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Dr. Vivek Srivastava
University of British Columbia

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0 Ecology
0 Entomology
0 Forestry
0 Geoinformatics
0 Remote Sensing

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Forestry
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Short Biography

I specialize in ecological modelling with special interest in predicting future risk hotspots, as well as economic and ecological impacts of potential pest invasions. My research aims to provide critical information for biodiversity conservation, management planning of invasive species and for understanding species’ ecology and behavior under changing climatic conditions.

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Communication
Published: 19 January 2021 in Forests
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The introduction of the Asian gypsy moth into novel environments continues with frequent interceptions in North America. There is a concern that these subspecies will pose a greater threat to the forests and urban environments of North America than the established gypsy moths (Lymantria dispardispar L.), due to their greater capacity for female flight. Asian gypsy moth populations vary in many key traits, including female flight capabilities. The potential impacts of female flight, in combination with the other key traits, on the ecology and spread of this insect are first discussed in this communication. This also provides the first review of most of the current literature on the variations in flight capability and flight distance of gypsy moth populations, as well as variation in other traits of concern and the potential methods of identification, with special attention paid to the Asian subspecies Lymantria dispar japonica Motschulsky and Lymantria dispar asiatica Vinkovskij. There are currently good tools for identifying the general origin of introduced gypsy moth populations, but these do not provide enough information to effectively manage introductions. Gypsy moth key traits differ among populations, even within each subspecies of the gypsy moth, so introduction of gypsy moths from other world areas into locations where the gypsy moth is already present could result in unwanted changes in gypsy moth biology. It also appears that the introduction of flight-capable females could enhance a population’s dispersal capability and require modifications to management protocols used for flightless females. Therefore, rapid tools to assess key traits in introduced populations are needed to adequately plan for, or deal with, new introductions into novel habitats.

ACS Style

Vivek Srivastava; Melody A. Keena; Galen E. Maennicke; Richard. C. Hamelin; Verena C. Griess. Potential Differences and Methods of Determining Gypsy Moth Female Flight Capabilities: Implications for the Establishment and Spread in Novel Habitats. Forests 2021, 12, 103 .

AMA Style

Vivek Srivastava, Melody A. Keena, Galen E. Maennicke, Richard. C. Hamelin, Verena C. Griess. Potential Differences and Methods of Determining Gypsy Moth Female Flight Capabilities: Implications for the Establishment and Spread in Novel Habitats. Forests. 2021; 12 (1):103.

Chicago/Turabian Style

Vivek Srivastava; Melody A. Keena; Galen E. Maennicke; Richard. C. Hamelin; Verena C. Griess. 2021. "Potential Differences and Methods of Determining Gypsy Moth Female Flight Capabilities: Implications for the Establishment and Spread in Novel Habitats." Forests 12, no. 1: 103.

Journal article
Published: 06 December 2020 in Ecological Modelling
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Understanding biodiversity pressures associated with recreation and tourism is a major challenge for conservation planning and landscape management. While estimates of landscape use are often collected using mechanisms such as park entry fees and traffic density estimates, these data do not provide substantial detail about the spatial location or intensity of recreation and tourism across biodiversity management areas. To better predict patterns of recreation and tourism likelihood to support conservation planning, we used social network data from Facebook(™), Flickr(™), Google(™), Strava(™), and Wikilocs(™) along with a suite of remote-sensing-derived environmental covariates in a maximum entropy (MaxEnt) presence-only modelling framework. Social network samples were compiled and processed to reduce sampling bias and spatial autocorrelation. Road access, climate data, and remote sensing covariates describing vegetation greenness, disturbance, topography, and moisture were used as predictor variables in the MaxEnt modelling framework. Our focus site was a grizzly bear (Ursus arctos) management area in west-central Alberta, Canada. Individual models were developed for each social network dataset, as well as a combined model including all the samples . Mean cross-validated AUC, partial ROC, and true skill statistics (TSS) were used to evaluate model accuracy. Results indicated that the covariates proposed were able to best model Strava and Wikilocs activity (TSS = 0.69 and 0.50, respectively), while samples from Flickr or the combination of all social networks were least accurate (TSS = 0.32). The “access” covariate was most important for MaxEnt training gain across a number of social network models, highlighting the importance of access for recreation and tourism likelihood. The summer heat moisture index and normalized burn ratio were also useful spatial covariates in many predictions. Recreation and tourism likelihood maps were combined with grizzly bear telemetry data to examine how recreation and tourism may affect grizzly bear behaviour. All social network models found a similar influence on grizzly bear behaviour, with increasing recreation and tourism use resulting in decreased foraging behaviour and increased rapid movement, suggesting that the models developed here are useful tools for predicting grizzly bear behaviour and planning conservation strategies for the species.

ACS Style

Tristan R.H. Goodbody; Nicholas C. Coops; Vivek Srivastava; Bethany Parsons; Sean P. Kearney; Gregory J.M. Rickbeil; Gordon B. Stenhouse. Mapping recreation and tourism use across grizzly bear recovery areas using social network data and maximum entropy modelling. Ecological Modelling 2020, 440, 109377 .

AMA Style

Tristan R.H. Goodbody, Nicholas C. Coops, Vivek Srivastava, Bethany Parsons, Sean P. Kearney, Gregory J.M. Rickbeil, Gordon B. Stenhouse. Mapping recreation and tourism use across grizzly bear recovery areas using social network data and maximum entropy modelling. Ecological Modelling. 2020; 440 ():109377.

Chicago/Turabian Style

Tristan R.H. Goodbody; Nicholas C. Coops; Vivek Srivastava; Bethany Parsons; Sean P. Kearney; Gregory J.M. Rickbeil; Gordon B. Stenhouse. 2020. "Mapping recreation and tourism use across grizzly bear recovery areas using social network data and maximum entropy modelling." Ecological Modelling 440, no. : 109377.

Original paper
Published: 02 November 2020 in Biological Invasions
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Species distribution modelling (SDM) is a valuable tool for predicting the potential distribution of invasive species across space and time. Maximum entropy modelling (MaxEnt) is a popular choice for SDM, but questions have been raised about how these models are developed. Without biologically informed baseline assumptions, complex default SDM models could be selected, even though alternative settings may be more appropriate. Here we explored the effects of various SDM design strategies on distribution mapping of four forest invasive species (FIS) in Canada. We found that if we ignored the underlying FIS biology such as use of biologically relevant predictors, appropriate feature selection and inclusion of dispersal and biotic interactions when we developed our SDMs, we obtained complex SDMs (default) that provided an incomplete picture of the potential FIS invasion. We recommend simplifying SDM complexity and including biologically informed assumptions to achieve more accurate dispersal predictions, particularly when projecting FIS spread across time. We strongly encourage SDM users to perform species-specific tuning when modeling FIS distributions with MaxEnt to determine the best SDM design.

ACS Style

Vivek Srivastava; Amanda D. Roe; Melody A. Keena; Richard C. Hamelin; Verena C. Griess. Oh the places they’ll go: improving species distribution modelling for invasive forest pests in an uncertain world. Biological Invasions 2020, 23, 297 -349.

AMA Style

Vivek Srivastava, Amanda D. Roe, Melody A. Keena, Richard C. Hamelin, Verena C. Griess. Oh the places they’ll go: improving species distribution modelling for invasive forest pests in an uncertain world. Biological Invasions. 2020; 23 (1):297-349.

Chicago/Turabian Style

Vivek Srivastava; Amanda D. Roe; Melody A. Keena; Richard C. Hamelin; Verena C. Griess. 2020. "Oh the places they’ll go: improving species distribution modelling for invasive forest pests in an uncertain world." Biological Invasions 23, no. 1: 297-349.

Journal article
Published: 15 October 2020 in Sustainability
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ACS Style

Wanwan Liang; Liem Tran; Jerome Grant; Vivek Srivastava. Estimating Invasion Dynamics with Geopolitical Unit-Level Records: The Optimal Method Depends on Irregularity and Stochasticity of Spread. Sustainability 2020, 1 .

AMA Style

Wanwan Liang, Liem Tran, Jerome Grant, Vivek Srivastava. Estimating Invasion Dynamics with Geopolitical Unit-Level Records: The Optimal Method Depends on Irregularity and Stochasticity of Spread. Sustainability. 2020; ():1.

Chicago/Turabian Style

Wanwan Liang; Liem Tran; Jerome Grant; Vivek Srivastava. 2020. "Estimating Invasion Dynamics with Geopolitical Unit-Level Records: The Optimal Method Depends on Irregularity and Stochasticity of Spread." Sustainability , no. : 1.

Review article
Published: 01 September 2020 in Environmental Reviews
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Biological invasions represent an increasing threat to ecosystems worldwide, with negative ecological and socio-economic impacts, whereas risk assessment and management remain challenging. The development of decision support systems (DSS) has the potential to help decision-makers and managers mitigate invasive species, but few DSS exist for forest invasive alien species (FIAS). The use of DSS in forestry is not new but they represent an asset in decision making in times of increasing complexity of issues foresters face and factors to consider. Yet, few forest DSS address the problem of FIAS. In this review, we identify key elements of the FIAS risk-assessment and management decision-making process, discuss these elements with a model-based DSS development perspective, and summarize outstanding challenges and opportunities for FIAS DSS development. FIAS DSS should not only estimate the probability of FIAS invasion but also consider forest vulnerability and quantify exposure (i.e., value at risk), while allowing different threat scenarios and possible solutions to be compared. Such a complete risk assessment and management calls for integrative modelling approaches that explicitly link different components of FIAS invasion, management, and impact assessment into a DSS. Such integrative modelling is challenging and may require collaboration among experts of different domains. International collaboration is also needed to facilitate data exchange, as the lack of data is one of the main challenges. In many cases, data and ecological knowledge of invasive species are too limited (in quantity or quality) to constitute useful input to DSS or their components (e.g., species distribution model). Another challenge is to better consider the multiple sources of uncertainties inherent to modelling invasions (e.g., host preferences and behavior, forest vulnerability, potential impacts, and cost and benefits of mitigation actions) when assessing FIAS risk and communicating results from risk assessment. Communication with stakeholders and DSS end-users, in fact, appears as one of the keys to successful DSS development and appropriation, not only to ensure that they correspond to end-users’ needs but also to ensure ease of use, functionality, and good visualization of DSS outputs.

ACS Style

Valentine Lafond; Federico Lingua; Stefanie Lumnitz; Gregory Paradis; Vivek Srivastava; Verena C. Griess. Challenges and opportunities in developing decision support systems for risk assessment and management of forest invasive alien species. Environmental Reviews 2020, 28, 218 -245.

AMA Style

Valentine Lafond, Federico Lingua, Stefanie Lumnitz, Gregory Paradis, Vivek Srivastava, Verena C. Griess. Challenges and opportunities in developing decision support systems for risk assessment and management of forest invasive alien species. Environmental Reviews. 2020; 28 (3):218-245.

Chicago/Turabian Style

Valentine Lafond; Federico Lingua; Stefanie Lumnitz; Gregory Paradis; Vivek Srivastava; Verena C. Griess. 2020. "Challenges and opportunities in developing decision support systems for risk assessment and management of forest invasive alien species." Environmental Reviews 28, no. 3: 218-245.

Journal article
Published: 29 July 2020 in Insects
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Invasive species experience biotic and abiotic conditions that may (or may not) resemble their native environment. We explored the methodology of determining climatic niches and compared the native and post-invasion niches of four invasive forest pests to determine if these species experienced shifts or changes in their new climatic niches. We used environmental principle components analysis (PCA-env) method to quantify climatic niche shifts, expansions, and temporal changes. Furthermore, we assessed the effect of variable selection in the delineation and comparison of niche space. We found that variable selection influenced the delineation and overlap of each niche, whereas the subset of climatic variables selected from the first two PCA-env axes explained more variance in environmental conditions than the complete set of climatic variables for all four species. Most focal species showed climatic niche shifts in their invasive range and had not yet fully occupied the available niche within the invaded range. Our species varied the proportion of niche overlap between the native and invasive ranges. By comparing native and invasive niches, we can help predict a species’ potential range expansion and invasion potential. Our results can guide monitoring and help inform management of these and other invasive species.

ACS Style

Vivek Srivastava; Wanwan Liang; Melody A. Keena; Amanda D. Roe; Richard C. Hamelin; Verena C. Griess. Assessing Niche Shifts and Conservatism by Comparing the Native and Post-Invasion Niches of Major Forest Invasive Species. Insects 2020, 11, 479 .

AMA Style

Vivek Srivastava, Wanwan Liang, Melody A. Keena, Amanda D. Roe, Richard C. Hamelin, Verena C. Griess. Assessing Niche Shifts and Conservatism by Comparing the Native and Post-Invasion Niches of Major Forest Invasive Species. Insects. 2020; 11 (8):479.

Chicago/Turabian Style

Vivek Srivastava; Wanwan Liang; Melody A. Keena; Amanda D. Roe; Richard C. Hamelin; Verena C. Griess. 2020. "Assessing Niche Shifts and Conservatism by Comparing the Native and Post-Invasion Niches of Major Forest Invasive Species." Insects 11, no. 8: 479.

Journal article
Published: 08 January 2020 in Scientific Reports
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Gypsy moth (Lymantria dispar L.) is one of the world’s worst hardwood defoliating invasive alien species. It is currently spreading across North America, damaging forest ecosystems and posing a significant economic threat. Two subspecies L. d. asiatica and L. d. japonica, collectively referred to as Asian gypsy moth (AGM) are of special concern as they have traits that make them better invaders than their European counterpart (e.g. flight capability of females). We assessed the potential distribution of AGM in Canada using two presence-only species distribution models, Maximum Entropy (MaxEnt) and Genetic Algorithm for Rule-set Prediction (GARP). In addition, we mapped AGM potential future distribution under two climate change scenarios (A1B and A2) while implementing dispersal constraints using the cellular automation model MigClim. MaxEnt had higher AUC, pAUC and sensitivity scores (0.82/1.40/1.00) when compared to GARP (0.70/1.26/0.9), indicating better discrimination of suitable versus unsuitable areas for AGM. The models indicated that suitable conditions for AGM were present in the provinces of British Columbia, Ontario, Quebec, Nova Scotia and New Brunswick. The human influence index was the variable found to contribute the most in predicting the distribution of AGM. These model results can be used to identify areas at risk for this pest, to inform strategic and tactical pest management decisions.

ACS Style

Vivek Srivastava; Verena C. Griess; Melody A. Keena. Assessing the Potential Distribution of Asian Gypsy Moth in Canada: A Comparison of Two Methodological Approaches. Scientific Reports 2020, 10, 1 -10.

AMA Style

Vivek Srivastava, Verena C. Griess, Melody A. Keena. Assessing the Potential Distribution of Asian Gypsy Moth in Canada: A Comparison of Two Methodological Approaches. Scientific Reports. 2020; 10 (1):1-10.

Chicago/Turabian Style

Vivek Srivastava; Verena C. Griess; Melody A. Keena. 2020. "Assessing the Potential Distribution of Asian Gypsy Moth in Canada: A Comparison of Two Methodological Approaches." Scientific Reports 10, no. 1: 1-10.

Journal article
Published: 01 April 2019 in CAB Reviews: Perspectives in Agriculture, Veterinary Science, Nutrition and Natural Resources
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The use of species distribution models (SDM) is of increasing popularity when studying biological invasions, e.g. to assess the impact of climate change on invasive species, to prioritize conservation measures, or to study invasive evolutionary biology. SDM correlate known occurrences of species with environmental variables and predict a species' potential distribution on other geographies over space and time. Today, SDM are widely used to produce invasion risk maps by delineating probable risk areas based on climatic suitability for a species. These maps can guide early detection and rapid response measures. While recent developments in modelling approaches and wider availability of environment datasets have helped to create better and more accurate SDM, in many cases the used models ignore the associated underlying ecological processes for e.g. dispersal and biotic interactions and thus provide only an incomplete picture of invasion risks. In this paper, we present a review of the most common applications of SDM in invasive species management and provide an overview of possible solutions to various challenges like uncertainty and transferability associated with them. Based on our review we conclude that ways towards more accurate model outputs include fitting models with existing ecological knowledge (hybrid models), address uncertainty and biotic interactions and link species dispersal traits with projections of species distributions. In the future, work is required on developing more hybrid approaches and models that addresses both local diffusion and long distance movement of alien species.

ACS Style

V. Srivastava. Species distribution models (SDM): applications, benefits and challenges in invasive species management. CAB Reviews: Perspectives in Agriculture, Veterinary Science, Nutrition and Natural Resources 2019, 14, 1 .

AMA Style

V. Srivastava. Species distribution models (SDM): applications, benefits and challenges in invasive species management. CAB Reviews: Perspectives in Agriculture, Veterinary Science, Nutrition and Natural Resources. 2019; 14 (020):1.

Chicago/Turabian Style

V. Srivastava. 2019. "Species distribution models (SDM): applications, benefits and challenges in invasive species management." CAB Reviews: Perspectives in Agriculture, Veterinary Science, Nutrition and Natural Resources 14, no. 020: 1.

Review
Published: 20 August 2018 in Journal of Pest Science
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Invasive species pose significant threats to forest ecosystems. Early intervention strategies are the most cost-effective means to control biological invasions, but are reliant on robust biosurveillance. State-of-the-art genomic approaches can provide an unprecedented opportunity to access detailed information on the invasion process and adaptive potential of invasive insects that pose an immediate threat to forests environments. Genomics can improve diagnostics of the invader and identify its route of invasion by determining the source population(s), assess its probability of establishment and patterns of spread, as well as provide evidence of adaptation. Applied biosurveillance efforts by plant health regulatory agencies will benefit substantially from the detailed insights that genomic data bring to our understanding of biological invasions.

ACS Style

Amanda Roe; Alex S. Torson; Guillaume Bilodeau; Pierre Bilodeau; Gwylim S. Blackburn; Mingming Cui; Michel Cusson; Daniel Doucet; Verena C. Griess; Valentine Lafond; Gregory Paradis; Ilga Porth; Julien Prunier; Vivek Srivastava; Emilie Tremblay; Adnan Uzunovic; Denys Yemshanov; Richard C. Hamelin. Biosurveillance of forest insects: part I—integration and application of genomic tools to the surveillance of non-native forest insects. Journal of Pest Science 2018, 92, 51 -70.

AMA Style

Amanda Roe, Alex S. Torson, Guillaume Bilodeau, Pierre Bilodeau, Gwylim S. Blackburn, Mingming Cui, Michel Cusson, Daniel Doucet, Verena C. Griess, Valentine Lafond, Gregory Paradis, Ilga Porth, Julien Prunier, Vivek Srivastava, Emilie Tremblay, Adnan Uzunovic, Denys Yemshanov, Richard C. Hamelin. Biosurveillance of forest insects: part I—integration and application of genomic tools to the surveillance of non-native forest insects. Journal of Pest Science. 2018; 92 (1):51-70.

Chicago/Turabian Style

Amanda Roe; Alex S. Torson; Guillaume Bilodeau; Pierre Bilodeau; Gwylim S. Blackburn; Mingming Cui; Michel Cusson; Daniel Doucet; Verena C. Griess; Valentine Lafond; Gregory Paradis; Ilga Porth; Julien Prunier; Vivek Srivastava; Emilie Tremblay; Adnan Uzunovic; Denys Yemshanov; Richard C. Hamelin. 2018. "Biosurveillance of forest insects: part I—integration and application of genomic tools to the surveillance of non-native forest insects." Journal of Pest Science 92, no. 1: 51-70.

Journal article
Published: 20 July 2018 in Ecological Modelling
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Biological invasion is considered the subsequent most important threat to biodiversity after habitat destruction and is documented as a main cause of global biodiversity loss and species extinction. Species distribution models can be used to identify areas that are at risk for invasions by harmful invasive alien species (IAS) if IAS have not yet spread to all suitable habitats. We tested the potential of combining two or more independent but complementary modelling methods to enhance the accuracy of spatial predictions. We used the modelling tools Maxent, GARP and BIOCLIM with presence-only data of the IAS Yushania maling (Maling bamboo), from Darjeeling, Himalaya to develop distribution maps. Modelling tools were chosen based on their performance with presence-only data (Area under curve (AUC) > 0.7) as well as their differences in underlying modelling concept and statistics. The models combine occurrence records with topographic, climatic, and vegetative predictors derived from satellite data. By combining the 3 selected models in an ensemble approach we were able to minimize the spatial uncertainty related to suitable habitat prediction for Yushania maling. While both GARP and BIOCLIM consistently performed at an AUC > 0.7, both our ensemble model and Maxent performed better with an AUC of 0.95 and 0.94 respectively. Moderately and highly suitable habitats for Yushania maling predicted by models correlated well with survey records except for GARP, where we found the model to over-predict suitability outside of the species’ known ecological niche. Our findings identify the best modelling approach enhancing overall explanatory power of habitat suitability models. Our findings show that an ensemble approach should be used to ensure appropriate mitigation measures are applied in the appropriate places, enhancing overall effectiveness both ecologically as well as economically. It was shown that Yushania maling is indeed a threat to the ecosystems in the region, and while the species’ potential habitat may decrease in some areas with climate change, other areas will become more suitable for it.

ACS Style

Vivek Srivastava; Verena C. Griess; Hitendra Padalia. Mapping invasion potential using ensemble modelling. A case study on Yushania maling in the Darjeeling Himalayas. Ecological Modelling 2018, 385, 35 -44.

AMA Style

Vivek Srivastava, Verena C. Griess, Hitendra Padalia. Mapping invasion potential using ensemble modelling. A case study on Yushania maling in the Darjeeling Himalayas. Ecological Modelling. 2018; 385 ():35-44.

Chicago/Turabian Style

Vivek Srivastava; Verena C. Griess; Hitendra Padalia. 2018. "Mapping invasion potential using ensemble modelling. A case study on Yushania maling in the Darjeeling Himalayas." Ecological Modelling 385, no. : 35-44.

Review
Published: 06 July 2018 in Journal of Pest Science
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Early intervention, effective management, and regulations are essential to mitigate the potential negative impacts of invasive forest insects. Biosurveillance provides the necessary knowledge to inform management, and regulatory practices. Genomic approaches can contribute valuable information to this process. Unfortunately, adoption and incorporation of genomic tools into biosurveillance frameworks is not straightforward. To realize the full potential of genomic knowledge, researchers must work together with end users to ensure full adoption, standardization, validation, and interpretation of genomic results.

ACS Style

Pierre Bilodeau; Amanda Roe; Guillaume Bilodeau; Gwylim S. Blackburn; Mingming Cui; Michel Cusson; Daniel Doucet; Verena C. Griess; Valentine Lafond; Chelsea Nilausen; Gregory Paradis; Ilga Porth; Julien Prunier; Vivek Srivastava; Don Stewart; Alex S. Torson; Emilie Tremblay; Adnan Uzunovic; Denys Yemshanov; Richard Hamelin. Biosurveillance of forest insects: part II—adoption of genomic tools by end user communities and barriers to integration. Journal of Pest Science 2018, 92, 71 -82.

AMA Style

Pierre Bilodeau, Amanda Roe, Guillaume Bilodeau, Gwylim S. Blackburn, Mingming Cui, Michel Cusson, Daniel Doucet, Verena C. Griess, Valentine Lafond, Chelsea Nilausen, Gregory Paradis, Ilga Porth, Julien Prunier, Vivek Srivastava, Don Stewart, Alex S. Torson, Emilie Tremblay, Adnan Uzunovic, Denys Yemshanov, Richard Hamelin. Biosurveillance of forest insects: part II—adoption of genomic tools by end user communities and barriers to integration. Journal of Pest Science. 2018; 92 (1):71-82.

Chicago/Turabian Style

Pierre Bilodeau; Amanda Roe; Guillaume Bilodeau; Gwylim S. Blackburn; Mingming Cui; Michel Cusson; Daniel Doucet; Verena C. Griess; Valentine Lafond; Chelsea Nilausen; Gregory Paradis; Ilga Porth; Julien Prunier; Vivek Srivastava; Don Stewart; Alex S. Torson; Emilie Tremblay; Adnan Uzunovic; Denys Yemshanov; Richard Hamelin. 2018. "Biosurveillance of forest insects: part II—adoption of genomic tools by end user communities and barriers to integration." Journal of Pest Science 92, no. 1: 71-82.

Journal article
Published: 26 March 2015 in Environmental Monitoring and Assessment
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Invasive species and climate change are considered as the most serious global environmental threats. In this study, we investigated the influence of projected global climate change on the potential distribution of one of the world's most successful invader weed, bushmint (Hyptis suaveolens (L.) Poit.). We used spatial data on 20 environmental variables at a grid resolution of 5 km, and 564 presence records of bushmint from its native and introduced range. The climatic profiles of the native and invaded sites were analyzed in a multi-variate space in order to examine the differences in the position of climatic niches. Maximum Entropy (MaxEnt) model was used to predict the potential distribution of bushmint using presence records from entire range (invaded and native) along with 14 eco-physiologically relevant predictor variables. Subsequently, the trained MaxEnt model was fed with Hadley Centre Coupled Model (HadCM3) climate projections to predict potential distribution of bushmint by the year 2050 under A2a and B2a emission scenarios. MaxEnt predictions were very accurate with an Area Under Curve (AUC) value of 0.95. The results of Principal Component Analysis (PCA) indicated that climatic niche of bushmint on the invaded sites is not entirely similar to its climatic niche in the native range. A vast area spread between 34 ° 02' north and 28 ° 18' south latitudes in tropics was predicted climatically suitable for bushmint. West and middle Africa, tropical southeast Asia, and northern Australia were predicted at high invasion risk. Study indicates enlargement, retreat, or shift across bushmint's invasion range under the influence of climate change. Globally, bushmint's potential distribution might shrink in future with more shrinkage for A2a scenario than B2a. The study outcome has immense potential for undertaking effective preventive/control measures and long-term management strategies for regions/countries, which are at higher risk of bushmint's invasion.

ACS Style

Hitendra Padalia; Vivek Srivastava; S. P. S. Kushwaha. How climate change might influence the potential distribution of weed, bushmint (Hyptis suaveolens)? Environmental Monitoring and Assessment 2015, 187, 1 -14.

AMA Style

Hitendra Padalia, Vivek Srivastava, S. P. S. Kushwaha. How climate change might influence the potential distribution of weed, bushmint (Hyptis suaveolens)? Environmental Monitoring and Assessment. 2015; 187 (4):1-14.

Chicago/Turabian Style

Hitendra Padalia; Vivek Srivastava; S. P. S. Kushwaha. 2015. "How climate change might influence the potential distribution of weed, bushmint (Hyptis suaveolens)?" Environmental Monitoring and Assessment 187, no. 4: 1-14.

Journal article
Published: 01 July 2014 in Ecological Informatics
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ACS Style

Hitendra Padalia; Vivek Srivastava; S.P.S. Kushwaha. Modeling potential invasion range of alien invasive species, Hyptis suaveolens (L.) Poit. in India: Comparison of MaxEnt and GARP. Ecological Informatics 2014, 22, 36 -43.

AMA Style

Hitendra Padalia, Vivek Srivastava, S.P.S. Kushwaha. Modeling potential invasion range of alien invasive species, Hyptis suaveolens (L.) Poit. in India: Comparison of MaxEnt and GARP. Ecological Informatics. 2014; 22 ():36-43.

Chicago/Turabian Style

Hitendra Padalia; Vivek Srivastava; S.P.S. Kushwaha. 2014. "Modeling potential invasion range of alien invasive species, Hyptis suaveolens (L.) Poit. in India: Comparison of MaxEnt and GARP." Ecological Informatics 22, no. : 36-43.