Null model analyses are not adequate to summarize strong associations: Rebuttal to Ulrich et al. (2022) - Kumar P. Mainali; Eric Slud (Journal of Biogeography, 2024)

Climate change and more disturbed land-use types will further the invasion of a non-native annual grass, Ventenata dubia - Arjun Adhikari; Jane Mangold; Kumar P. Mainali; Lisa J. Rew (Biological Invasions, 2023)
Identification of suitable habitat for invasive weeds and their projected infestation extent across different land use cover types under a changing climate is crucial for the broad management goals of prevention, detection, and rapid response. In this study, we adopted an ensemble approach of species distribution models to project potential habitat of the invasive annual grass, Ventenata dubia , within the Gallatin County and along its road corridors, in Montana, USA, under current and future climates. The model prediction of V. dubia habitat was excellent with an AUC value of > 0.90. The climate predictors with most influence on V . dubia occurrence were precipitation, potential evapo-transpiration, relative humidity, vapor pressure deficit, and solar radiation for growing season months. Under current climate, the model projected 243 and 1,371 km 2 coverage of V. dubia along road corridors and the entire County, respectively. The projected coverage of V. dubia was greatest for road corridors (239% under RCP4.5 and 302% under RCP8.5) compared to that of Gallatin County (127% under RCP4.5 and 241% under RCP8.5). Among the land use cover types, the model projected greatest expansion of V. dubia across agriculture land with 425% and 484%, and grasslands with 278% and 442% under RCP4.5 and RCP8.5 respectively. Our modelling approach suggests that the changing climate will facilitate spread and establishment of non-native species in disturbed habitats. We conclude that V. dubia with a short history of invasion is expanding at an alarming rate and requires greater investment in detection and monitoring to prevent further expansion.

Correction to: Future distribution of invasive weed species across the major road network in the state of Montana, USA - Arjun Adhikari; Lisa J. Rew; Kumar P. Mainali; Subodh Adhikari; Bruce D. Maxwell (Regional Environmental Change, 2020)

Future distribution of invasive weed species across the major road network in the state of Montana, USA - Arjun Adhikari; Lisa J. Rew; Kumar P. Mainali; Subodh Adhikari; Bruce D. Maxwell (Regional Environmental Change, 2020)
Invasive plant species are a significant global problem, with the potential to alter structure and function of ecosystems and cause economic damage to managed landscapes. An effective course of action to reduce the spread of invasive plant species is to identify potential habitat incorporating changing climate scenarios. In this study, we used a suite of species distribution models (SDMs) to project habitat suitability of the eleven most abundant invasive weed species across road networks of Montana, USA, under current (2005) conditions and future (2040) projected climates. We found high agreement between different model predictions for most species. Among the environmental predictors, February minimum temperature, monthly precipitation, solar radiation, and December vapor pressure deficit accounted for the most variation in projecting habitat suitability for most of the invasive weed species. The model projected that habitat suitability along roadsides would expand for seven species ranging from + 5 to + 647% and decline for four species ranging from − 11 to − 88% under high representative concentration pathway (RCP 8.5) greenhouse gas (GHG) trajectory. When compared with current distribution, the ensemble model projected the highest expansion habitat suitability with six-fold increase for St. John’s Wort ( Hypericum perforatum ), whereas habitat suitability of leafy spurge ( Euphorbia esula ) was reduced by − 88%. Our study highlights the roadside areas that are currently most invaded by our eleven target species across 55 counties of Montana, and how this will change with climate. We conclude that the projected range shift of invasive weeds challenges the status quo, and requires greater investment in detection and monitoring to prevent expansion. Though our study focuses across road networks of a specific region, we expect our approach will be globally applicable as the predictions reflect fundamental ecological processes.

Projecting future expansion of invasive species: comparing and improving methodologies for species distribution modeling - Kumar P. Mainali; Dan L. Warren; Kunjithapatham Dhileepan; Andrew McConnachie; Lorraine Strathie; Gul Hassan; Debendra Karki; Bharat B. Shrestha; Camille Parmesan (Global Change Biology, 2015)
Modeling the distributions of species, especially of invasive species in non‐native ranges, involves multiple challenges. Here, we developed some novel approaches to species distribution modeling aimed at reducing the influences of such challenges and improving the realism of projections. We estimated species–environment relationships for Parthenium hysterophorus L. (Asteraceae) with four modeling methods run with multiple scenarios of (i) sources of occurrences and geographically isolated background ranges for absences, (ii) approaches to drawing background (absence) points, and (iii) alternate sets of predictor variables. We further tested various quantitative metrics of model evaluation against biological insight. Model projections were very sensitive to the choice of training dataset. Model accuracy was much improved using a global dataset for model training, rather than restricting data input to the species’ native range. AUC score was a poor metric for model evaluation and, if used alone, was not a useful criterion for assessing model performance. Projections away from the sampled space ( i.e., into areas of potential future invasion) were very different depending on the modeling methods used, raising questions about the reliability of ensemble projections. Generalized linear models gave very unrealistic projections far away from the training region. Models that efficiently fit the dominant pattern, but exclude highly local patterns in the dataset and capture interactions as they appear in data (e.g., boosted regression trees), improved generalization of the models. Biological knowledge of the species and its distribution was important in refining choices about the best set of projections. A post hoc test conducted on a new Parthenium dataset from Nepal validated excellent predictive performance of our ‘best’ model. We showed that vast stretches of currently uninvaded geographic areas on multiple continents harbor highly suitable habitats for parthenium. However, discrepancies between model predictions and parthenium invasion in Australia indicate successful management for this globally significant weed.