A class of fast-slow models for adaptive resistance evolution

International audienceResistance to insecticide is considered nowadays one of the major threats to insect control, as its occurrence reduces drastically the efficiency of chemical control campaigns, and may also perturb the application of other control methods, like biological and genetic control. In order to account for the emergence and spread of such phenomenon as an effect of exposition to larvicide and/or adulticide, we develop in this paper a general time-continuous population model with two life phases, subsequently simplified through slow manifold theory. The derived models present density-dependent recruitment and mortality rates in a non-conventional way. We show that in absence of selection, they evolve in compliance with Hardy-Weinberg law; while in presence of selection and in the dominant or codominant cases, convergence to the fittest genotype occurs. The proposed mathematical models should allow for the study of several issues of importance related to the use of insecticides and other adaptive phenomena

A fast-slow model for adaptive resistance evolution

Resistance to insecticide is considered nowadays one of the major threats to insect control, as its occurrence reduces drastically the efficiency of chemical control campaigns, and may also perturb the application of other control methods, like biological and genetic control. In order to account for the emergence and spread of such phenomenon as an effect of exposition to larvicide and/or adulticide, we develop in this paper a general time-continuous population model with two life phases, subsequently simplified through slow manifold theory. The derived models present density-dependent recruitment and mortality rates in a non-conventional way. We show that in absence of selection, they evolve in compliance with Hardy-Weinberg law; while in presence of selection and in the dominant or codominant cases, convergence to the fittest genotype occurs. The proposed mathematical models should allow for the study of several issues of importance related to the use of insecticides and other adaptive phenomena

High-resolution satellite imagery is an important yet underutilized resource in conservation biology

Technological advances and increasing availability of high-resolution satellite imagery offer the potential for more accurate land cover classifications and pattern analyses, which could greatly improve the detection and quantification of land cover change for conservation. Such remotely-sensed products, however, are often expensive and difficult to acquire, which prohibits or reduces their use. We tested whether imagery of high spatial resolution (≤5 m) differs from lower-resolution imagery (≥30 m) in performance and extent of use for conservation applications. To assess performance, we classified land cover in a heterogeneous region of Interior Atlantic Forest in Paraguay, which has undergone recent and dramatic human-induced habitat loss and fragmentation. We used 4 m multispectral IKONOS and 30 m multispectral Landsat imagery and determined the extent to which resolution influenced the delineation of land cover classes and patch-level metrics. Higher-resolution imagery more accurately delineated cover classes, identified smaller patches, retained patch shape, and detected narrower, linear patches. To assess extent of use, we surveyed three conservation journals (Biological Conservation, Biotropica, Conservation Biology) and found limited application of high-resolution imagery in research, with only 26.8% of land cover studies analyzing satellite imagery, and of these studies only 10.4% used imagery ≤5 m resolution. Our results suggest that high-resolution imagery is warranted yet under-utilized in conservation research, but is needed to adequately monitor and evaluate forest loss and conversion, and to delineate potentially important stepping-stone fragments that may serve as corridors in a human-modified landscape. Greater access to low-cost, multiband, high-resolution satellite imagery would therefore greatly facilitate conservation management and decision-making.Fil: Boyle, Sarah A.. Rhodes College; Estados UnidosFil: Kennedy, Christina M.. The Nature Conservancy; Estados UnidosFil: Torres Monges, Julio Cesar. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Nacional Patagónico; ArgentinaFil: Colman, Karen. Secretaría del Ambiente. Dirección de Vida Silvestre; ParaguayFil: Pérez Estigarribia, Pastor E.. Universidad de Concepción; ChileFil: de la Sancha, Noé U.. The Field Museum of Natural History; Estados Unido

Patch metrics varied with imagery type.

<p>IKONOS and Landsat classifications significantly differed in patch metrics for all forest patches and those ≥0.5 ha in (a) patch area; (b) patch edge; (c) shape index; (d) perimeter-area ratio; (e) mean distance from patch centroid to all other patch centroids; and (f) distance from patch centroid to the closest patch’s centroid. Asterisks (*, **, ***) indicate significant differences at <i>p</i>≤0.05, 0.01, 0.001, respectively; with df = 52,168 and df = 464 for all t-tests using data from all patches and from patches ≥0.50 ha, respectively. Error bars represent one standard error.</p

Detection of linear forest features varied between IKONOS (4 m resolution) and Landsat (30 m resolution).

<p>IKONOS correctly identified more narrow forest fragments than Landsat (a) as evident in one example from the study area with (b) IKONOS preserving small forest fragments and forested corridors better than (c) Landsat.</p

Limited use of high-resolution imagery for conservation.

<p>Out of 1064 articles in <i>Conservation Biology</i>, <i>Biological Conservation</i>, and <i>Biotropica</i> (2011–2012), 157 utilized primary satellite imagery and analyzed land cover predominantly based on (a) satellite imagery of 30 m resolution and (b) quantified geographic areas ≤1000 km² (equivalent to ≤100,000 ha).</p

Description of land cover classes delineated in this study.

a<p>Agricultural components (i.e. crop fields, pasture) were combined into one class for general comparisons across the broader land cover classes.</p

Comparison of imagery performance.

<p>IKONOS and Landsat imagery classifications significantly differed in (a) percent land cover and (b) total number of patches for the six land cover classes found in the study area in Paraguay.</p