spatially-explicit test of the refuge strategy for delaying insecticide resistance

The refuge strategy is used worldwide to delay the evolution of pest resistance to insecticides that are either sprayed or produced by transgenic Bacillus thuringiensis (Bt) crops. This strategy is based on the idea that refuges of host plants where pests are not exposed to an insecticide promote survival of susceptible pests. Despite widespread adoption of this approach, large-scale tests of the refuge strategy have been problematic. Here we tested the refuge strategy with 8 y of data on refuges and resistance to the insecticide pyriproxyfen in 84 populations of the sweetpotato whitefly (Bemisia tabaci) from cotton fields in central Arizona. We found that spatial variation in resistance to pyriproxyfen within each year was not affected by refuges of melons or alfalfa near cotton fields. However, resistance was negatively associated with the area of cotton refuges and positively associated with the area of cotton treated with pyriproxyfen. A statistical model based on the first 4 y of data, incorporating the spatial distribution of cotton treated and not treated with pyriproxyfen, adequately predicted the spatial variation in resistance observed in the last 4 y of the study, confirming that cotton refuges delayed resistance and treated cotton fields accelerated resistance. By providing a systematic assessment of the effectiveness of refuges and the scale of their effects, the spatially explicit approach applied here could be useful for testing and improving the refuge strategy in other crop-pest systems. pesticide resistance | predictive evolutionary models | pest management | resistance management P opulation growth will continue to favor agricultural intensification for decades. Because agricultural intensification is associated with increased pest pressure, pesticides generally help to increase yield (1-3). Although significant progress has been made to reduce reliance on pesticides (4, 5), an increasing number of insects and mites exhibit field-evolved resistance to synthetic pesticides, Bacillus thuringiensis (Bt) sprays, and transgenic Bt crops (6, 7). Negative consequences of resistance include increased pesticide use, disruption of food webs and ecosystem services, increased risk to human health, and loss of profits for farmers and industry (1, 3). One of the main strategies for delaying resistance promotes survival of susceptible pests by providing refuges, which are areas of host plants where pests are not exposed to an insecticide. Theory predicts that refuges will slow the evolution of resistance by reducing the fitness advantage of resistant individuals (7-9). Refuges can also reduce the heritability of resistance when susceptible individuals mate with resistant individuals surviving exposure to an insecticide (7). Empirical support for the refuge strategy was provided by short-term laboratory and greenhouse experiments (10, 11). Although these experiments test the hypothesis that mating between susceptible and resistant individuals delays the evolution of resistance, they do not consider several factors that affect resistance in the field (7-9), and thus only provide partial support for effectiveness of the refuge strategy in the field. Retrospective analyses of variation in resistance evolution in the field also suggest that refuges have been effective, but these previous tests have been based primarily on comparisons among species, or qualitative comparisons within species based on a limited number of widely separated geographic areas (12, 13). In such tests, factors that vary among species or geographic areas can confound the effects of refuges. Accordingly, large-scale field tests of the refuge strategy for a single species within a geographic area where factors affecting resistance are similar are needed to test the refuge strategy more rigorously. Moreover, tests of predictive refuge strategy models are required to determine if the refuge strategy can delay resistance (14). Furthermore, to improve our ability to develop efficient refuge strategies, empirical approaches are necessary to characterize effects of refuges on resistance evolution (7, 15). Here we tested the refuge strategy using 8 y of data on refuges and resistance to the insecticide pyriproxyfen in 84 populations of the sweetpotato whitefly (Bemisia tabaci) sampled in cotton fields of central Arizona. We studied the B biotype of B. tabaci, also known as the Asia Minor-Middle East 1 species, which is a key pest of cotton and other crops in Arizona and worldwide (16). The insect growth regulators pyriproxyfen (a juvenile hormone analog) and buprofezin (a chitin synthesis inhibitor) are selective insecticides that have been used for whitefly control in Arizona cotton (Gossypium spp.) since 1996 (17, 18). A single application of either insecticide on cotton when B. tabaci populations start to increase has substantially reduced sprays of broad-spectrum insecticides, helped to conserve natural enemies, and restored farmers ' profits (18, 19). To deter rapid evolution of resistance, farmers in Arizona generally have not used pyriproxyfen to control B. tabaci on crops other than cotton Although B. tabaci is polyphagous, few whitefly crops other than cotton are available in central Arizona from June to September, when pyriproxyfen is sprayed on cotton. In principle, crops that could act as refuges include spring melons (Citrullus lanatus and Cucumis melo), alfalfa (Medicago sativa) and cotton not treated with pyriproxyfen (referred to hereafter as untreated cotton). B. tabac

Woody Cover Estimates in Oklahoma and Texas Using a Multi-Sensor Calibration and Validation Approach

Woody cover encroachment/expansion/conversion is a complex phenomenon that has environmental and economic impacts around the world. This research demonstrates the development of highly accurate models for estimating percent woody cover using high spatial resolution image data in combination with multi-seasonal Landsat reflectance products. We use a classification and regression tree (CART) approach to classify woody cover using fine resolution multispectral National Agricultural Imaging Program (NAIP) data. A continuous classification and regression tree (Cubist) ingests the aggregated woody cover classification along with the seasonal Landsat data to create a continuous woody cover model. We applied the models, derived by Cubist, across several Landsat scenes to estimate the percentage of woody plant cover, within each Landsat pixel, over a larger regional extent. We measured an average absolute error of 12.1 percent and a correlation coefficient of 0.78 for the models performed. The method of modelling percent woody cover established in this manuscript outperforms currently available woody cover estimates including Landsat Vegetation Continuous Fields (VCF), on average by 26 percent, and Web-Enabled Landsat Data (WELD) products, on average by 16 percent, for the region of interest. Current woody cover products are also limited to certain years and not available pre-2000. This manuscript describes a novel Cubist-based technique to model woody cover for any area of the world, as long as fine (similar to 1-2 m) spatial resolution and Landsat data are available.NSF's Division of Environmental Biology [1413900]This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Remotely Sensed Changes in Vegetation Cover Distribution and Groundwater along the Lower Gila River

Introduced as a soil erosion deterrent, salt cedar has become a menace along riverbeds in the desert southwest. Salt cedar replaces native species, permanently altering the structure, composition, function, and natural processes of the landscape. Remote sensing technologies have the potential to monitor the level of invasion and its impacts on ecosystem services. In this research, we developed a species map by segmenting and classifying various species along a stretch of the Lower Gila River. We calculated metrics from high-resolution multispectral imagery and light detection and ranging (LiDAR) data to identify salt cedar, mesquite, and creosote. Analysts derived training and validation information from drone-acquired orthophotos to achieve an overall accuracy of 94%. It is clear from the results that salt cedar completely dominates the study area with small numbers of mesquite and creosote present. We also show that vegetation has declined in the study area over the last 25 years. We discuss how water usage may be influencing the plant health and biodiversity in the region. An examination of ground well, stream gauge, and Gravity Recovery and Climate Experiment (GRACE) groundwater storage data indicates a decline in water levels near the study area over the last 25 years.U.S. Bureau of Land ManagementOpen access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Fusion of High Resolution Aerial Multispectral and LiDAR Data: Land Cover in the Context of Urban Mosquito Habitat

Remotely sensed multi-spectral and -spatial data facilitates the study of mosquito-borne disease vectors and their response to land use and cover composition in the urban environment. In this study we assess the feasibility of integrating remotely sensed multispectral reflectance data and LiDAR (Light Detection and Ranging)-derived height information to improve land use and land cover classification. Classification and Regression Tree (CART) analyses were used to compare and contrast the enhancements and accuracy of the multi-sensor urban land cover classifications. Eight urban land-cover classes were developed for the city of Tucson, Arizona, USA. These land cover classes focus on pervious and impervious surfaces and microclimate landscape attributes that impact mosquito habitat such as water ponds, residential structures, irrigated lawns, shrubs and trees, shade, and humidity. Results show that synergistic use of LiDAR, multispectral and the Normalized Difference Vegetation Index data produced the most accurate urban land cover classification with a Kappa value of 0.88. Fusion of multi-sensor data leads to a better land cover product that is suitable for a variety of urban applications such as exploring the relationship between neighborhood composition and adult mosquito abundance data to inform public health issues

Topoedaphic constraints on woody plant cover in a semi-arid grassland

The proliferation of unpalatable woody plants at the expense of perennial grasses in recent decades has challenged our ability to manage rangelands. While there is substantial research documenting shrub proliferation, we know little about the maximum potential shrub cover for a given topoedaphic setting. To better understand the environmental controls over and constraints on shrub cover, we used high spatial resolution imagery to classify cover of a shrub (Prosopis velutina, velvet mesquite) proliferating in a Sonoran Desert grassland in southern Arizona, USA and explored how maximum shrub cover varies across ecological sites and topoedpahic settings. While the upper limit of shrub cover at the continental-scale is constrained by mean annual precipitation (MAP), our results show that this maxima has a wide range variously dictated by elevation, slope inclination/aspect, soil texture, and rainfall re-distribution. Within the watershed, maximum potential shrub cover ranged from < 3% to 45% with the magnitude and direction of topoedaphic influences varying significantly between landscape components. For example, topoedaphic properties enhanced precipitation (PPT) effectiveness and elevated maximum shrub cover above what might be predicted based on MAP alone on some ecological sites, but reduced PPT effectiveness and constrained shrub cover to levels below what would be predicted from MAP on other sites. Knowledge of upper limits of shrub cover at the within-watershed scale will strengthen dynamic vegetation models, serve as a basis to better design field and modeling experiments and decision support tools, and provide a spatial context indicators for prioritizing conservation/land management goals and objectives

Trends and ENSO/AAO Driven Variability in NDVI Derived Productivity and Phenology alongside the Andes Mountains

Abstract: Increasing water use and droughts, along with climate variability and land use change, have seriously altered vegetation growth patterns and ecosystem response in several regions alongside the Andes Mountains. Thirty years of the new generation biweekly normalized difference vegetation index (NDVI3g) time series data show significant land cover specific trends and variability in annual productivity and land surface phenological response. Productivity is represented by the growing season mean NDVI values (July to June). Arid and semi-arid and sub humid vegetation types (Atacama desert, Chaco and Patagonia) across Argentina, northern Chile, northwest Uruguay and southeast Bolivia show negative trends in productivity, while some temperate forest and agricultural areas in Chile and sub humid and humid areas in Brazil, Bolivia and Peru show positive trends in productivity. The start (SOS) and length (LOS) of the growing season results show large variability and regional hot spots where later SOS often coincides with reduced productivity. A longer growing season is generally found for some locations in the south of Chile (sub-antarctic forest) and Argentina (Patagonia steppe), while central Argentina (Pampa-mixed grasslands and agriculture) has a shorter LOS. Some of the areas hav

Analyzing Landscape Trends on Agriculture, Introduced Exotic Grasslands and Riparian Ecosystems in Arid Regions of Mexico

Riparian Zones are considered biodiversity and ecosystem services hotspots. In arid environments, these ecosystems represent key habitats, since water availability makes them unique in terms of fauna, flora and ecological processes. Simple yet powerful remote sensing techniques were used to assess how spatial and temporal land cover dynamics, and water depth reflect distribution of key land cover types in riparian areas. Our study area includes the San Miguel and Zanjon rivers in Northwest Mexico. We used a supervised classification and regression tree (CART) algorithm to produce thematic classifications (with accuracies higher than 78%) for 1993, 2002 and 2011 using Landsat TM scenes. Our results suggest a decline in agriculture (32.5% area decrease) and cultivated grasslands (21.1% area decrease) from 1993 to 2011 in the study area. We found constant fluctuation between adjacent land cover classes and riparian habitat. We also found that water depth restricts Riparian Vegetation distribution but not agricultural lands or induced grasslands. Using remote sensing combined with spatial analysis, we were able to reach a better understanding of how riparian habitats are being modified in arid environments and how they have changed through time.Project: "Strengthening Resilience of Arid Region Riparian Corridors Ecohydrology and Decision-Making in the Sonora and San Pedro Watersheds"; National Science Foundation's Dynamics of Coupled Natural and Human (CNH) Systems Program; National Council for Science and Technology of Mexico (CONACYT); [CB2013-223525-R]; [INF2012/1-188387]This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

A Novel Spectral Index to Identify Cacti in the Sonoran Desert at Multiple Scales Using Multi-Sensor Hyperspectral Data Acquisitions

Accurate identification of cacti, whether seen as an indicator of ecosystem health or an invasive menace, is important. Technological improvements in hyperspectral remote sensing systems with high spatial resolutions make it possible to now monitor cacti around the world. Cacti produce a unique spectral signature because of their morphological and anatomical characteristics. We demonstrate in this paper that we can leverage a reflectance dip around 972 nm, due to cacti’s morphological structure, to distinguish cacti vegetation from non-cacti vegetation in a desert landscape. We also show the ability to calculate two normalized vegetation indices that highlight cacti. Furthermore, we explore the impacts of spatial resolution by presenting spectral signatures from cacti samples taken with a handheld field spectroradiometer, drone-based hyperspectral sensor, and aerial hyperspectral sensor. These cacti indices will help measure baseline levels of cacti around the world and examine changes due to climate, disturbance, and management influences

A Novel Spectral Index to Identify Cacti in the Sonoran Desert at Multiple Scales Using Multi-Sensor Hyperspectral Data Acquisitions

Accurate identification of cacti, whether seen as an indicator of ecosystem health or an invasive menace, is important. Technological improvements in hyperspectral remote sensing systems with high spatial resolutions make it possible to now monitor cacti around the world. Cacti produce a unique spectral signature because of their morphological and anatomical characteristics. We demonstrate in this paper that we can leverage a reflectance dip around 972 nm, due to cacti&rsquo;s morphological structure, to distinguish cacti vegetation from non-cacti vegetation in a desert landscape. We also show the ability to calculate two normalized vegetation indices that highlight cacti. Furthermore, we explore the impacts of spatial resolution by presenting spectral signatures from cacti samples taken with a handheld field spectroradiometer, drone-based hyperspectral sensor, and aerial hyperspectral sensor. These cacti indices will help measure baseline levels of cacti around the world and examine changes due to climate, disturbance, and management influences