Andrew Paul Gutierrez

Potential of remote sensing to predict species invasions A modelling perspective

Understanding the causes and effects of species invasions is a priority in ecology and conservation biology. One of the crucial steps in evaluating the impact of invasive species is to map changes in their actual and potential distribution and relative abundance across a wide region over an appropriate time span. While direct and indirect remote sensing approaches have long been used to assess the invasion of plant species, the distribution of invasive animals is mainly based on indirect methods that rely on environmental proxies of conditions suitable for colonization by a particular species. The aim of this article is to review recent efforts in the predictive modelling of the spread of both plant and animal invasive species using remote sensing, and to stimulate debate on the potential use of remote sensing in biological invasion monitoring and forecasting. Specifically, the challenges and drawbacks of remote sensing techniques are discussed in relation to: i) developing species distribution models, and ii) studying life cycle changes and phenological variations. Finally, the paper addresses the open challenges and pitfalls of remote sensing for biological invasion studies including sensor characteristics, upscaling and downscaling in species distribution models, and uncertainty of results.

Deconstructing the control of the spotted alfalfa aphid Therioaphis maculata

The control of insect pests and other taxa may be a result of many factors that are difficult to separate and quantify. Introduced parasitoids, host plant resistance, pathogens and native predators led to the successful control of the spotted alfalfa aphid (SAA; Therioaphis maculata Monell) in California and elsewhere, although the relative contribution of each factor remained largely unknown. The relative contribution of each control factor was estimated using a weather‐driven physiologically‐based demographic system model consisting of alfalfa, SAA, its three exotic parasitoids [Aphelinus semiflavus Howard, Praon palitans Muesebeck and Trioxys complanatus (Quilis)], a native coccinellid beetle [Hippodamia convergens (Guérin‐Menéville)], a fungal pathogen [Erynia neoaphidis Remaudière & Hennebert (Zygomycetes: Entomophthorales)] and host plant resistance (HPR). Daily weather data for the period 1995–2006 from 142 locations in Arizona and California were used to drive the model. The factors were introduced to the model singly or in combination to assess their effects in suppressing simulated SAA populations using SAA‐days m−2 year−1 (i.e. density) as the metric of control. Data from selected runs were mapped using the geographic information system grass (http://grass.osgeo.org). The simulation data across all factor combinations, years and locations were summarized using linear multiple regression, with the dependent variable being log10 SAA‐days m−2 year−1 and the independent variables being the presence–absence (0, 1) of the various factors and their interactions. Marginal analysis of the regression model (∂y/∂xi) enabled separation of the average effects of the different factors (xi) given the average effects of the other factors. Alone, each factor failed to control SAA, as did combinations of the parasitoids and coccinellid predation. Control was predicted across all ecological zones only when all mortality factors were included. The marginal analysis suggests that the order of importance of the mortality factors is HPR > coccinellid beetles > T. complanatus > P. palitans > A. semiflavus > the fungal pathogen. The variability of control by coccinellid beetles and the fungal pathogen was high and hence unreliable.