Consequences of exotic host use: impacts on Lepidoptera and a test of the ecological trap hypothesis

Su’adYoonand Quentin Read, 2015

Supplementary Material: Online Resource 1

Analysis of Covariates

Exotic plant species tend to decrease in abundance and richness with increasing elevation and latitude, which may be primarily due to increased anthropogenic introductions at lower elevations and latitudes (Alexander et al. 2011). Because of this pattern, and because of differences in lepidopteran communities across climate gradients, we decided to test whether site-level climate variables moderate the influence of exotic plants on native lepidopteran communities. For each study measuring lepidopteran abundance or richness, we recorded the study type (experimental or observational) and the size of the study plots where present, and recorded the latitude and longitude of each study site. In addition, we extracted mean annual temperature and mean annual precipitation for each study site from the WorldClim database (Hijmans et al., 2005). We also calculated an aggregated land-use intensity score for each study as follows: first, we used publicly available data to find the most recent information for each study site on distance to the nearest road (CIESIN and ITOS, 2013), travel time to a city of >50,000 inhabitants, and human population density (CIESIN et al., 2011; Nelson, 2008). We hypothesized that human land use intensity might confound the relationship between lepidopteran community structure and invasive plants. This is because increasing intensification of human land use may cause both increased invasive plant abundance and other negative effects on lepidopterans unrelated to invasive plants (Vitousek et al., 1997). We calculated a score for land use intensity at each study site by taking the first principal components axis of three metrics: distance to the nearest road, human population density, and travel time to the nearest city. We used the same datasets as recent work by Newbold et al. (Newbold et al., 2015), which demonstrated that land use intensity as represented by this aggregate metric was significantly correlated with global-scale decreases in biodiversity.We fitted multiple regression models with study type as a categorical predictor, plot size, temperature, precipitation, and land-use intensity as continuous predictors, and Hedges’ g from each study as the response variable. For each study on performance, survival, or oviposition preference we recorded the diet breadth of the lepidopteran species in question and whether the native and exotic plant hosts were congeneric (yes or no). We tested whether diet breadth (generalist or specialist) had an effect on the performance, survival, or oviposition preference of native insects. In order to classify diet breadth, we referred to the paper itself and whether the authors referred to the study organism as a specialist or a generalist. Similarly, we tested whether congeneric status of the plant hosts had any effect on performance, survival, or oviposition preference. We fitted multiple regression models with the diet breadth and congeneric status as categorical predictors and the Hedges’ g from each pooled comparison as a response variable. As before, we performed all the multiple regression analyses in R (R Development Core Team, 2008), fitting the regressions in a Bayesian framework with uninformative priors using the rjagspackage (Plummer, 2014).

References

1.Alexander JM, Kueffer C, Daehler CC, Edwards PJ, Pauchard A, Seipel T, Arevalo J, Cavieres L, Hansjoerg D, Jakobs G, McDougall K, Naylor B, Otto R, Parks CG, Rew L, Walsh N (2011) Assembly of nonnative floras along elevational gradients explained by directional ecological filtering. ProcNatlAcadSci USA 108: 656-661

2.CIESIN (Center for International Earth Science Information Network, Columbia University), and ITOS (Information Technology Outreach Services, University of Georgia). 2013. Global Roads Open Access Data Set, Version 1 (gROADSv1). Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). Accessed 5 May 2015.

3.CIESIN (Center for International Earth Science Information Network, Columbia University), IFPRI (International Food Policy Research Institute), The World Bank, and CIAT (Centro Internacional de Agricultura Tropical). 2011. Global Rural-Urban Mapping Project, Version 1 4.(GRUMPv1): Population Density Grid. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). Accessed 5 May 2015.

5.Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A (2005) Very high resolution interpolated climate surfaces for global land areas. Int J Climatology 25:1965-1978

6.Nelson A (2008) Estimated travel time to the nearest city of 50,000 or more people in year 2000. Global Environment Monitoring Unit - Joint Research Centre of the European Commission, Ispra Italy. Accessed 5 May 2015.

7.Newbold T, Hudson LN, Hill SLL, Contu S, Lysenko I, Senior RA, Borger L, Bennett DJ, Choimes A, Collen B, Day J, De Palma A, Diaz S, Echeverria-Londono S, Edgar MJ, Feldman A, Garon M, Harrison MLK, Alhusseini T, Ingram DJ, Itescu Y, Kattge J, Kemp V, Kirkpatrick L, Kleyer M, Correia DLP, Martin CD, Meiri S, Novosolov M, Pan Y, Phillips HRP, Purves DW, Robinson A, Simpson J, Tuck SL, Weiher E, White HJ, Ewers RM, Mace GM, Scharlemann JPW, Purvis A (2015) Global effects of land use on local terrestrial biodiversity. Nature520:45-50

8.Vitousek PM, D'Antonio CM, Loope LL, Rejmanek M, Westbrooks R (1997) Introduced species: a significant component of human-caused global change. N Z J Ecol 21:1-16