Coupling historical prospection data and a remotely-sensed vegetation index for the preventative control of Desert locusts

Abstract Locusts are grasshopper species that exhibit phase polyphenism resulting in the expression of gregarious behaviors that favor the development of large devastating bands and swarms. Desert locust preventative management aims to prevent crop damage by controlling populations before they can reach high densities and form mass migrating swarms. The areas of potential gregarization for Desert locust are large and need to be physically assessed by survey teams for efficient preventative management. An ongoing challenge is to be able to guide where prospection surveys should occur depending on local meteorological and vegetation conditions. In this study, we analyzed the relationship between historical prospection data of Desert locust observations from 2005 to 2009 and spatio-temporal statistics of a vegetation index gathered by remote-sensing with the help of multiple models of logistic regression. The vegetation index was a composite Normalized Difference Vegetation Index (NDVI) given every 16 days and at 250 m spatial resolution (MOD13Q1 from MODIS satellite). The statistics extracted from this index were: (1) spatial means at different scales around the prospection point, (2) relative differences of NDVI variation through time before the prospection, and (3) large-scale summary of vegetation quantity. The multi-model framework showed that vegetation development a month and a half before the survey was amongst the best predictors of locust presence. Also, the local vegetation quantity was not enough to predict locust presence. Vegetation quantity on a scale of a few kilometers was a better predictor but varied non-linearly, reflecting specific biotope types that support Desert locust development. Using one of the best logistic regression models and NDVI data, we were able to derive a predictive model of probability of finding locusts in specific areas. This methodology should help in more efficiently focusing survey efforts on specific parts of the gregarization areas based on the predicted probability of locusts being present.

[1]  Pietro Ceccato,et al.  The desert locust upsurge in West Africa (2003 – 2005): Information on the desert locust early warning system and the prospects for seasonal climate forecasting , 2007 .

[2]  P. Boylan,et al.  Using redd counts to estimate salmonids spawner abundances: A Bayesian modelling approach , 2010 .

[3]  Compton J. Tucker,et al.  Assessment of ecological conditions associated with the 1980/81 desert locust plague upsurge in West Africa using environmental satellite data , 1986 .

[4]  A. Showler.  A summary of control strategies for the desert locust, Schistocerca gregaria (Forskål) , 2002 .

[5]  H. Bozdogan.  Model selection and Akaike's Information Criterion (AIC): The general theory and its analytical extensions , 1987 .

[6]  Stephen J. Simpson,et al.  Phase polyphenism in locusts: mechanisms, population consequences, adaptive significance and evolution. , 2009 .

[7]  K. Cressman,et al.  Surveillance, information sharing and early warning systems for transboundary plant pests diseases: the FAO experience. , 2009 .

[8]  Stephen J. Simpson,et al.  Locust Phase Polyphenism: An Update , 2009 .

[9]  S. Simpson,et al.  Advances, controversies and consensus in locust phase polyphenism research , 2005 .

[10]  Christelle Vancutsem,et al.  Development and Application of Multi-Temporal Colorimetric Transformation to Monitor Vegetation in the Desert Locust Habitat , 2011, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[11]  Michel Lecoq,et al.  Desert locust threat to agricultural development and food security and FAO / International role in its control , 2003 .

[12]  D. Hunter.  Advances in the control of locusts (Orthoptera: Acrididae) in eastern Australia: from crop protection to preventive control , 2004 .

[13]  Éric Parent,et al.  A Bayesian state-space modelling framework for fitting a salmon stage-structured population dynamic model to multiple time series of field data , 2004 .

[14]  J. B. Williams,et al.  Satellite Environmental Monitoring for Migrant Pest Forecasting by FAO: The ARTEMIS System [and Discussion] , 1990 .

[15]  A. G. Hamilton.  THE RELATION OF HUMIDITY AND TEMPERATURE TO THE DEVELOPMENT OF THREE SPECIES OF AFRICAN LOCUSTS—LOCUSTA MIGRATORIA MIGRATORIOIDES (R. & F.), SCHISTOCERCA GREGARIA (FORSK.), NOMADACRIS SEPTEMFASCIATA (SERV.) , 2009 .

[16]  JEREMY ROFFEY,et al.  Environmental and Behavioural Processes in a Desert Locust Outbreak , 1968, Nature.

[17]  M. Plantegenest,et al.  Hierarchical Bayesian Modelling of plant colonisation by winged aphids: Inferring dispersal processes by linking aerial and field count data , 2010 .

[18]  E. Despland.  Fractal index captures the role of vegetation clumping in locust swarming , 2003 .

[19]  S. O. Mohamed,et al.  Field Observations of the Sheltering Behavior of the Solitarious Phase of the Desert Locust, Schistocerca gregaria, with Particular Reference to Antipredator Strategies , 2012 .

[20]  Ramesh Sivanpillai,et al.  Locust Habitat Monitoring and Risk Assessment Using Remote Sensing and GIS Technologies , 2010 .

[21]  David R. Anderson,et al.  Model selection and multimodel inference : a practical information-theoretic approach , 2003 .

[22]  Robert A. Cheke,et al.  Can NDVI GAC imagery be used to monitor desert locust breeding areas , 2006 .

[23]  B. P. Uvarov,et al.  Phases of Locusts and their Interrelations , 1929 .

[24]  B. P. Uvarov,et al.  A revision of the genus Locusta, L. (= Pachytylus, Fieb.), with a new theory as to the periodicity and migrations of locusts , 1921 .

[25]  A. Huete,et al.  MODIS Vegetation Indices , 2010 .

[26]  N. Stenseth,et al.  Desert locust populations, rainfall and climate change: insights from phenomenological models using gridded monthly data , 2010 .

[27]  David W. Hosmer,et al.  Applied Logistic Regression , 1991 .

[28]  Matthew Collett,et al.  Small‐scale processes in desert locust swarm formation: how vegetation patterns influence gregarization , 2000 .

[29]  V. Grimm,et al.  Proposing an information criterion for individual-based models developed in a pattern-oriented modelling framework , 2009 .

[30]  P. Ceccato,et al.  Operational Early Warning System Using SPOT-VEGETATION and TERRA-MODIS to Predict Desert Locust Outbreaks , 2005 .

[31]  D C Krakauer,et al.  Spatial scales of desert locust gregarization. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[32]  Mohamed Abdallahi Ould Babah,et al.  Linking Locust Gregarization to Local Resource Distribution Patterns Across a Large Spatial Scale , 2004 .

[33]  C. Abraham,et al.  Estimating an homogeneous series of a population abundance indicator despite changes in data collection procedure: A hierarchical Bayesian modelling approach , 2011 .

[34]  M. Lecoq.  Recent progress in Desert and Migratory Locust management in Africa. Are preventative actions possible ? , 2001 .

[35]  M. Lecoq.  Desert locust management: from ecology to anthropology , 2005 .

[36]  E. Despland,et al.  Landscape structure and locust swarming: a satellite's eye view , 2004 .

[37]  Cyril Piou,et al.  Plant Size-dependent Escaping Behavior of Gregarious Nymphs of the Desert Locust, Schistocerca gregaria , 2013, Journal of Insect Behavior.

[38]  Michel Lecoq,et al.  Phase polyphenism and preventative locust management. , 2010, Journal of insect physiology.

[39]  Michel Lecoq,et al.  Vers une solution durable au problème du criquet pèlerin , 2004 .

[40]  M. Lecoq,et al.  Preventive control and desert locust plagues , 2008 .

[41]  Darryl I MacKenzie,et al.  Modeling species occurrence dynamics with multiple states and imperfect detection. , 2009, Ecology.

[42]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .