Magnitude and direction of green-up date in response to drought depend on background climate over Mongolian grassland

Increasing drought is one major consequence of ongoing global climate change and is expected to cause significant changes in vegetation phenology, especially for naturally vulnerable ecosystems such as grassland. However, the linkage between the response characteristic of green-up date (GUD) to drought and background climate remains largely unknown. Here, we focused on how the GUD of Mongolian grassland responds to extreme drought events (EDE). We first extracted the GUD from the MODIS Enhanced Vegetation Index data during 2001-2020 and identified the preseason EDE using the standardized precipitation evapotranspiration index data. Subsequently, we quantified the response of GUD to preseason EDE (DGUD) in each pixel as the difference in GUD between drought and normal years. The effect of 12 factors on DGUD was analyzed using the random forest algorithm. The results showed that the GUD under EDE may delay or advance by > 20 days compared to normal years. For the regions with mean annual temperature > -2 °C, the GUD was delayed under EDE due to the dominant role of water restriction on GUD, while the GUD was advanced under EDE in colder areas due to the warmer temperature during drought. However, the magnitude of delay in GUD under drought was greater in regions with less precipitation and more severe droughts. Our results could help to develop appropriate management strategies to mitigate the impacts of drought on grasslands.

Testing the hierarchy of predictability in grassland restoration across a gradient of environmental severity

Ecological restoration is critical for recovering degraded ecosystems but is challenged by variable success and low predictability. Understanding which outcomes are more predictable and less variable following restoration can improve restoration effectiveness. Recent theory asserts that the predictability of outcomes would follow an order from most to least predictable from coarse to fine community properties (physical structure > taxonomic diversity > functional composition > taxonomic composition) and that predictability would increase with more severe environmental conditions constraining species establishment. We tested this "hierarchy of predictability" hypothesis by synthesizing outcomes along an aridity gradient with 11 grassland restoration projects across the United States. We used 1829 vegetation monitoring plots from 227 restoration treatments, spread across 52 sites. We fit generalized linear mixed-effects models to predict six indicators of restoration outcomes as a function of restoration characteristics (i.e., seed mixes, disturbance, management actions, time since restoration) and used variance explained by models and model residuals as proxies for restoration predictability. We did not find consistent support for our hypotheses. Physical structure was among the most predictable outcomes when the response variable was relative abundance of grasses, but unpredictable for total canopy cover. Similarly, one dimension of taxonomic composition related to species identities was unpredictable, but another dimension of taxonomic composition indicating whether exotic or native species dominated the community was highly predictable. Taxonomic diversity (i.e., species richness) and functional composition (i.e., mean trait values) were intermittently predictable. Predictability also did not increase consistently with aridity. The dimension of taxonomic composition related to the identity of species in restored communities was more predictable (i.e., smaller residuals) in more arid sites, but functional composition was less predictable (i.e., larger residuals), and other outcomes showed no significant trend. Restoration outcomes were most predictable when they related to variation in dominant species, while those responding to rare species were harder to predict, indicating a potential role of scale in restoration predictability. Overall, our results highlight additional factors that might influence restoration predictability and add support to the importance of continuous monitoring and active management beyond one-time seed addition for successful grassland restoration in the United States.

Simulating highly disturbed vegetation distribution: the case of China's Jing-Jin-Ji region

Background

Simulating vegetation distribution is an effective method for identifying vegetation distribution patterns and trends. The primary goal of this study was to determine the best simulation method for a vegetation in an area that is heavily affected by human disturbance.

Methods

We used climate, topographic, and spectral data as the input variables for four machine learning models (random forest (RF), decision tree (DT), support vector machine (SVM), and maximum likelihood classification (MLC)) on three vegetation classification units (vegetation group (I), vegetation type (II), and formation and subformation (III)) in Jing-Jin-Ji, one of China’s most developed regions. We used a total of 2,789 vegetation points for model training and 974 vegetation points for model assessment.

Results

Our results showed that the RF method was the best of the four models, as it could effectively simulate vegetation distribution in all three classification units. The DT method could only simulate vegetation distribution in units I and II, while the other two models could not simulate vegetation distribution in any of the units. Kappa coefficients indicated that the DT and RF methods had more accurate predictions for units I and II than for unit III. The three vegetation classification units were most affected by six variables: three climate variables (annual mean temperature, mean diurnal range, and annual precipitation), one geospatial variable (slope), and two spectral variables (Mid-infrared ratio of winter vegetation index and brightness index of summer vegetation index). Variables Combination 7, including annual mean temperature, annual precipitation, mean diurnal range and precipitation of driest month, produced the highest simulation accuracy.

Conclusions

We determined that the RF model was the most effective for simulating vegetation distribution in all classification units present in the Jing-Jin-Ji region. The RF model produced high accuracy vegetation distributions in classification units I and II, but relatively low accuracy in classification unit III. Four climate variables were sufficient for vegetation distribution simulation in such region.

Projected Landscape Impacts from Oil and Gas Development Scenarios in the Permian Basin, USA

Projecting landscape impacts from energy development is essential to land management decisions. We forecast landscape alteration resulting from oil and gas well-pad construction across the economically important Permian Basin of Texas and New Mexico, USA, by projecting current landscape trends through 2050. We modeled three landscape-impact scenarios (low, medium, and high) using recent (2008-2017) trends in well-pad construction and energy production. The results of low-, medium-, and high-impact scenarios suggest that ~60,000, ~180,000, and ~430,000 new well pads could be constructed, potentially causing ~1000, ~2800, and ~6700 km² of new direct landscape alteration. Almost two-thirds of all new well pads will be constructed within the geologic boundaries of the Delaware and Midland Basins. This translates into a 40, 120, and 300% increase in direct landscape alteration compared with direct alteration from existing well pads. We found that indirect effects (from edges) could increase by twofold, and that the ratio between indirect and direct alteration could decline by half as alteration intensifies and overlaps with existing alteration. The Chihuahuan Desert occupies the largest portion of the study area, and is projected to experience the largest area of alteration from future well-pad construction in the Permian Basin; the degree of direct alteration could increase by 70, 200, and 500% in this desert region, under low-, medium-, and high-impact scenarios. These scenarios can be used to design proactive conservation strategies to reduce landscape impacts from future oil and gas development.

Long-term loss in extent and current protection of terrestrial ecosystem diversity in the temperate and tropical Americas

Documenting changes in ecosystem extent and protection is essential to understanding status of biodiversity and related ecosystem services and have direct applications to measuring Essential Biodiversity Variables, Targets under the Convention on Biological Diversity (CBD), and IUCN Red List of Ecosystems. We developed both potential and current distribution maps of terrestrial ecosystem types for the temperate and tropical Americas; with "potential" estimating where a type would likely occur today had there not been prior land conversion for modern land uses. We utilized a hierarchical classification to describe and map natural ecosystem types at six levels of thematic detail, with lower thematic levels defining more units each with narrower floristic range than upper levels. Current land use/land cover was derived using available global data on human land use intensity and combined with the potential distribution maps to estimate long-term change in extent for each ecosystem type. We also assessed representation of ecosystem types within protected areas as defined by IUCN I-VI land status categories. Of the 749 ecosystem types assessed, represented at 5th (n = 315) vs. 6th (n = 433) levels of the classification hierarchy, 5 types (1.6%) and 31 types (7.1%), respectively, have lost >90% of their potential extent. Some 66 types (20.9%) and 141 types (32.5%), respectively, have lost >50% of their potential extent; thus, crossing thresholds of Vulnerable status under IUCN Red List criterion A3. For ecosystem type representation within IUCN protected area classes, with reference to potential extent of each type, 111 (45.3%) and 125 (28.8%) of types, respectively, have higher representation (>17%) than CBD 2020 targets. Twelve types (3.8%) and 23 (5.3%) of types, respectively, are represented with <1% within protected areas. We illustrate an option for visualizing and reporting on CBD targets (2020 and proposed post-2020) for ecosystem representativeness using both potential extent as a baseline.

Biodiversity conservation in cities: Defining habitat analogues for plant species of conservation interest

SYNTHESIS AND APPLICATIONS

The stepwise method was useful in producing informative plant lists and assemblages for planting designs and landscape management; it generated a plant selection palette that is not restrictive and does not enforce a native only policy. It also offered a wide range of potential habitat analogues for M. crassifolia.

Multiple axes of ecological vulnerability to climate change

Observed ecological responses to climate change are highly individualistic across species and locations, and understanding the drivers of this variability is essential for management and conservation efforts. While it is clear that differences in exposure, sensitivity, and adaptive capacity all contribute to heterogeneity in climate change vulnerability, predicting these features at macroecological scales remains a critical challenge. We explore multiple drivers of heterogeneous vulnerability across the distributions of 96 vegetation types of the ecologically diverse western US, using data on observed climate trends from 1948 to 2014 to highlight emerging patterns of change. We ask three novel questions about factors potentially shaping vulnerability across the region: (a) How does sensitivity to different climate variables vary geographically and across vegetation classes? (b) How do multivariate climate exposure patterns interact with these sensitivities to shape vulnerability patterns? (c) How different are these vulnerability patterns according to three widely implemented vulnerability paradigms-niche novelty (decline in modeled suitability), temporal novelty (standardized anomaly), and spatial novelty (inbound climate velocity)-each of which uses a distinct frame of reference to quantify climate departure? We propose that considering these three novelty paradigms in combination could help improve our understanding and prediction of heterogeneous climate change responses, and we discuss the distinct climate adaptation strategies connected with different combinations of high and low novelty across the three metrics. Our results reveal a diverse mosaic of climate change vulnerability signatures across the region's plant communities. Each of the above factors contributes strongly to this heterogeneity: climate variable sensitivity exhibits clear patterns across vegetation types, multivariate climate change data reveal highly diverse exposure signatures across locations, and the three novelty paradigms diverge widely in their climate change vulnerability predictions. Together, these results shed light on potential drivers of individualistic climate change responses and may help to inform effective management strategies.

The MODIS Global Vegetation Fractional Cover Product 2001–2018: Characteristics of Vegetation Fractional Cover in Grasslands and Savanna Woodlands

Vegetation Fractional Cover (VFC) is an important global indicator of land cover change, land use practice and landscape, and ecosystem function. In this study, we present the Global Vegetation Fractional Cover Product (GVFCP) and explore the levels and trends in VFC across World Grassland Type (WGT) Ecoregions considering variation associated with Global Livestock Production Systems (GLPS). Long-term average levels and trends in fractional cover of photosynthetic vegetation (FPV), non-photosynthetic vegetation (FNPV), and bare soil (FBS) are mapped, and variation among GLPS types within WGT Divisions and Ecoregions is explored. Analysis also focused on the savanna-woodland WGT Formations. Many WGT Divisions showed wide variation in long-term average VFC and trends in VFC across GLPS types. Results showed large areas of many ecoregions experiencing significant positive and negative trends in VFC. East Africa, Patagonia, and the Mitchell Grasslands of Australia exhibited large areas of negative trends in FNPV and positive trends FBS. These trends may reflect interactions between extended drought, heavy livestock utilization, expanded agriculture, and other land use changes. Compared to previous studies, explicit measurement of FNPV revealed interesting additional information about vegetation cover and trends in many ecoregions. The Australian and Global products are available via the GEOGLAM RAPP (Group on Earth Observations Global Agricultural Monitoring Rangeland and Pasture Productivity) website, and the scientific community is encouraged to utilize the data and contribute to improved validation.

The role of adaptive strategies in plant naturalization

Determining the factors associated with the naturalization of alien species is a central theme in ecology. Here, we tested the usefulness of a metric for quantifying Grime's seminal concept of adaptive strategies - competitors, stress-tolerators and ruderals (CSR) - to explain plant naturalizations worldwide. Using a global dataset of 3004 vascular plant species, and accounting for phylogenetic relatedness and species' native biomes, we assessed the associations between calculated C-, S- and R-scores and naturalization success for species exhibiting different life forms. Across different plant life forms, C-scores were positively and S-scores negatively associated with both the probability of naturalization and the number of regions where the species has naturalized. R-scores had positive effects on the probability of naturalization. These effects of the scores were, however, weak to absent for tree species. Our findings demonstrate the utility of CSR-score calculation to broadly represent, and potentially explain, the naturalization success of plant species.

The native-exotic species richness relationship varies with spatial grain of measurement and environmental conditions

Biological invasions can have dramatic impacts on communities and biodiversity, and are critical considerations in conservation and management decisions. We present a novel analysis to determine how exotic species success varies with community richness and scale of measurement. Using 5,022 plots representing natural vegetation of the Carolinas, we calculated native and exotic species richness of all vascular plants at five grain sizes. To avoid spatial pseudoreplication, we randomly selected unique subplots from each larger plot, re-selecting 100 times to develop an empirical distribution of the native-exotic richness relationship (NERR). Because observed NERRs vary with spatial scale, we developed separate scale-specific null-model distributions to compare to the empirical data. For each spatial scale, we compared the empirical distribution of 100 slopes to the null distribution containing 99 permutations of species origin per empirical slope. We also analyzed the dataset according to broad assignments corresponding to environmental conditions, using the formation type assigned to each community. The plots followed across most scales the general trend that exotic richness increases with native richness. At the smallest scale, however, the NERR was negative. The slope of the NERR is significantly higher than the null model at the largest observed scale and significantly lower than the null model at the smallest two observed scales. The NERR for most formations follows the general pattern with scale for the entire dataset. Warm temperate forests expressed essentially 0 slope at the largest spatial grain, decreasing to a negative relationship at 1 m² and smaller. Temperate freshwater marshes and wet meadows and shrublands expressed a positive relationship at all spatial grains, demonstrating that unique environmental and biogeographic conditions differentially affect exotic species. Further, these results indicate that exotic species are unevenly distributed across natural communities and that community assembly processes vary with scale.

Geographical and climatic gradients of evergreen versus deciduous broad-leaved tree species in subtropical China: Implications for the definition of the mixed forest

Understanding climatic influences on the proportion of evergreen versus deciduous broad-leaved tree species in forests is of crucial importance when predicting the impact of climate change on broad-leaved forests. Here, we quantified the geographical distribution of evergreen versus deciduous broad-leaved tree species in subtropical China. The Relative Importance Value index (RIV) was used to examine regional patterns in tree species dominance and was related to three key climatic variables: mean annual temperature (MAT), minimum temperature of the coldest month (MinT), and mean annual precipitation (MAP). We found the RIV of evergreen species to decrease with latitude at a lapse rate of 10% per degree between 23.5 and 25°N, 1% per degree at 25-29.1°N, and 15% per degree at 29.1-34°N. The RIV of evergreen species increased with: MinT at a lapse rate of 10% per °C between -4.5 and 2.5°C and 2% per °C at 2.5-10.5°C; MAP at a lapse rate of 10% per 100 mm between 900 and 1,600 mm and 4% per 100 mm between 1,600 and 2,250 mm. All selected climatic variables cumulatively explained 71% of the geographical variation in dominance of evergreen and deciduous broad-leaved tree species and the climatic variables, ranked in order of decreasing effects were as follows: MinT > MAP > MAT. We further proposed that the latitudinal limit of evergreen and deciduous broad-leaved mixed forests was 29.1-32°N, corresponding with MAT of 11-18.1°C, MinT of -2.5 to 2.51°C, and MAP of 1,000-1,630 mm. This study is the first quantitative assessment of climatic correlates with the evergreenness and deciduousness of broad-leaved forests in subtropical China and underscores that extreme cold temperature is the most important climatic determinant of evergreen and deciduous broad-leaved tree species' distributions, a finding that confirms earlier qualitative studies. Our findings also offer new insight into the definition and distribution of the mixed forest and an accurate assessment of vulnerability of mixed forests to future climate change.

Comparison modeling for alpine vegetation distribution in an arid area

Mapping and modeling vegetation distribution are fundamental topics in vegetation ecology. With the rise of powerful new statistical techniques and GIS tools, the development of predictive vegetation distribution models has increased rapidly. However, modeling alpine vegetation with high accuracy in arid areas is still a challenge because of the complexity and heterogeneity of the environment. Here, we used a set of 70 variables from ASTER GDEM, WorldClim, and Landsat-8 OLI (land surface albedo and spectral vegetation indices) data with decision tree (DT), maximum likelihood classification (MLC), and random forest (RF) models to discriminate the eight vegetation groups and 19 vegetation formations in the upper reaches of the Heihe River Basin in the Qilian Mountains, northwest China. The combination of variables clearly discriminated vegetation groups but failed to discriminate vegetation formations. Different variable combinations performed differently in each type of model, but the most consistently important parameter in alpine vegetation modeling was elevation. The best RF model was more accurate for vegetation modeling compared with the DT and MLC models for this alpine region, with an overall accuracy of 75 % and a kappa coefficient of 0.64 verified against field point data and an overall accuracy of 65 % and a kappa of 0.52 verified against vegetation map data. The accuracy of regional vegetation modeling differed depending on the variable combinations and models, resulting in different classifications for specific vegetation groups.