Determining key monitoring areas for the 10 most important weed species under a changing climate

Science of the Total Environment 683 (2019) 568–577

Contents lists available at ScienceDirect

Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv

Determining key monitoring areas for the 10 most important weed species under a changing climate Ji-Zhong Wan a,c, Chun-Jing Wang a,b,⁎ a b c

State Key Laboratory of Plateau Ecology and Agriculture, Qinghai University, Xining 810016, China College of Agriculture and Animal Husbandry, Qinghai University, Xining 810016, China Departamento de Ecología, Facultad de Ciencias Biológicas, Pontificia Universidad Católica de Chile, Santiago, Chile

H I G H L I G H T S

G R A P H I C A L

A B S T R A C T

• We determined key monitoring areas for the 10 most important weed species under climate change using conservation prioritization analysis coupled with habitat suitability modelling. • We determined some specific biomes (e.g., tropical & subtropical biomes) as key monitoring areas. • Effective management strategies are necessary to prevent and control the distribution of these 10 weed species in vulnerable ecoregions.

a r t i c l e

i n f o

Article history: Received 23 February 2019 Received in revised form 13 May 2019 Accepted 13 May 2019 Available online 16 May 2019 Editor: SCOTT SHERIDAN Keywords: Biome Climatic change Conservation prioritization analysis Ecoregion Habitat suitability modelling Weed management

a b s t r a c t On a global level, weed species have a large potential to threaten ecosystems under a changing climate. The determination of key monitoring areas is an effective approach to prevent and control the spread of such species. The 10 most important weeds have been listed on a global scale. It is therefore crucial to delineate the areas with high monitoring ranks for the 10 most important weed species under climate change. We coupled conservation prioritization analysis with habitat suitability modelling to determine key monitoring areas for these species, based on different types and vulnerability levels of biomes under current and future (i.e., 2040–2069 and 2070–2099) scenarios. We determined some specific biomes (i.e., tropical and subtropical biomes, flooded grasslands and savannas, Mediterranean forests, woodlands and scrub, and mangroves) as key monitoring areas for the 10 most important weed species under a changing climate. These biomes are distributed in most regions of Latin America, the United States, Europe, central and south Africa, south and southeast Asia, southeast Australia, and New Zealand, including large vulnerable ecoregions. Tropical and subtropical grasslands, savannas, and shrublands were particularly vulnerable, because these biomes had the largest area with a high monitoring rank, and this rank was predicted to further increase in the near future. Our study highlights the importance of effective management strategies for the prevention and control of these species across different biomes on a global scale. © 2019 Elsevier B.V. All rights reserved.

⁎ Corresponding author at: State Key Laboratory of Plateau Ecology and Agriculture, College of Agriculture and Animal Husbandry, Qinghai University, Xining 810016, China. E-mail address: [email protected] (C.-J. Wang).

https://doi.org/10.1016/j.scitotenv.2019.05.175 0048-9697/© 2019 Elsevier B.V. All rights reserved.

J.-Z. Wan, C.-J. Wang / Science of the Total Environment 683 (2019) 568–577

1. Introduction Weeds can grow and reproduce aggressively and have the ability to invade unwanted habitats because of their generally broad physiological niches and/or specific traits (Roush and Radosevich, 1985; Storkey, 2004; Van Kleunen et al., 2010). They can also quickly adapt to changing environmental conditions and different biomes (Dennill and Donnelly, 1991; Gallagher et al., 2010; Rouget et al., 2015). There is substantial evidence on weeds that can grow in disturbed or degraded environments with damaged or frequently disturbed soil or natural vegetation cover, often making them the dominant species in such habitats (Grime, 1988). In some cases, this prevents the original habitats from naturally recovering (Chamorro et al., 2016; Hitchmough et al., 2017). Weeds have a strong ability to compete for resources or space with native plants in areas with both low and high plant diversity (Johnson, 1971; Van Kleunen et al., 2010). Biodiversity and ecosystem stability are negatively affected by the expansion of weeds (Roush and Radosevich, 1985; Benvenuti, 2004; Storkey, 2004; Tscharntke et al., 2005). Furthermore, weeds can move out of their natural geographic ranges and spread to different parts of the world, mainly assisted by human activities (e.g., tourism; Pickering and Mount, 2010; Alvarado-Serrano et al., 2019). The expansion of weeds potentially leads to economic or environmental problems (Zanin et al., 1993; Lososová et al., 2004; Rodenburg et al., 2016). For example, weeds can have a detrimental impact on water and soil use by humans, and in the case of Europe, considerable economic losses occurred because of the invasion of weeds into crop and forest areas (Scheepens et al., 2001; Streit et al., 2002; Lososová et al., 2004). Holm (1969) listed the 10 most important weed species on a global scale. These species have large distributions and can expand widely across different biomes. Recent studies (e.g., Ervin and Holly, 2011; Estrada and Flory, 2015; Qin et al., 2016) confirm that the list is still relevant and have paid attention to the policies developed for their prevention and control. For example, Imperata cylindrica, a dominant weed in the pine-grassland biomes of tropical and subtropical Asia, can create an intense competitive environment with commercially important species (Holm, 1969; MacDonald, 2004; Ervin and Holly, 2011). This weed species has been listed as one of the 100 worst invasive alien species in the world by Lowe et al. (2000). Numerous studies (e.g., Holm, 1969; Monaghan, 1979; Panetta and Mitchell, 1991; Estrada and Flory, 2015; Wan et al., 2019) have shown that these 10 species have substantial negative effects on natural ecosystems' stability and ecosystem services. Spatial analysis is a valuable tool to quantitatively assess high risk areas to make adequate monitoring and control strategies (namely, determine key monitoring areas) for the 10 most important weeds on a global scale (Holm, 1969; Thuiller et al., 2005; Liang et al., 2014). The concept of key monitoring areas could improve the accuracy of prevention and control for weeds (Mostert et al., 2018; Rodgers et al., 2018). Understanding the delineation of key monitoring areas will be helpful in determining patterns of risk and help in identifying the factors driving changes in weed distribution from regional to global scales. Global climatic change has the potential to alter the geographic distribution of weeds and to promote their growth in new habitats (Dukes and Mooney, 1999; Gallagher et al., 2010; Clements and Ditommaso, 2011; Ziska et al., 2011). Furthermore, the environmental niches of such species can change across different habitats depending on available resources. Weeds have a strong ability to expand in various environmental conditions. Hence, the economic or environmental problems caused by the expansion of the 10 most important weeds may be enhanced by global climate change. To address various relevant economic or environmental problems, it is crucial to determine key monitoring areas for these species against the background of climate change. As a result, the prevention and control of weeds would become more effective. However, the planning and establishment of key monitoring areas remains a challenging task.

569

With climate as the dominant factor influencing the distribution of plants, habitat suitability modelling (HSM) has been developed to model the geographic distribution of weeds with the aim to project potential future changes in their geographic range across different spatial scales (from the community to the globe) based on the results of previous studies (e.g., Wilson et al., 2009; Duursma et al., 2013; Sheppard, 2013; Beaumont et al., 2014; Akasaka et al., 2015). In combination with HSM, conservation planning software (e.g., Zonation and Marxan) has been used to examine the effectiveness of existing reserve systems and prioritize biodiversity conservation under climate change (AdamsHosking et al., 2015; Li et al., 2017; Spiers et al., 2018). Here, we used this approach in the context of weeds (Li et al., 2017). In the present study, we used the MaxEnt method of HSM to project the geographic distribution of the 10 most important weeds under predicted climate change and delineated key monitoring areas for these species on a global scale. This enabled us to formulate management suggestions based on key monitoring area planning. If key monitoring areas could be delineated for these species, we investigated the plausible causal links by testing the following hypotheses: 1) if climate change can alter the geographic distribution of these weeds, the key monitoring areas will be changed; 2) if these species can distribute across the globe under the conditions of climate change, the coverage of key monitoring areas across biomes will be different, and there will be differences for different weed species; and 3) vulnerable biomes have a high monitoring rank for weeds because such species are likely to spread in vulnerable areas.

2. Material and methods 2.1. Species data The 10 most important weed species, based on the study by Holm (1969), include purple nutsedge (Cyperus rotundus), Bermuda grass (Cynodon dactylon), barnyard grass (Echinochloa crus-galli), jungle rice (Echinochloa colonum), goose grass (Eleusine indica), Johnson grass (Sorghum halepense), guinea grass (Panicum maximum), water hyacinth (Eichhornia crassipes), cogon grass (Imperata cylindrica), and Lantana (Lantana camara; Table 1). The occurrence records for these species were downloaded from the Global Biodiversity Information Facility (GBIF; https://www.gbif.org/). We conducted data harmonization and cleaning pipelines with the following aspects: 1) duplicated records within the area of specific spatial resolution; 2) records with both longitude and latitude = 0°; 3) records in which the values of longitude and latitude were identical and probably represent erroneous repetitive data entry; and 4) records with incorrect species names (GarcíaRoselló et al., 2015). Detailed occurrence records are shown in Table 1.

Table 1 Basic MaxEnt modelling results. Samples: the occurrence records of the 10 most important weed species as the input of MaxEnt modelling; Training omission: Mean ± SD values of training omission rates based on six thresholds, including fixed cumulative value 1, fixed cumulative value 5, fixed cumulative value 10, equal training sensitivity and specificity, equal test sensitivity and specificity, and balance training omission, predicted area, and threshold value to assess the binomial probabilities (Phillips et al., 2006; Phillips and Dudík, 2008). Species

Samples

Training AUC

Test AUC

Training omission

Cynodon dactylon Cyperus rotundus Echinochloa colona Echinochloa crus-galli Eichhornia crassipes Eleusine indica Imperata cylindrica Lantana camara Panicum maximum Sorghum halepense

7169 2409 3315 7931 1795 2904 2597 4133 1940 3092

0.773 0.848 0.817 0.774 0.884 0.818 0.855 0.829 0.872 0.852

0.772 0.846 0.815 0.774 0.882 0.815 0.853 0.828 0.869 0.851

0.142 ± 0.129 0.109 ± 0.101 0.122 ± 0.114 0.143 ± 0.128 0.094 ± 0.083 0.126 ± 0.116 0.109 ± 0.095 0.118 ± 0.107 0.099 ± 0.088 0.108 ± 0.095

570

J.-Z. Wan, C.-J. Wang / Science of the Total Environment 683 (2019) 568–577

2.2. Environmental data We collected environmental variables at 10-arc-min resolution, including human footprint variables (Sanderson et al., 2002), soil variables (Hengl et al., 2014), and current climate variables (Karger et al., 2017). We obtained data for eight soil variables [i.e., bulk density (kg/ cubic-meter); cation exchange capacity (cmolc/kg); soil texture fraction clay (%); coarse fragments volumetric (%); soil organic carbon stock (tonnes per ha); soil pH; soil texture fraction silt (%); soil texture fraction sand (%); Hengl et al., 2014] at 0.5-arc-min resolution from SoilGrids at 1 km (http://soilgrids.org/) and downloaded human footprint data at 0.5-arc-min resolution from the Global Human Footprint Dataset of the Last of the Wild Project, Version 2, 2005 (LWP-2; HFD; http://sedac.ciesin.columbia.edu/wildareas/). ArcGIS 10.5 (Esri; Redlands, CA, USA) was used to resample the 0.5-arc-min data into 10.0arc-min resolution for human footprint and soil variables. Four climate variables [annual mean temperature (°C*10); temperature seasonality (standard deviation *100); annual precipitation; precipitation seasonality (coefficient of variation); Karger et al., 2017] were selected (http:// chelsa-climate.org/). Environmental variables with Pearson's correlation coefficients between −0.75 and 0.75 were removed to eliminate the negative effect of multicollinearity on the adjustment of the regression models. The selected variables of our study were closely related to the distribution and physiological performance of weeds (Holm, 1969; Thuiller et al., 2005). Human footprint and soil variables were unchanged when projecting future species distributions. The selected variables of future climate were obtained from analogue datasets [for 2040–2069 (2050s) and 2070–2099 (2080s)] of three climate models (i.e., mohc_hadgem2, csiro_mk3_6_0, and bcc_csm1_1_m), downloaded from the website of the International Centre for Tropical Agriculture (http://ccafs-climate.org; Beaumont et al., 2011). These global climate models include bio-geochemical components that account for the important fluxes of carbon among ocean, atmosphere, and terrestrial biosphere carbon reservoirs. In addition, these models are related to dynamic vegetation components (Wang and Su, 2013). Representative Concentration Pathway (RCP) was defined as a specific emission scenario (including data on land use and land cover) as a plausible pathway towards reaching each target radiative forcing trajectory (Moss et al., 2010). We selected RCPs 4.5 (mean 780 ppm; range 595 to 1005 by 2100) and 8.5 (mean 1685 ppm; range 1365 to 1910 by 2100) to represent the scenarios of low and high greenhouse gas concentrations, respectively. RCP 8.5 is different from RCP 4.5 by having larger cumulative concentrations or emissions of carbon dioxide. The result is a different pattern of climate change due to varying anthropogenic emissions of greenhouse gases and other pollutants (Riahi et al., 2011; Thomson et al., 2011). These two RCPs were widely used for modelling the distributions of global harmful plant species (e.g., weeds and invasive plants) under a changing climate (e.g., Bellard et al., 2013; Duursma et al., 2013; Sheppard, 2013).

2.3. Biome data The vector map of global biomes was obtained from the study by Olson et al. (2001; https://www.worldwildlife.org/), which delineated 867 ecoregions. This map represents an effective tool for biodiversity conservation on a global scale (Olson and Dinerstein, 2002). A total of 14 biomes (i.e., tropical and subtropical moist broadleaf forests, tropical and subtropical dry broadleaf forests, tropical and subtropical coniferous forests, temperate broadleaf and mixed forests, temperate conifer forests, boreal forests/taiga, tropical and subtropical grasslands, savannas and shrublands, temperate grasslands, savannas and shrublands, flooded grasslands and savannas, montane grasslands and shrublands, tundra, Mediterranean forests, woodlands and scrub, deserts and xeric shrublands, and mangroves; Fig. S1) belonged to three vulnerability levels: (1) critical or endangered; (2) vulnerable; and

(3) relatively stable or intact (Olson et al., 2001; Olson and Dinerstein, 2002). 2.4. Habitat suitability modelling We used MaxEnt modelling (http://biodiversityinformatics.amnh. org/open_source/maxent/) to project the suitable habitat distributions of 10 weed species against the background of a changing climate on a global scale (Phillips et al., 2006, 2017). The specific approach has been widely used in modelling the habitat suitability of weeds under climate change based on occurrence records and environmental variables. Here, we set the MaxEnt model as follows: 1) We set the regularization multiplier (beta) to 2.0 to produce a smooth and general response curve that represents a biologically realistic behaviour (Radosavljevic and Anderson, 2014). 2) We set the maximum number of background points to 10,000, with the same bias as the buffer of occurrence records. The background records were constrained to closely match the empirical average ranges of occurrence data based on GBIF (Phillips et al., 2009). 3) A five-fold cross-validation approach was used to remove biases with respect to occurrence records (Moreno-Amat et al., 2015). 4) We used a complementary log-log (cloglog) transformation to produce an estimate of habitat suitability of weeds (Phillips et al., 2017). 5) Future projection was predicted by clamping as the ranges of occurrence records (Merow et al., 2013). 6) Other settings were the same as in Merow et al. (2013). We evaluated the predictive precision of the species distribution models by using the area under the curve (AUC) of the receiver operation characteristic, which regards each value of the prediction result as a possible threshold and then obtains the corresponding sensitivity and specificity values to calculate the curve (Phillips and Dudík, 2008). The training omission rate showed the proportion of training grid cells of distribution areas compared to the predicted absence distribution areas (Phillips et al., 2006; Phillips and Dudík, 2008). A one-tailed binomial test was applied to investigate the null hypothesis that test points are predicted no better than via random prediction. We used six thresholds, including fixed cumulative value 1, fixed cumulative value 5, fixed cumulative value 10, equal training sensitivity and specificity, equal test sensitivity and specificity, and balance training omission, predicted area, and threshold value to assess the binomial probabilities (Phillips et al., 2006; Phillips and Dudík, 2008). These six thresholds were widely used to assess the suitable habitat distributions of species (e.g., Thuiller et al., 2005; Phillips et al., 2006; Sheppard, 2013). We considered models for each weed species with an AUC higher than 0.7 and an average training omission rate lower than 17%, based on six thresholds, for further analysis (Phillips and Dudík, 2008; Anderson and Gonzalez Jr, 2011). 2.5. Planning key monitoring areas We used the Zonation framework (Zonation v 4.0 software) to plan the key monitoring areas for the 10 weed species under climate change on a global scale, based on habitat suitability maps from the HSM (Li et al., 2017). This approach has been widely used in conservation prioritization analysis for biological diversity (Lehtomäki and Moilanen, 2013; Wan et al., 2014; Adams-Hosking et al., 2015; Budiharta et al., 2016; Kukkala and Moilanen, 2017). Zonation framework identified key monitoring areas with high rank scores by a post hoc analysis of ecologically optimized prioritization and the removal of sites (Lehtomäki and Moilanen, 2013). The areas with high habitat suitability had a high monitoring rank for weeds (Li et al., 2017). We ran our analyses using the additive benefit function of Zonation, which assigns rank values to each cell based on the sum of weighted habitat suitability of species, and set the warp factor to 10 to maximize the analysis speed while maintaining output reliability (Moilanen, 2007; Lehtomäki and Moilanen, 2013).

J.-Z. Wan, C.-J. Wang / Science of the Total Environment 683 (2019) 568–577

The input of the Zonation framework consisted of grid cell maps of habitat suitability for all 10 weed species in current, low, and high greenhouse gas scenarios based on MaxEnt models. These maps also included two periods [i.e., 2040–2069 (2050s) and 2070–2099 (2080s)] of three climate models (i.e., mohc_hadgem2, csiro_mk3_6_0, and bcc_csm1_1_m). Subsequently, the maps of key monitoring areas, produced by Zonation, were averaged into one single map based on the three above-mentioned climate models. This way, we could obtain five grid cell maps of key monitoring areas (i.e., current scenario, and low and high scenarios for the 2050s and 2080s, respectively). Then, we analysed the key monitoring areas for the 10 weed species separately based on current, 2050s, and 2080s maps for each climate model (i.e., mohc_hadgem2, csiro_mk3_6_0, and bcc_csm1_1_m) of RCP 4.5 and 8.5. The map of each species was averaged based on three climate models (i.e., mohc_hadgem2, csiro_mk3_6_0, and bcc_csm1_1_m) for the 2050s and 2080s. Thus, we could obtain twenty grid cell maps of key monitoring areas (i.e., RCP 4.5 and 8.5 for 10 weed species, respectively). We computed the average monitoring rank score of grid cells from the Zonation framework for each ecoregion. To explore the change in the monitoring ranks of weeds under different greenhouse gas concentrations, we calculated the log response ratio of habitat suitability: RR = ln (Xf / Xc), where RR is the log response ratio of average monitoring rank score of grid cells in an ecoregion, and Xc and Xf are the average monitoring rank scores of grid cells in a specific ecoregion in current and future scenarios, respectively (Hedges et al., 1999; Lajeunesse, 2015; Wan et al., 2017). We weighted RR by sample size, using the equation n/2, where n is the number of grid cells within an ecoregion (Hedges et al., 1999; Wan et al., 2017). For quantification on the log response ratio of average monitoring rank score of grid cells in an ecoregion, the paired sample t-test (i.e., nonparametric test) was used in our analysis with a Bonferroni adjustment. This enabled us to calculate the average and standard deviation of the monitoring rank scores of ecoregions, and the relevant changes between current and future scenarios within biomes belonging to 14 types and three vulnerability levels (Hedges et al., 1999; Lajeunesse, 2015; Wan et al., 2017). Biomes with a high monitoring rank could be determined based on the monitoring rank score of the ecoregions belonging to a specific biome being significantly higher than all the ecoregions around the world (Bellard et al., 2013). We used an ANOVA test to compare the mean scores of monitoring rank scores between the ecoregions of selected biome and all the ones on a global scale. The paired sample t-test and ANOVA test were conducted in JMP version 11.0 (SAS Institute Inc., Cary NC). We calculated the average values of the ecoregion monitoring rank score for each weed species based on biome types to compare the monitoring ranks across the 10 weed species (Table S1). We found that there were significant correlations among ecoregional monitoring rank scores across low and high gas concentration scenarios of 2050s and 2080s (all the Pearson correlation coefficients N0.950; P b 0.001). Therefore, only the monitoring maps based on high gas concentration scenario (RCP 8.5) were used for further analysis. The workflow of our analysis was shown in Fig. 1. 3. Results Both training and test AUC modelling ranged from 0.772 to 0.884 across the 10 weed species. Average training omission rates range from 0.094 ± 0.083 to 0.143 ± 0.128 based on six thresholds (i.e., fixed cumulative value 1, fixed cumulative value 5, fixed cumulative value 10, equal training sensitivity and specificity, equal test sensitivity and specificity, and balance training omission, predicted area, and threshold value to assess the binomial probabilities). Our results indicated that the performances of the HSMs were good, and all results could be used in further tests for all 10 weed species (Table 1). Areas with high monitoring ranks in terms of the 10 most important weed species were widely distributed around the world (Fig. 2). We

571

Fig. 1. Workflow of our study (detailed information in Material and methods).

used the monitoring maps based on high gas concentration scenarios for the 2050s and 2080s for further analysis (Fig. 2). Across the current, low and high greenhouse gas concentration scenarios, the dominant regions were parts of Latin America, the United States, Europe, central and south Africa, south and southeast Asia, southeast Australia, and New Zealand (Figs. 2 and 3; Table S2). However, the areas with highest monitoring ranks were changed across different species and biomes (Figs. 2 and 3; Tables S3 and S4). The monitoring rank was highest for E. crusgalli in western Europe under climate change (Fig. 3 and Table S3). In current and future scenarios, the biomes with the highest monitoring ranks (monitoring rank scores N0.700) included tropical and subtropical moist broadleaf forests (the highest for P. maximum), tropical and subtropical dry broadleaf forests (the highest for E. crassipes), tropical and subtropical coniferous forests (the highest for L. camara), temperate broadleaf and mixed forests (the highest for E. crus-galli), tropical and subtropical grasslands, savannas and shrublands (the highest for P. maximum), flooded grasslands and savannas (the highest for E. colona), Mediterranean forests, woodlands and scrub (the highest for C. dactylon), and mangroves (the highest for E. crassipes; Tables 2 and S4). The areas of critical or endangered and vulnerable ecoregions were larger than those of relatively stable or intact regions with a high monitoring rank (Fig. 4; Table S5). The critical or endangered ecoregions with high monitoring ranks were mainly distributed in Latin America, the United States, Europe, south and southeast Asia, southeast Australia, and New Zealand (Fig. 4). The distribution of vulnerable

572

J.-Z. Wan, C.-J. Wang / Science of the Total Environment 683 (2019) 568–577

Fig. 2. Key monitoring areas for the 10 most important weed species under a changing climate on a global scale. The color of maps from blue to red represented the monitoring rank of the 10 most important weed species from low to high levels across the current and high greenhouse gas concentration scenarios of 2050s (2040–2069) and 2080s (2070–2099). High greenhouse gas concentration scenario referred to Representative Concentration Pathway 8.5 (RCP 8.5).

ecoregions with high monitoring ranks was similar when compared to that of critical or endangered ecoregions, except for Europe, south Asia, and New Zealand (Fig. 4). The large areas of relatively stable or intact ecoregions with high monitoring ranks were distributed in South America, southeast Asia, Australia, and south Africa (Fig. 4). Regarding species, high monitoring ranks in critical or endangered and vulnerable ecoregions were the largest for E. crassipes, E. indica, L. camara, and P. maximum. E. crassipes, E. indica, and L. camara should be monitored with high priority in relatively stable or intact ecoregions (Table S4). Areas with increasing monitoring ranks were mainly distributed in the northern hemisphere and in the central regions of Africa and South America (t-test: P b 0.05; Figs. 2 and S1). Specifically, areas with high monitoring ranks in tropical and subtropical grasslands, savannas and shrublands, montane grasslands and shrublands, and mangroves were either critical, endangered, or vulnerable, while areas of flooded grasslands and savannas were relatively stable or intact in the current scenario as well as in the low and high scenarios (Figs. 4 and S1; Table S6). The critical or endangered and relatively stable or intact ecoregions with the largest increase in monitoring ranks belonged to the boreal forests/taiga ecoregion, while the vulnerable regions

belonged to the montane grasslands and shrublands ecoregion (Figs. 4 and S1; Table S6). 4. Discussion We found that climate change may not affect the distribution of areas with high monitoring ranks of the 10 most important weed species on a global scale, but such effects may be enhanced following the concomitant changes in ecoregion and biome types. Hence, monitoring is an important step for the prevention and control of weed species in certain biomes on a global scale. Our study could give new insights into risk assessments of biological invasions under climate change. Climate change can drive potentially harmful plant species to expand widely at large spatial scales (e.g., across different continents; Hellmann et al., 2008; Allen and Bradley, 2016; Petitpierre et al., 2016, 2017). These species have similar ecological characteristics: 1) a wide environmental niche; 2) strong dispersal abilities across long distances; 3) a high tolerance to extreme weather; and 4) strong competitive ability; some of them even have the potential to alter the ecological landscape (Hellmann et al., 2008; Van Kleunen et al., 2010; Hulme, 2017).

J.-Z. Wan, C.-J. Wang / Science of the Total Environment 683 (2019) 568–577

573

Fig. 3. Key monitoring areas for each of the 10 most important weed species under the high greenhouse gas concentration scenario (Representative Concentration Pathway 8.5) on a global scale. The color of maps from blue to red represented the monitoring rank of the 10 most important weed species from low to high levels.

All 10 weed species show the above-mentioned characteristics, which means that under a changing climate, they very likely threaten the biodiversity of these areas (Holm, 1969). For example, L. camara is a weed in large areas of the Paleotropics and is highly resistant to fire; it therefore potentially changes fire patterns in a forest ecosystem (Gentle and Duggin, 1997; Berry et al., 2011; Osunkoya and Perrett, 2011). Soil nitrogen concentrations can be threatened by dense populations of E. crusgalli (Korres et al., 2017). Previous studies have shown that climate change may be a main driver of forest fires (Flannigan et al., 2000). It is highly likely that L. camara can expand into and even invade intact ecosystems under climate change. Hence, L. camara is a very problematic weed and enters rainforests and wildlands on a large scale (MacDonald, 2004; Ervin and Holly, 2011; Qin et al., 2016). In Australia, L. camara has already occurred in numerous sites, even at current conditions (MacDonald, 2004; Ervin and Holly, 2011). The overall distribution of areas with high monitoring ranks would be unchanged under the low and high greenhouse gas scenarios, although the areas with high monitoring ranks would vary following changes in ecoregions on a global scale, indicating that monitoring

rank scores in our study are an efficient indicator of weed prevention and control priority by biome type against the background of a changing climate. We determined that eight biomes were key to monitoring the 10 most important weed species under a changing climate. The 10 most important weed species are likely to expand in tropical and subtropical biomes (particularly, P. maximum, E. crassipes and L. camara) because tropical and subtropical regions have suitable climatic conditions and rich soil resources (Sheil, 1999; Ziska et al., 2011; van Klinken and Friedel, 2018). For example, subtropical biomes in New Zealand have been widely invaded by weeds (Panetta and Mitchell, 1991). Furthermore, future climate change may enhance the expansion of weeds and the threat to native biodiversity in New Zealand (Sheppard, 2013; Sheppard et al., 2016). Hence, New Zealand is still an area with high monitoring ranks for weeds under climate change, which was consistent to our study. Tropical and subtropical grasslands, savannas, and shrublands represented the biome with the largest area of a high monitoring rank, and this rank is predicted to further increase in the near future, mainly as a result of 1) the strong competitive ability of these weed species

574

J.-Z. Wan, C.-J. Wang / Science of the Total Environment 683 (2019) 568–577

Table 2 Monitoring ranks (mean ± SD) of the 10 most important weed species under a changing climate across biomes and vulnerability levels. We made the bold notes for the ecoregions of high monitoring rank belonging to specific biomes and vulnerability levels. Current rank represented monitoring ranks of the 10 most important weed species under current concentration scenario; Future rank represented monitoring ranks of the 10 most important weed species under the high gas concentration scenario (Representative Concentration Pathway 8.5) of 2080s (2070–2099). Current rank

Future rank

Biome Tropical and Subtropical Moist Broadleaf Forests Tropical and Subtropical Dry Broadleaf Forests Tropical and Subtropical Coniferous Forests Temperate Broadleaf and Mixed Forests Temperate Conifer Forests Boreal Forests/Taiga Tropical and Subtropical Grasslands, Savannas and Shrublands Temperate Grasslands, Savannas and Shrublands Flooded Grasslands and Savannas Montane Grasslands and Shrublands Tundra Mediterranean Forests, Woodlands and Scrubs Deserts and Xeric Shrublands Mangroves

0.776 ± 0.1340 0.858 ± 0.0762 0.883 ± 0.0736 0.709 ± 0.1884 0.570 ± 0.2045 0.288 ± 0.1349 0.796 ± 0.0992 0.547 ± 0.2316 0.716 ± 0.2003 0.590 ± 0.2409 0.232 ± 0.1729 0.888 ± 0.0756 0.604 ± 0.1818 0.848 ± 0.0903

0.768 ± 0.1412 0.851 ± 0.0800 0.881 ± 0.0703 0.770 ± 0.1898 0.633 ± 0.2333 0.332 ± 0.1954 0.746 ± 0.1163 0.541 ± 0.2394 0.666 ± 0.2119 0.615 ± 0.2483 0.297 ± 0.2165 0.860 ± 0.1089 0.537 ± 0.1954 0.843 ± 0.0912

Vulnerability Critical or Endangered Vulnerable Relatively Stable or Intact

0.767 ± 0.1831 0.638 ± 0.2235 0.514 ± 0.2431

0.769 ± 0.1829 0.625 ± 0.2338 0.525 ± 0.2497

compared with native plant species (Roush and Radosevich, 1985; Gentle and Duggin, 1997); and 2) suitable conditions for seed dispersal of these weed species in grasslands, savannas, and shrublands (Brown and Carter, 1998; Chuong et al., 2016; Möhler et al., 2018). P. maximum should be monitored with priority because air temperature has a positive effect on the growth of P. maximum (Habermann et al., 2019). Moreover, these biomes have large forest areas with high monitoring ranks (Van Auken, 2000; Jenkins and Pimm, 2003; van Klinken and Friedel, 2018). In this sense, it is crucial to consider the effects of fire and climate change on such forest habitats, because most of these important weed species can invade areas with frequent fires (Ens et al., 2015; Sánchez Meador et al., 2017). The above-mentioned biomes contain large areas of vulnerable habitats and ecoregions which are potentially threatened by the invasion of these weed species. Critical or endangered and vulnerable ecoregions, particularly tropical and subtropical grasslands, savannas, and shrublands, montane grasslands and shrublands, and mangroves were larger than the relatively stable or intact ecoregions when considering the areas with high monitoring ranks, highlighting the importance of adequate prevention and control programs for weeds. Olson and Dinerstein (2002) provided an estimate of ecoregional vulnerability across different biomes, based on landscape-level vulnerability features such as total habitat loss, fragmentation degree, and protection degree. Ecoregional vulnerability may affect habitat suitability of some invasive plant species on a global scale (Kalusová et al., 2013; Wan et al., 2019). These invasive plants can expand widely across different global biomes as similarly as weeds (Holm, 1969; Bellard et al., 2013; Wan et al., 2019). The environmental disturbance hypothesis implies that environmental disturbances can result in the expansion of the distribution range of weeds, and rapid global change potentially promotes plant invasions around the world (Sheil, 1999; Roxburgh et al., 2004; Catford et al., 2012). The intermediate disturbance hypothesis has shown that moderate levels of disturbance can promote harmful plant richness by preventing competitive exclusion (Leishman and Thomson, 2005; Catford et al., 2012; Peltzer et al., 2016). In our study, Latin America, the United States, Europe, south and southeast Asia, southeast Australia, and New Zealand contained large areas of vulnerable ecoregions with high risk to the 10 most important weed species under a changing climate (Gallagher et al., 2010; Catford et al., 2012; Peltzer et al., 2016). For example, numerous studies (e.g., Brown and Carter, 1998; Leishman and Thomson, 2005; Wilson et al., 2009;

Duursma et al., 2013; Sheppard et al., 2016; van Klinken and Friedel, 2018) have indicated that these weed species have already invaded non-native regions of Australia and New Zealand, and our study provides further evidence for the importance of efficient weed management programs in Australia and New Zealand, highlighting the importance of integrating climate change into weed control strategies at large spatial scales. Based on the vulnerability levels of different biomes, the following actions for the prevention and control of weed species should be taken: 1) effective monitoring strategies should be defined and applied for the 10 most important weed species under a changing climate (Jenkins and Pimm, 2003; Zhang, 2003; van Wilgen et al., 2008). Considering the study by Olson et al. (2001), specific monitoring steps could be conducted (Jenkins and Pimm, 2003; Gallagher et al., 2010; Rouget et al., 2015). 2) Policies for the prevention of the intentional or accidental introduction or dispersal of these weed species should be developed under a changing climate following the type of biome (e.g., tropical and subtropical grasslands, savannas, and shrublands; Zhang, 2003; Akasaka et al., 2015; Chuong et al., 2016). 3) A comprehensive analysis of the global exchange pathways for wee species across continents would facilitate the prevention of humanmediated dispersal (Pickering and Mount, 2010; Alvarado-Serrano et al., 2019). The determination of exchange pathways could be based on the map of ecoregional vulnerability levels assessed by Olson and Dinerstein (2002). Critical or endangered and vulnerable ecoregions should be the priority areas for monitoring weed dispersal by human activities because weeds can grow in disturbed or degraded environments. 5. Limitations Although our study provides global maps of key monitoring areas for the 10 most important weed species under a changing climate, it has several limitations: First, the likelihood of weed spread depends on numerous factors such as region of origin, destination region, human usage, likelihood of being transported, and sensitivity of the invaded region; these factors need to be considered in future studies. Second, we only used the presence-only HSM to model the distribution of weeds under climate change. There are still some uncertainties in terms of the HSM results for plant species on a global scale; one of the main problems may be the low modelling transferability of HSM (Moreno-Amat

J.-Z. Wan, C.-J. Wang / Science of the Total Environment 683 (2019) 568–577

575

Fig. 4. Key monitoring ecoregions by vulnerability level for the 10 most important weed species under the high greenhouse gas concentration scenario (Representative Concentration Pathway 8.5) in the 2080s (2070–2099). The color of maps from yellow to brown represented the monitoring rank of the 10 most important weed species from low to high levels for (a) critical or endangered, (b) vulnerable, and (c) relatively stable or intact ecoregions.

et al., 2015; Petitpierre et al., 2017). However, these 10 weed species have wide ecological niches, and therefore, the occurrence records of these species might be sufficient to increase the modelling robustness. Third, more weed species should be considered in the establishment of key monitoring areas on a global scale.

Acknowledgment We thank for the useful comments of the editor and two reviewers on the improvement of our early manuscript. This work has been supported by the National Natural Science Foundation of China (31800449 and 31800464), the Basic Research Project of Qinghai Province, China (2019-ZJ-936Q), and the Proyecto FONDECYT (3180028).

6. Conclusions Appendix A. Supplementary data On a global scale, we determined some specific biomes (i.e., tropical and subtropical biomes, flooded grasslands and savannas, Mediterranean forests, woodlands and scrub, and mangroves) as key monitoring areas for the 10 most important weed species under a changing climate, using conservation prioritization analysis coupled with HSM. Effective management strategies are necessary to prevent and control the distribution of these species in vulnerable ecoregions, particularly in tropical and subtropical grasslands, savannas, and shrublands. Monitoring programs are particularly important in most regions of Latin America, the United States, Europe, central and south Africa, south and southeast Asia, southeast Australia, and New Zealand.

Supplementary data to this article can be found online at https://doi. org/10.1016/j.scitotenv.2019.05.175.

References Adams-Hosking, C., McAlpine, C.A., Rhodes, J.R., Moss, P.T., Grantham, H.S., 2015. Prioritizing regions to conserve a specialist folivore: considering probability of occurrence, food resources, and climate change. Conserv. Lett. 8, 162–170. Akasaka, M., Osawa, T., Ikegami, M., 2015. The role of roads and urban area in occurrence of an ornamental invasive weed: a case of Rudbeckia laciniata L. Urban Ecosyst. 18, 1021–1030.

576

J.-Z. Wan, C.-J. Wang / Science of the Total Environment 683 (2019) 568–577

Allen, J.M., Bradley, B.A., 2016. Out of the weeds? Reduced plant invasion risk with climate change in the continental United States. Biol. Conserv. 203, 306–312. Alvarado-Serrano, D.F., Van Etten, M.L., Chang, S.M., Baucom, R.S., 2019. The relative contribution of natural landscapes and human-mediated factors on the connectivity of a noxious invasive weed. Heredity 122, 29. Anderson, R.P., Gonzalez Jr., I., 2011. Species-specific tuning increases robustness to sampling bias in models of species distributions: an implementation with Maxent. Ecol. Model. 222, 2796–2811. Beaumont, L.J., Pitman, A., Perkins, S., Zimmermann, N.E., Yoccoz, N.G., Thuiller, W., 2011. Impacts of climate change on the world's most exceptional ecoregions. Proc. Natl. Acad. Sci. 108, 2306–2311. Beaumont, L.J., Gallagher, R.V., Leishman, M.R., Hughes, L., Downey, P.O., 2014. How can knowledge of the climate niche inform the weed risk assessment process? A case study of Chrysanthemoides monilifera in Australia. Divers. Distrib. 20, 613–625. Bellard, C., Thuiller, W., Leroy, B., Genovesi, P., Bakkenes, M., Courchamp, F., 2013. Will climate change promote future invasions? Glob. Chang. Biol. 19, 3740–3748. Benvenuti, S., 2004. Weed dynamics in the Mediterranean urban ecosystem: ecology, biodiversity and management. Weed Res. 44, 341–354. Berry, Z.C., Wevill, K., Curran, T.J., 2011. The invasive weed Lantana camara increases fire risk in dry rainforest by altering fuel beds. Weed Res. 51, 525–533. Brown, J.R., Carter, J., 1998. Spatial and temporal patterns of exotic shrub invasion in an Australian tropical grassland. Landsc. Ecol. 13, 93–102. Budiharta, S., Meijaard, E., Wells, J.A., Abram, N.K., Wilson, K.A., 2016. Enhancing feasibility: incorporating a socio-ecological systems framework into restoration planning. Environ. Sci. Pol. 64, 83–92. Catford, J.A., Daehler, C.C., Murphy, H.T., Sheppard, A.W., Hardesty, B.D., Westcott, D.A., Rejmánek, M., Bellingham, P.J., Pergl, J., Horvitz, C.C., Hulme, P.E., 2012. The intermediate disturbance hypothesis and plant invasions: implications for species richness and management. Perspect. Plant Ecol. Evol. S. 14, 231–241. Chamorro, L., Masalles, R.M., Sans, F.X., 2016. Arable weed decline in Northeast Spain: does organic farming recover functional biodiversity? Agric. Ecosyst. Environ. 223, 1–9. Chuong, J., Huxley, J., Spotswood, E.N., Nichols, L., Mariotte, P., Suding, K.N., 2016. Cattle as dispersal vectors of invasive and introduced plants in a California annual grassland. Rangel. Ecol. Manag. 69, 52–58. Clements, D.R., Ditommaso, A., 2011. Climate change and weed adaptation: can evolution of invasive plants lead to greater range expansion than forecasted? Weed Res. 51, 227–240. Dennill, G.B., Donnelly, D., 1991. Biological control of Acacia longifolia and related weed species (Fabaceae) in South Africa. Agric. Ecosyst. Environ. 37, 115–135. Dukes, J.S., Mooney, H.A., 1999. Does global change increase the success of biological invaders? Trends Ecol. Evol. 14, 135–139. Duursma, D.E., Gallagher, R.V., Roger, E., Hughes, L., Downey, P.O., Leishman, M.R., 2013. Next-generation invaders? Hotspots for naturalised sleeper weeds in Australia under future climates. PLoS One 8, e84222. Ens, E., Hutley, L.B., Rossiter-Rachor, N.A., Douglas, M.M., Setterfield, S.A., 2015. Resourceuse efficiency explains grassy weed invasion in a low-resource savanna in North Australia. Front. Plant Sci. 6, 560. Ervin, G.N., Holly, D.C., 2011. Examining local transferability of predictive species distribution models for invasive plants: an example with cogongrass (Imperata cylindrica). Invas. Plant Sci. Mana. 4, 390–401. Estrada, J.A., Flory, S.L., 2015. Cogongrass (Imperata cylindrica) invasions in the US: mechanisms, impacts, and threats to biodiversity. Glob. Ecol. Conserv. 3, 1–10. Flannigan, M.D., Stocks, B.J., Wotton, B.M., 2000. Climate change and forest fires. Sci. Total Environ. 262, 221–229. Gallagher, R.V., Beaumont, L.J., Hughes, L., Leishman, M.R., 2010. Evidence for climatic niche and biome shifts between native and novel ranges in plant species introduced to Australia. J. Ecol. 98, 790–799. García-Roselló, E., Guisande, C., Manjarrés-Hernández, A., González-Dacosta, J., Heine, J., Pelayo-Villamil, P., González-Vilas, L., Vari, R.P., Vaamonde, A., Granado-Lorencio, C., Lobo, J.M., 2015. Can we derive macroecological patterns from primary global biodiversity information facility data? Glob. Ecol. Biogeogr. 24, 335–347. Gentle, C.B., Duggin, J.A., 1997. Lantana camara L. invasions in dry rainforest-open forest ecotones: the role of disturbances associated with fire and cattle grazing. Aust. J. Ecol. 22, 298–306. Grime, J.P., 1988. The CSR Model of Primary Plant Strategies—Origins, Implications and Tests. Plant Evolutionary Biology. Springer, Dordrecht, Netherlands. Habermann, E., San Martin, J.A.B., Contin, D.R., Bossan, V.P., Barboza, A., Braga, M.R., Groppo, M., Martinez, C.A., 2019. Increasing atmospheric CO2 and canopy temperature induces anatomical and physiological changes in leaves of the C4 forage species Panicum maximum. PLoS One 14, e0212506. Hedges, L.V., Gurevitch, J., Curtis, P.S., 1999. The meta-analysis of response ratios in experimental ecology. Ecology 80, 1150–1156. Hellmann, J.J., Byers, J.E., Bierwagen, B.G., Dukes, J.S., 2008. Five potential consequences of climate change for invasive species. Conserv. Biol. 22, 534–543. Hengl, T., de Jesus, J.M., MacMillan, R.A., Batjes, N.H., Heuvelink, G.B., Ribeiro, E., SamuelRosa, A., Kempen, B., Leenaars, J.G.B., Walsh, M., Gonzalez, M.R., 2014. SoilGrids1km— global soil information based on automated mapping. PLoS One 9, e105992. Hitchmough, J., Wagner, M., Ahmad, H., 2017. Extended flowering and high weed resistance within two layer designed perennial “prairie-meadow” vegetation. Urban For. Urban Green. 27, 117–126. Holm, L., 1969. Weeds problems in developing countries. Weed Sci. 17, 113–118. Hulme, P.E., 2017. Climate change and biological invasions: evidence, expectations, and response options. Biol. Rev. 92, 1297–1313. Jenkins, C.N., Pimm, S.L., 2003. How big is the global weed patch. Ann. Mo. Bot. Gard. 172–178.

Johnson, B.J., 1971. Effect of weed competition on sunflowers. Weed Sci. 19, 378–380. Kalusová, V., Chytrý, M., Kartesz, J.T., Nishino, M., Pyšek, P., 2013. Where do they come from and where do they go? European natural habitats as donors of invasive alien plants globally. Divers. Distrib. 19, 199–214. Karger, D.N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R.W., Zimmermann, N.E., Linder, H.P., Kessler, M., 2017. Climatologies at high resolution for the earth's land surface areas. Sci. Data 4, 170122. Korres, N.E., Norsworthy, J.K., Brye, K.R., Skinner Jr., V., Mauromoustakos, A., 2017. Relationships between soil properties and the occurrence of the most agronomically important weed species in the field margins of eastern Arkansas–implications for weed management in field margins. Weed Res. 57, 159–171. Kukkala, A.S., Moilanen, A., 2017. Ecosystem services and connectivity in spatial conservation prioritization. Landsc. Ecol. 32, 5–14. Lajeunesse, M.J., 2015. Bias and correction for the log response ratio in ecological metaanalysis. Ecology 96, 2056–2063. Lehtomäki, J., Moilanen, A., 2013. Methods and workflow for spatial conservation prioritization using zonation. Environ. Model. Softw. 47, 128–137. Leishman, M.R., Thomson, V.P., 2005. Experimental evidence for the effects of additional water, nutrients and physical disturbance on invasive plants in low fertility Hawkesbury sandstone soils, Sydney, Australia. J. Ecol. 93, 38–49. Li, L.H., Liu, H.Y., Lin, Z.S., Jia, J.H., Liu, X., 2017. Identifying priority areas for monitoring the invasion of Solidago Canadensis based on MAXENT and ZONATION. Acta Ecol. Sin. 37, 3124-2132. Liang, L., Clark, J.T., Kong, N., Rieske, L.K., Fei, S., 2014. Spatial analysis facilitates invasive species risk assessment. For. Ecol. Manag. 315, 22–29. Lososová, Z., Chytrý, M., Cimalová, S., Kropáč, Z., Otýpková, Z., Pyšek, P., Tichý, L., 2004. Weed vegetation of arable land in Central Europe: gradients of diversity and species composition. J. Veg. Sci. 15, 415–422. Lowe, S., Browne, M., Boudjelas, S., De Poorter, M., 2000. 100 of the world's Worst Invasive Alien Species: A Selection from the Global Invasive Species Database. Invasive Species Specialist Group, Auckland. MacDonald, G.E., 2004. Cogongrass (Imperata cylindrica)—biology, ecology, and management. Crit. Rev. Plant Sci. 23, 367–380. Merow, C., Smith, M.J., Silander Jr., J.A., 2013. A practical guide to MaxEnt for modeling species' distributions: what it does, and why inputs and settings matter. Ecography 36, 1058–1069. Möhler, H., Diekötter, T., Herrmann, J.D., Donath, T.W., 2018. Allelopathic vs. autotoxic potential of a grassland weed—evidence from a seed germination experiment. Plant Ecolog. Divers. 1–11. Moilanen, A., 2007. Landscape zonation, benefit functions and target-based planning: unifying reserve selection strategies. Biol. Conserv. 134, 571–579. Monaghan, N., 1979. The biology of Johnson grass (Sorghum halepense). Weed Res. 19, 261–267. Moreno-Amat, E., Mateo, R.G., Nieto-Lugilde, D., Morueta-Holme, N., Svenning, J.C., García-Amorena, I., 2015. Impact of model complexity on cross-temporal transferability in Maxent species distribution models: an assessment using paleobotanical data. Ecol. Model. 312, 308–317. Moss, R.H., Edmonds, J.A., Hibbard, K.A., Manning, M.R., Rose, S.K., Van Vuuren, D.P., Carter, T.R., Emori, S., Kainuma, M., Kram, T., Meehl, G.A., Mitchell, J.F.B., Nakicenovic, N., Riahi, K., Smith, S.J., Stouffer, R.J., Thomson, A.M., Weyant, J.P., Wilbanks, T.J., 2010. The next generation of scenarios for climate change research and assessment. Nature 463, 747. Mostert, E., Gaertner, M., Holmes, P.M., O'Farrell, P.J., Richardson, D.M., 2018. A multicriterion approach for prioritizing areas in urban ecosystems for active restoration following invasive plant control. Environ. Manag. 62, 1150–1167. Olson, D.M., Dinerstein, E., 2002. The global 200: priority ecoregions for global conservation. Ann. Mo. Bot. Gard. 199–224. Olson, D.M., Dinerstein, E., Wikramanayake, E.D., Burgess, N.D., Powell, G.V., Underwood, E.C., D'amico, J., Itoua, I., Strand, H., Morrison, J., Loucks, C., Allnutt, T., Ricketts, T., Kura, Y., Lamoreux, J., Wettengel, W., Hedao, P., Kassem, K., 2001. Terrestrial ecoregions of the world: a new map of life on earth. A new global map of terrestrial ecoregions provides an innovative tool for conserving biodiversity. BioScience 51, 933–938. Osunkoya, O.O., Perrett, C., 2011. Lantana camara L.(Verbenaceae) invasion effects on soil physicochemical properties. Biol. Fertil. Soils 47, 349–355. Panetta, F.D., Mitchell, N.D., 1991. Bioclimatic prediction of the potential distributions of some weed species prohibited entry to New Zealand. N. Z. J. Agric. Res. 34, 341–350. Peltzer, D.A., Kurokawa, H., Wardle, D.A., 2016. Soil fertility and disturbance interact to drive contrasting responses of co-occurring native and nonnative species. Ecology 97, 515–529. Petitpierre, B., McDougall, K., Seipel, T., Broennimann, O., Guisan, A., Kueffer, C., 2016. Will climate change increase the risk of plant invasions into mountains? Ecol. Appl. 26, 530–544. Petitpierre, B., Broennimann, O., Kueffer, C., Daehler, C., Guisan, A., 2017. Selecting predictors to maximize the transferability of species distribution models: lessons from cross-continental plant invasions. Glob. Ecol. Biogeogr. 26, 275–287. Phillips, S.J., Dudík, M., 2008. Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography 31, 161–175. Phillips, S.J., Anderson, R.P., Schapire, R.E., 2006. Maximum entropy modeling of species geographic distributions. Ecol. Model. 190, 231–259. Phillips, S.J., Dudík, M., Elith, J., Graham, C.H., Lehmann, A., Leathwick, J., Ferrier, S., 2009. Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data. Ecol. Appl. 19, 181–197. Phillips, S.J., Anderson, R.P., Dudík, M., Schapire, R.E., Blair, M.E., 2017. Opening the black box: an open-source release of Maxent. Ecography 40, 887–893.

J.-Z. Wan, C.-J. Wang / Science of the Total Environment 683 (2019) 568–577 Pickering, C., Mount, A., 2010. Do tourists disperse weed seed? A global review of unintentional human-mediated terrestrial seed dispersal on clothing, vehicles and horses. J. Sustain. Tour. 18, 239–256. Qin, Z., Zhang, J.E., DiTommaso, A., Wang, R.L., Liang, K.M., 2016. Predicting the potential distribution of Lantana camara L. under RCP scenarios using ISI-MIP models. Clim. Chang. 134, 193–208. Radosavljevic, A., Anderson, R.P., 2014. Making better Maxent models of species distributions: complexity, overfitting and evaluation. J. Biogeogr. 41, 629–643. Riahi, K., Rao, S., Krey, V., Cho, C., Chirkov, V., Fischer, G., Kindermann, G., Nakicenovic, N., Rafaj, P., 2011. RCP 8.5—a scenario of comparatively high greenhouse gas emissions. Clim. Chang. 109, 33–57. Rodenburg, J., Demont, M., Zwart, S.J., Bastiaans, L., 2016. Parasitic weed incidence and related economic losses in rice in Africa. Agric. Ecosyst. Environ. 235, 306–317. Rodgers, L., Pernas, T., Redwine, J., Shamblin, B., Bruscia, S., 2018. Multiscale invasive plant monitoring: experiences from the Greater Everglades restoration area. Weed Technol. 32, 11–19. Rouget, M., Hui, C., Renteria, J., Richardson, D.M., Wilson, J.R.U., 2015. Plant invasions as a biogeographical assay: vegetation biomes constrain the distribution of invasive alien species assemblages. S. Afr. J. Bot. 101, 24–31. Roush, M.L., Radosevich, S.R., 1985. Relationships between growth and competitiveness of four annual weeds. J. Appl. Ecol. 895–905. Roxburgh, S.H., Shea, K., Wilson, J.B., 2004. The intermediate disturbance hypothesis: patch dynamics and mechanisms of species coexistence. Ecology 85, 359–371. Sánchez Meador, A., Springer, J.D., Huffman, D.W., Bowker, M.A., Crouse, J.E., 2017. Soil functional responses to ecological restoration treatments in frequent-fire forests of the western United States: a systematic review. Restor. Ecol. 25, 497–508. Sanderson, E.W., Jaiteh, M., Levy, M.A., Redford, K.H., Wannebo, A.V., Woolmer, G., 2002. The human footprint and the last of the wild: the human footprint is a global map of human influence on the land surface, which suggests that human beings are stewards of nature, whether we like it or not. BioScience 52, 891–904. Scheepens, P.C., Müller-Schärer, H., Kempenaar, C., 2001. Opportunities for biological weed control in Europe. BioControl 46, 127–138. Sheil, D., 1999. Tropical forest diversity, environmental change and species augmentation: after the intermediate disturbance hypothesis. J. Veg. Sci. 10, 851–860. Sheppard, C.S., 2013. How does selection of climate variables affect predictions of species distributions? A case study of three new weeds in New Zealand. Weed Res. 53, 259–268. Sheppard, C.S., Burns, B.R., Stanley, M.C., 2016. Future-proofing weed management for the effects of climate change: is New Zealand underestimating the risk of increased plant invasions? N. Z. J. Ecol. 40, 398–405. Spiers, J.A., Oatham, M.P., Rostant, L.V., Farrell, A.D., 2018. Applying species distribution modelling to improving conservation based decisions: a gap analysis of Trinidad and Tobago's endemic vascular plants. Biodivers. Conserv. 27, 2931–2949. Storkey, J., 2004. Modelling seedling growth rates of 18 temperate arable weed species as a function of the environment and plant traits. Ann. Bot-London 93, 681–689.

577

Streit, B., Rieger, S.B., Stamp, P., Richner, W., 2002. The effect of tillage intensity and time of herbicide application on weed communities and populations in maize in Central Europe. Agric. Ecosyst. Environ. 92, 211–224. Thomson, A.M., Calvin, K.V., Smith, S.J., Kyle, G.P., Volke, A., Patel, P., Delgado-Arias, S., Bond-Lamberty, B., Wise, M.A., Clarke, L.E., Edmonds, J.A., 2011. RCP4. 5: a pathway for stabilization of radiative forcing by 2100. Clim. Chang. 109, 77. Thuiller, W., Richardson, D.M., PYŠEK, P., Midgley, G.F., Hughes, G.O., Rouget, M., 2005. Niche-based modelling as a tool for predicting the risk of alien plant invasions at a global scale. Glob. Chang. Biol. 11, 2234–2250. Tscharntke, T., Klein, A.M., Kruess, A., Steffan-Dewenter, I., Thies, C., 2005. Landscape perspectives on agricultural intensification and biodiversity–ecosystem service management. Ecol. Lett. 8, 857–874. Van Auken, O.W., 2000. Shrub invasions of North American semiarid grasslands. Annu. Rev. Ecol. Syst. 31, 197–215. Van Kleunen, M., Weber, E., Fischer, M., 2010. A meta-analysis of trait differences between invasive and non-invasive plant species. Ecol. Lett. 13, 235–245. van Klinken, R.D., Friedel, M.H., 2018. Unassisted invasions: understanding and responding to Australia's high-impact environmental grass weeds. Aust. J. Bot. 65, 678–690. van Wilgen, B.W., Reyers, B., Le Maitre, D.C., Richardson, D.M., Schonegevel, L., 2008. A biome-scale assessment of the impact of invasive alien plants on ecosystem services in South Africa. J. Environ. Manag. 89, 336–349. Wan, J., Wang, C., Han, S., Yu, J., 2014. Planning the priority protected areas of endangered orchid species in northeastern China. Biodivers. Conserv. 23, 1395–1409. Wan, J.Z., Wang, C.J., Yu, F.H., 2017. Wind effects on habitat distributions of winddispersed invasive plants across different biomes on a global scale: assessment using six species. Eco. Inform. 42, 38–45. Wan, J.Z., Zhang, Z.X., Wang, C.J., 2019. Effects of ecoregional vulnerability on habitat suitability of invasive alien plants: an assessment using 13 species on a global scale. Environ. Earth Sci. 78, 180. Wang, H., Su, W., 2013. Evaluating and understanding top of the atmosphere cloud radiative effects in intergovernmental panel on climate change (IPCC) fifth assessment report (AR5) coupled model intercomparison project phase 5 (CMIP5) models using satellite observations. J. Geophys. Res.-Atmos. 118, 683–699. Wilson, P.D., Downey, P.O., Leishman, M., Gallagher, R., Hughes, L., O'Donnell, J., 2009. Weeds in a warmer world: predicting the impact of climate change on Australia's alien plant species using MaxEnt. Plant Prot. Quarterly 24, 84. Zanin, G., Berti, A., Toniolo, L., 1993. Estimation of economic thresholds for weed control in winter wheat. Weed Res. 33, 459–467. Zhang, Z.P., 2003. Development of chemical weed control and integrated weed management in China. Weed Biol. Manag. 3, 197–203. Ziska, L.H., Blumenthal, D.M., Runion, G.B., Hunt, E.R., Diaz-Soltero, H., 2011. Invasive species and climate change: an agronomic perspective. Clim. Chang. 105, 13–42.