The importance of roads, nutrients, and climate for invasive plant establishment in riparian areas in the northwestern United States

Menuz, Diane; Kettenring, Karin M.

doi:10.1007/s10530-012-0395-6

Cited by 33 publications

(29 citation statements)

References 61 publications

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“…As mentioned above the main causes of dispersal are by livestock, human transport and by water which explains well the strong influence of these factors in most models. In contrast to Menuz & Kettenring (), our data suggest that landscape structure variables are more relevant for species distribution/invasion at the current stage of invasion than climatic factors (precipitation and temperature), which describe the environmental niche of plant species (Guisan & Thuiller, ). However, at larger spatial scales climatic factors might additionally capture well the distribution pattern of the species (Coutts, Klinkenvan, Yokomizo, & Buckley, ; Cabra‐Rivas, Saldana, Castro‐Dıez, & Gallien, ).…”

Section: Discussioncontrasting

confidence: 99%

Performances of machine learning algorithms for mapping fractional cover of an invasive plant species in a dryland ecosystem

Shiferaw

Bewket

Eckert

2019

Ecology and Evolution

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In recent years, an increasing number of distribution maps of invasive alien plant species (IAPS) have been published using different machine learning algorithms (MLAs). However, for designing spatially explicit management strategies, distribution maps should include information on the local cover/abundance of the IAPS. This study compares the performances of five MLAs: gradient boosting machine in two different implementations, random forest, support vector machine and deep learning neural network, one ensemble model and a generalized linear model; thereby identifying the best‐performing ones in mapping the fractional cover/abundance and distribution of IPAS, in this case called Prosopis juliflora (SW. DC.) . Field level Prosopis cover and spatial datasets of seventeen biophysical and anthropogenic variables were collected, processed, and used to train and validate the algorithms so as to generate fractional cover maps of Prosopis in the dryland ecosystem of the Afar Region, Ethiopia. Out of the seven tested algorithms, random forest performed the best with an accuracy of 92% and sensitivity and specificity >0.89. The next best‐performing algorithms were the ensemble model and gradient boosting machine with an accuracy of 89% and 88%, respectively. The other tested algorithms achieved comparably low performances. The strong explanatory variables for Prosopis distributions in all models were NDVI, elevation, distance to villages and distance to rivers; rainfall, temperature, near‐infrared and red reflectance, whereas topographic variables, except for elevation, did not contribute much to the current distribution of Prosopis . According to the random forest model, a total of 1.173 million ha (12.33% of the study region) was found to be invaded by Prosopis to varying degrees of cover. Our findings demonstrate that MLAs can be successfully used to develop fractional cover maps of plant species, particularly IAPS so as to design targeted and spatially explicit management strategies.

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Section: Discussioncontrasting

confidence: 99%

Performances of machine learning algorithms for mapping fractional cover of an invasive plant species in a dryland ecosystem

Shiferaw

Bewket

Eckert

2019

Ecology and Evolution

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“…Roads can act as conduits for invasion by providing high propagule loads, high light availability, disturbance and low competition from native vegetation (Watkins et al. ; Theoharides & Dukes ; Menuz & Kettenring ). Furthermore, roads can offer novel habitats compared to interior vegetation (Neher et al.…”

Section: Discussionmentioning

confidence: 99%

“…Distance from road can influence the non-native plant community (Pollnac et al 2012;Seipel et al 2012). Roads can act as conduits for invasion by providing high propagule loads, high light availability, disturbance and low competition from native vegetation (Watkins et al 2003;Theoharides & Dukes 2007;Menuz & Kettenring 2013). Furthermore, roads can offer novel habitats compared to interior vegetation (Neher et al 2013) that might enhance colonization by non-native species (Theoharides & Dukes 2007).…”

Section: Spread Into Natural Vegetationmentioning

confidence: 99%

Contrasting distribution patterns of invasive and naturalized non‐native species along environmental gradients in a semi‐arid montane ecosystem

Andersen

Naylor

Endress

et al. 2015

Applied Vegetation Science

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Questions Mountain systems have high abiotic heterogeneity over local spatial scales, offering natural experiments for examining plant species invasions. We ask whether functional groupings explain non‐native species spread into native vegetation and up elevation gradients. We examine whether non‐native species distribution patterns are related to environmental variables after controlling for elevation and, thus, driven by niche processes. Location The Wallowa Mountains, northeast Oregon, USA. Methods We surveyed non‐native plant species along three mountain roads and into the native habitat matrix to assess the extent of invasion success along distance from roadside and elevation gradients. We used GLM to predict single species occurrence probabilities, LMM to examine differences in distribution patterns among functional types, and pCCA to examine multivariate responses of the non‐native community to ecological variables. Results Probability of occurrence of the eight focal invasive species was not significantly related to distance from the road, but declined with elevation. Non‐native species with annual life history strategies were more restricted to lower elevations than perennial species. Non‐native species considered invasive occurred at lower minimum elevations than naturalized species. Shifts in the species composition of the non‐native plant community were related to changes in soil and climate variables. Conclusions Our results suggest that invasive species have similar patterns of habitat associations and spread from roadsides to interior vegetation zones, whereas naturalized species partition environmental gradients in this semi‐arid montane ecosystem. Furthermore, annual and invasive species groups occupy lower elevations and perennial and naturalized species groups have invaded further up the mountain roads and into the native vegetation. Thus, functional groupings may explain contrasting distribution patterns of non‐native species and could be used to inform management strategies for non‐native species.

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“…Distribution modeling has been widely applied to introduced plant species to estimate the vulnerability of sites within a region to invasion (Jarnevich and Reynolds ) and to understand drivers of establishment and spread (Menuz and Kettenring ). However, species distribution models assume that modeled species are at equilibrium with their environment (Araújo and Pearson , Guisan and Thuiller , Elith and Leathwick ).…”

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confidence: 99%

Non‐equilibrium in plant distribution models – only an issue for introduced or dispersal limited species?

et al. 2014

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Species distribution models rely on the assumption that species' distributions are at equilibrium with environmental conditions within a region – i.e. they occur in all suitable habitats. If this assumption holds, species occurrence should be predictable from measures of the environment. Introduced species may be poor candidates for distribution models due to their presumed lack of equilibrium within the landscapes they occupy, although predicting their potential distributions is often of critical importance to natural resource managers. We determined if the accuracy of species distribution models differed between 17 native and 17 introduced riparian plant species in the western United States. We also assessed if model accuracy was associated with both environmental and biological factors that can influence dispersal. We used Random Forests to model species distributions and linear regression to determine if model accuracy was associated with dispersal‐related traits. Model accuracy for introduced species was higher than that for native species. Dispersal‐related traits did not affect model accuracy or improvement, though two other traits, family affiliation and rarity on the landscape, did have an effect. Distance‐based measures of dispersal potential improved model fit equally for both native and introduced species and for species with a variety of dispersal traits, suggesting that the importance of regional propagule pressure is relatively constant across species with different dispersal opportunities. Several lines of future questioning are suggested by our results, including why introduced species may in some cases produce more accurate distribution models than native species and how species dispersal traits relate to distribution model accuracy at different spatial scales.

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The importance of roads, nutrients, and climate for invasive plant establishment in riparian areas in the northwestern United States

Cited by 33 publications

References 61 publications

Performances of machine learning algorithms for mapping fractional cover of an invasive plant species in a dryland ecosystem

Performances of machine learning algorithms for mapping fractional cover of an invasive plant species in a dryland ecosystem

Contrasting distribution patterns of invasive and naturalized non‐native species along environmental gradients in a semi‐arid montane ecosystem

Non‐equilibrium in plant distribution models – only an issue for introduced or dispersal limited species?

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