crop production, fishing, and similar livelihood activities. It is important to identify the extent of the areas prone to severe drought conditions in order… Show more

The analysis and forecasting of extreme climatic events has become increasingly relevant to planning effective financial and food-related interventions in third-world countries. Natural disasters and climate change, both large and small scale, have a great impact on non-industrialized populations who rely exclusively on activities such as crop production, fishing, and similar livelihood activities. It is important to identify the extent of the areas prone to severe drought conditions in order to study the possible consequences of the drought on annual crop production. In this paper, we aim to identify such areas within the South Tigray zone, Ethiopia, using a transformation of the Normalized Difference Vegetation Index (NDVI) called Absolute Difference NDVI (ADVI). Negative NDVI shifts from the historical average can generally be linked to a reduction in the vigor of local vegetation. Drought is more likely to increase in areas where negative shifts occur more frequently and with high magnitude, making it possible to spot critical situations. We propose a new methodology for the assessment of drought risk in areas where crop production represents a primary source of livelihood for its inhabitants. We estimate ADVI return levels pixel per pixel by fitting extreme value models to independent monthly minima. The study is conducted using SPOT-Vegetation (VGT) ten-day composite (S10) images from April 1998 to March 2009. In all short-term and long-term predictions, we found that central and southern areas of the South Tigray zone are prone to a higher drought risk compared to other areas. Show less

Termites are destructive insect pests that cause billions of dollars in property damage every year. Exotic invasive species have become established pests in parts of the United States. Spread models predicting the areas of infestation following initial introduction of an invasive species are important for an authoritative response. This presentation illustrates a simulation model for the spatial-temporal expansion pattern of the Asian subterranean termite (Coptotermes gestroi) by natural means… Show more

Termites are destructive insect pests that cause billions of dollars in property damage every year. Exotic invasive species have become established pests in parts of the United States. Spread models predicting the areas of infestation following initial introduction of an invasive species are important for an authoritative response. This presentation illustrates a simulation model for the spatial-temporal expansion pattern of the Asian subterranean termite (Coptotermes gestroi) by natural means in South Florida. The model applies statistical distributions over the dispersal distance of termite flights and flight directions, including nearest neighbors spatial models to incorporate the maximal attraction distance between heterosexual pairs to establish a new colony. ArcGIS is used to visualize the change of spatial phenomena (i.e. spread over time), and its compatibility with R, an open source programming language for statistical computing, is demonstrated.
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