Modelling presence versus abundance for invasive species risk assessment

Aim Improving species distribution models (SDMs) and species abundance models (SAMs) of woody shrubs is critical for predicting biodiversity changes in the Arctic, which is experiencing especially high warming rates. Yet, it remains relatively unexplored if SDMs and SAMs can explain local scale patterns. We aim to identify predictor differences for the distribution versus abundance of two widespread Arctic shrub species with high resolution models and to compare validation approaches to assess the models’ predictive abilities. Location Nuup Kangerlua (NK) and Kangerluarsunnguaq (K), two adjacent fjords in Southwest Greenland. Methods We conducted two separate field surveys in either fjord to construct high resolution (~90 m) SDMs and SAMs for Betula nana and Salix glauca, analysing the predictive influences of local scale climate, topography and soil moisture indicators. We then alternatively trained and validated models in either NK or K fjord and compared these results with the common split‐sample validation approach. Finally, we assessed if including local scale biotic predictors improves SAM performance. Results Temperature extremes and precipitation best predicted the distributions of both species, whereas insolation and soil moisture indicators best predicted abundances. Compared to split‐sample validation, both SDM and SAM performance was substantially reduced with separate survey validation. Regardless of validation approach, models performed poor to moderately well, and including local scale biotic parameters improved SAM performance. Main conclusions Substantial differences in model performance between validation approaches highlight the usefulness of using a separate survey for validating model predictive performance. We discuss various factors that might have caused poor model performance, such as not capturing all relevant predictors or enough local scale heterogeneity in predictor or response variables. We emphasise the need to include predictors relevant at the spatial scale of study, such as local scale biotic interactions, for improved predictions at high spatial resolution.

Section: Does Including Biotic Predictors Improve Sam Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

High resolution species distribution and abundance models cannot predict separate shrub datasets in adjacent Arctic fjords

Chardon

Nabe‐Nielsen

Assmann

et al. 2022

“…Responses to abiotic gradients vary among invasive annual grasses in the Great Basin (Brooks et al, 2016;Bykova & Sage, 2012;McMahon et al, 2021), but changes in a few key climate variables are likely to facilitate range expansions and/ or shifts into higher elevations among several species. In particular, minimum temperature, climatic water deficit (the difference between potential and actual evapotranspiration) and summer precipitation strongly influence the distributions of B. tectorum, B. rubens and (with the exception of summer precipitation) T. caput-medusae (Bradley, 2009;Jarnevich et al, 2021;McMahon et al, 2021). Warming effects on B. tectorum at high elevation are contingent on adequate soil moisture during the growing season (Compagnoni & Adler, 2014a).…”

Section: Climate Trendsmentioning

confidence: 99%

The elevational ascent and spread of exotic annual grass dominance in the Great Basin, USA

Smith

Allred

Boyd

et al. 2021

Aim:In the western United States, sagebrush (Artemisia spp.) and salt desert shrublands are rapidly transitioning to communities dominated by exotic annual grasses, a novel and self-reinforcing state that threatens the economic sustainability and conservation value of rangelands. Climate change is predicted to favour annual grasses, potentially pushing transitions to annual grass dominance into higher elevations and north-facing aspects. We sought to quantify expansion of annual grass-dominated vegetation communities along topographic gradients over the past several decades. Location: Our analysis focused on rangelands among three ecoregions in the GreatBasin of the western United States, where several species of exotic annual grasses are widespread among shrub and perennial grass-dominated vegetation communities. Methods:We used recently developed remote sensing-based rangeland vegetation data to produce yearly maps of annual grass-dominated vegetation communities spanning 1990-2020. With these maps, we quantified the rate of spread and characterized changes in the topographic distribution (i.e. elevation and aspect) of areas transitioning to annual grass dominance. Results:We documented more than an eightfold increase in annual grass-dominated area since 1990, occurring at an average rate of >2,300 km 2 year −1 (0.6% of the area of Great Basin rangelands). In 2020, annual grasses dominated approximately one-fifth (>77,000 km 2 ) of Great Basin rangelands. This rapid expansion was associated with a broadening topographic niche, with widespread movement into higher elevations and north-facing aspects consistent with predicted effects of a warming climate.Main conclusions: More than a century after first appearing in the region, exotic annual grasses continue to proliferate and establish dominance in new environments across the Great Basin. Accelerated, strategic intervention is critically needed to conserve vulnerable sagebrush and salt desert shrub communities not yet heavily invaded. In this era of warming, future climate provides important context for selecting from among alternative management actions and judging long-term prospects of success.

“…Presence–absence data are generally (but not always; Gormley et al, 2011) better than the presence‐only data for modelling species distributions because presence‐only data often suffer from a strong, and difficult to correct, spatial bias in sampling effort (Leroy et al, 2018; Phillips et al, 2009). Furthermore, presence‐only and presence–absence data might misrepresent the spatial distribution of species by exaggerating the importance of small marginal sink populations, contrarily to not only more accurate but also more costly and geographically limited data such as abundance data (Ashcroft et al, 2017; Jarnevich et al, 2021). Thus, the type of data used to measure species occurrence might affect our ability to not only model and predict species distributions but also to understand species–environment relationships (Inman et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

The observed link between urbanization and invasion can depend on how invasion is measured

Gippet

Rocabert

Colin

et al. 2022

Aim: Cities are thought to promote biological invasions because invasive species are more often introduced in urban areas and because they are more successful in disturbed environments. However, the association is not as strongly supported by the literature as is generally assumed and might depend on how urbanization and invasion are measured. In this study, we test if the type of data used to assess the link between urbanization and invasion can affect a study's conclusions.Location: Europe and middle Rhône valley (~5000 km 2 in south-eastern France). Method:We studied the spatial distribution of the invasive garden ant Lasius neglectus in its current introduced range in Europe and tested its association with urbanization using three measures of invasion (presence-only, presence-absence and population area) and two measures of urbanization (urban/nonurban land cover classification and proportion of impervious surfaces (buildings, road) per spatial unit). Results:Based on presence-only data across Europe, L. neglectus occurred in urban areas 10 times more often than expected from a random geographical distribution.However, when controlling for spatial bias in sampling effort with presence-absence data (1870 sampling locations in the middle Rhône valley, France), the occurrence of the species was independent of urbanization. Moreover, the surface occupied by L. neglectus populations was negatively correlated with urbanization.Main conclusions: These findings show that the type of occurrence data used to test the link between urbanization and invasion can strongly affect the conclusion of a study. This is particularly concerning because invasion studies often use presenceonly data that are likely biased towards cities. Future urban invasions studies must be carefully designed to avoid this pitfall.