Variability Of Vegetation Productivity Research Articles

Hydrologic connectivity drives extremes and high variability in vegetation productivity across Australian arid and semi-arid ecosystems

Vegetation growth drives many of the interactions between the land surface and atmosphere including the uptake of carbon through photosynthesis and loss of water through transpiration. In arid and semi-arid regions water is the dominant driver of vegetation growth. However, few studies consider the fact that water can move laterally across the landscape as runoff via streams and floodplains, termed hydrologic connectivity. Using multiple observations alongside models and a hydromorphology dataset for Australia, we examine how ecosystems with high hydrologic connectivity differ in their vegetation response to water availability, soil properties, and interannual variability and extremes in vegetation productivity. We find that the average interannual variability of vegetation productivity is 21–34% higher in ecosystems with high hydrologic connectivity, with skewed annual anomalies showing larger extremes in carbon uptake. This is driven by a higher average and more variable surface soil moisture and significantly higher soil available water capacity and soil depth. These spatially small ecosystems, covering 14% of the study region, contribute 15–22% (median = 17%) to regional-scale carbon uptake through higher rates of gross photosynthesis, especially evident during wet years, and 3–37% (median = 19%) to annual anomalies. Current global land surface models do not reproduce the observed spatial patterns of interannual variability in carbon uptake over regions where hydrologic connectivity is high as they lack the mechanism of connectivity of water between discrete land surface elements. This study highlights the significant role of riparian and floodplain vegetation on the interannual variability and extremes of the regional carbon cycle.

Time‐Scale Dependent Relations Between Earth Observation Based Proxies of Vegetation Productivity

AbstractMuch uncertainty remains in measuring the inter‐annual and longer‐term dynamics of vegetation gross and net primary productivity (GPP, NPP) and the connected land carbon sink. Potential for better GPP estimation lies in newer satellite products representing different processes or vegetation states, but how they capture interannual GPP dynamics remains to be explored. Here, we differentiate shorter‐ and longer‐term vegetation dynamics and their drivers for several Earth‐observation‐based vegetation proxies and a GPP estimate using time series decomposition. We find that relations between proxies can significantly differ between time scales, along land cover and climate gradients. For GPP estimated at FLUXNET sites, seasonal and multiannual slopes differ by at least 25% for half of the cases investigated, indicating considerable mismatch if multiannual relations were derived from seasonal slopes. Considering time‐scale‐specific sensitivities between proxies of vegetation productivity may improve estimates of interannual variability in vegetation productivity.

Spatio‐temporal effects of inundation and climate on vegetation greenness dynamics in dryland floodplains

AbstractMonitoring floodplain vegetation response to water availability is essential for sustainable water resource management. However, the interpretation of these responses through space and time has been challenging due to the limitation of satellite‐derived inundation extent, which is restricted by the return period of imagery and prevalence of clouds affected images during high flow events. We address this issue by utilising a predictive inundation model capable of reporting inundation at daily temporal resolution. Spatio‐temporal patterns in vegetation greenness of four floodplain vegetation communities (Eucalyptus camaldulensis, Eucalyptus largiflorens, Eucalyptus coolabah and Duma florulenta) were quantified using Landsat‐derived vegetation index and a combination of time series of floodplain inundation pattern, rainfall and soil moisture for the years 1989–2016. The results show that vegetation dynamics were highly variable between years and are closely associated with irregular rainfall patterns and overbank flooding. Linear mixed‐effect models explained between 38% and 67% of the variability in vegetation productivity, with all involved variables being significant predictors (p < 0.05). The effects of rainfall, soil moisture and inundation were diverse and depended on vegetation type. Higher inundation frequency and low interflood period often corresponded to lower vegetation responses, which may relate to a mixed surface water and vegetation signal, or a short‐term inhibitive effect of surface water on growth. These results highlight the importance of groundwater and lagged effects of rainfall and flow in explaining patterns of vegetation condition in semi‐arid floodplains and should be considered in the design of environmental water monitoring programmes.

Is the grass always greener? Land surface phenology reveals differences in peak and season-long vegetation productivity responses to climate and management.

Vegetation phenology—the seasonal timing and duration of vegetative phases—is controlled by spatiotemporally variable contributions of climatic and environmental factors plus additional potential influence from human management. We used land surface phenology derived from the Advanced Very High Resolution Radiometer and climate data to examine variability in vegetation productivity and phenological dates from 1989 to 2014 in the U.S. Northwestern Plains, a region with notable spatial heterogeneity in climate, vegetation, and land use. We first analyzed interannual trends in six phenological measures as a baseline. We then demonstrated how including annual‐resolution predictors can provide more nuanced insights into measures of phenology between plant communities and across the ecoregion. Across the study area, higher annual precipitation increased both peak and season‐long productivity. In contrast, higher mean annual temperatures tended to increase peak productivity but for the majority of the study area decreased season‐long productivity. Annual precipitation and temperature had strong explanatory power for productivity‐related phenology measures but predicted date‐based measures poorly. We found that relationships between climate and phenology varied across the region and among plant communities and that factors such as recovery from disturbance and anthropogenic management also contributed in certain regions. In sum, phenological measures did not respond ubiquitously nor covary in their responses. Nonclimatic dynamics can decouple phenology from climate; therefore, analyses including only interannual trends should not assume climate alone drives patterns. For example, models of areas exhibiting greening or browning should account for climate, anthropogenic influence, and natural disturbances. Investigating multiple aspects of phenology to describe growing‐season dynamics provides a richer understanding of spatiotemporal patterns that can be used for predicting ecosystem responses to future climates and land‐use change. Such understanding allows for clearer interpretation of results for conservation, wildlife, and land management.

Evaluating the Relationships of Inter-Annual Farmland Vegetation Dynamics with Biodiversity Using Multi-Spatial and Multi-Temporal Remote Sensing Data

Agricultural landscapes are highly dynamic ecosystems, but the effects of temporal farmland vegetation dynamics on species diversity have not been widely studied. In 93 sample farm landscapes in eastern Ontario, Canada, biodiversity data for seven taxa were collected in 2011 and 2012, prior to the initiation of this study. The goal of this study was to determine if trends and variability in vegetation productivity detected in these sample landscapes using long-term archived moderate and coarse resolution remote sensing time series data are related to the measured biodiversity. Mid-summer Moderate Resolution Imaging Spectroradiometer (MODIS) (2000–2011) and Landsat 5 (1985–2011) Normalized Difference Vegetation Index (NDVI) data were used with the Thiel–Sen slope and Contextual Mann–Kendall trend analysis to identify pixels showing significant trends. NDVI temporal metrics included 1) the percentage of pixels in each landscape with a significant negative or positive trend, and 2) the temporal coefficient of variation (CV) of both the mean and spatial CV of landscape NDVI. Larger areas of significant positive NDVI trends were found in the sample landscapes than negative trends, the former being associated with agricultural intensification or crop changes and the latter with smaller areas of natural vegetation removal. Landsat better-detected changes in individual fields or small areas of natural vegetation due to its much smaller pixel size. In addition, the longer Landsat time series showed a change in the NDVI trend from positive (1985–2000) to negative or a leveling off (2000–2011) for many pixels. In biodiversity modeling, the Landsat temporal CV of NDVI was negatively correlated with 2011–2012 plant and beetle diversity, while plant biodiversity was positively correlated with the percentage of pixels in a sample landscape showing a significantly positive NDVI trend. No significant relationships were found using the MODIS data. This study shows that temporal trends and variability in farmland vegetation density derived from Landsat data are related to biodiversity for certain taxa and that such relationships should be considered along with the more commonly studied spatial landscape attributes in evaluating landscape-level impacts of farming on biodiversity.

Long-term variability in vegetation productivity in relation to rainfall, herbivory and fire in Tswalu Kalahari Reserve

Exploring the long-term influence of climate and land use on vegetation change allows for a more robust understanding of how vegetation is likely to respond in the future. To inform management, this study investigated the relationship between vegetation productivity trends and potential drivers of change in the 110 000 ha of the Tswalu Kalahari Reserve between 2000 and 2015, using the Moderate Resolution Imaging Spectroradiometer Enhanced Vegetation Index (EVI, MOD13Q1). Spatio-temporal variability of the EVI was mapped and then related to the historical records of precipitation, animal numbers and fire occurrences. Long-term trends in productivity were analysed by residual trend analysis (RESTREND). Significantly different EVI profiles were found between vegetation types, and this was related to the structure and function of the vegetation, as well as the effects of soil reflectance. The EVI time-series signalled spatial and temporal heterogeneity in plant productivity, which was strongly correlated with rainfall, although fire and especially herbivory had noteworthy localised effects on productivity. The RESTREND identified a significant positive trend in plant productivity in shrub-dominated vegetation types, providing evidence for the ongoing thickening of woody species. Significant negative trends in productivity were associated with artificial water points and more heavily stocked areas, leading to degradation.Conservation implications: The southern Kalahari has a highly variable rainfall regime, which is tied to a dynamic vegetation response. This variability should be taken into account when making management decisions. Field-based monitoring together with adaptive management approaches are needed in the face of an uncertain future in which significant warming is expected.

Characterizing Drought Effects on Vegetation Productivity in the Four Corners Region of the US Southwest

The droughts striking the Colorado Plateau, where the Hopi Tribe and Navajo Nation Native American reservation lands are located, and their impacts have appeared slowly and relatively unnoticed in conventional national drought monitoring efforts like the National Drought Monitor. To understand the effect of drought-based drivers on vegetation productivity in the Hopi Tribe and Navajo Nation reservation lands, an assessment approach was developed integrating climate, land cover types, and topographical data with annual geospatially explicit normalized difference vegetation index (NDVI)-related productivity from 1989 to 2014 derived from 15-day composite multi-sensor NDVI time series data. We studied vegetation–environment relationships by conducting multiple linear regression analysis to explain the driver of vegetation productivity changes. Our results suggest that the interannual change of vegetation productivity showed high variability in middle elevations where needleleaf forest is the dominant vegetation cover type. Our analysis also shows that the spatial variation in interannual variability of vegetation productivity was more driven by climate drivers than by topography ones. Specifically, the interannual variability in spring precipitation and fall temperature seems to be the most significant factor that correlated with the interannual variability in vegetation productivity during the last two and a half decades.

The Dominance of Introduced Plant Species in the Diets of Migratory Galapagos Tortoises Increases with Elevation on a Human‐Occupied Island

AbstractThe distribution of resources and food selection are fundamental to the ecology, life history, physiology, population dynamics, and conservation of animals. Introduced plants are changing foraging dynamics of herbivores in many ecosystems often with unknown consequences. Galapagos tortoises, like many herbivores, undertake migrations along elevation gradients driven by variability in vegetation productivity which take them into upland areas dominated by introduced plants. We sought to characterize diet composition of two species of Galapagos tortoises, focussing on how the role of introduced forage species changes over space and the implications for tortoise conservation. We quantified the distribution of tortoises with elevation using GPS telemetry. Along the elevation gradient, we quantified the abundance of introduced and native plant species, estimated diet composition by recording foods consumed by tortoises, and assessed tortoise physical condition from body weights and blood parameter values. Tortoises ranged between 0 and 429 m in elevation over which they consumed at least 64 plant species from 26 families, 44 percent of which were introduced species. Cover of introduced species and the proportion of introduced species in tortoise diets increased with elevation. Introduced species were positively selected for by tortoises at all elevations. Tortoise physical condition was either consistent or increased with elevation at the least biologically productive season on Galapagos. Santa Cruz tortoises are generalist herbivores that have adapted their feeding behavior to consume many introduced plant species that has likely made a positive contribution to tortoise nutrition. Some transformed habitats that contain an abundance of introduced forage species are compatible with tortoise conservation.

How landscape dynamics link individual- to population-level movement patterns: a multispecies comparison of ungulate relocation data

Aim To demonstrate how the interrelations of individual movements form large-scale population-level movement patterns and how these patterns are associated with the underlying landscape dynamics by comparing ungulate movements across species. Locations Arctic tundra in Alaska and Canada, temperate forests in Massachusetts, Patagonian Steppes in Argentina, Eastern Steppes in Mongolia. Methods We used relocation data from four ungulate species (barren-ground caribou, Mongolian gazelle, guanaco and moose) to examine individual movements and the interrelation of movements among individuals. We applied and developed a suite of spatial metrics that measure variation in movement among individuals as population dispersion, movement coordination and realized mobility. Taken together, these metrics allowed us to quantify and distinguish among different large-scale population-level movement patterns such as migration, range residency and nomadism. We then related the population-level movement patterns to the underlying landscape vegetation dynamics via long-term remote sensing measurements of the temporal variability, spatial variability and unpredictability of vegetation productivity. Results Moose, which remained in sedentary home ranges, and guanacos, which were partially migratory, exhibited relatively short annual movements associated with landscapes having very little broad-scale variability in vegetation. Caribou and gazelle performed extreme long-distance movements that were associated with broad-scale variability in vegetation productivity during the peak of the growing season. Caribou exhibited regular seasonal migration in which individuals were clustered for most of the year and exhibited coordinated movements. In contrast, gazelle were nomadic, as individuals were independently distributed and moved in an uncoordinated manner that relates to the comparatively unpredictable (yet broad-scale) vegetation dynamics of their landscape. Main conclusions We show how broad-scale landscape unpredictability may lead to nomadism, an understudied type of long-distance movement. In contrast to classical migration where landscapes may vary at broad scales but in a predictable manner, long-distance movements of nomadic individuals are uncoordinated and independent from other such individuals. Landscapes with little broad-scale variability in vegetation productivity feature smaller-scale movements and allow for range residency. Nomadism requires distinct integrative conservation strategies that facilitate long-distance movements across the entire landscape and are not limited to certain migration corridors.

Analysing the vegetation cover variation of China from AVHRR‐NDVI data

In this paper, the characteristics of vegetation cover and variation in China have been studied by using the AVHRR NDVI time‐series data from 1981 to 2001. The Harmonic Analysis of Time Series (HANTS) method was successfully applied to eliminate the clouds on remote sensing data and reconstruct cloud‐free time series images. Then, the Fourier components of NDVI time series data were calculated. Finally, the physical meaning of Fourier components was analysed, and the relationship between Fourier components and land vegetation cover variation was investigated. The mean NDVI, or zeroth‐order harmonic, indicates overall vegetation cover level. The first harmonics of the HANTS summarizes the amplitude and phase of annual values of NDVI data, and the second harmonics of the HANTS summarizes those of biannual values of NDVI data. The amplitude of the first harmonic indicates the variability of vegetation productivity over the year. The phase of the first harmonic summarizes the timing of vegetation green‐up, while the second harmonic indicates the strength and timing of biannual vegetation cover variation. The Fourier components calculated by HANTS algorithm reveal the vegetation distribution and growing cycle characteristics. The physical meaning of Fourier components are significant to the land‐surface vegetation variation study of China. The methodology proposed in this paper is an effective method for the processing, analysis and application of long‐time‐series remote sensing data.

Spring Thaw and Its Effect on Terrestrial Vegetation Productivity in the Western Arctic Observed from Satellite Microwave and Optical Remote Sensing

Abstract Global satellite remote sensing records show evidence of recent vegetation greening and an advancing growing season at high latitudes. Satellite remote sensing–derived measures of photosynthetic leaf area index (LAI) and vegetation gross and net primary productivity (GPP and NPP) from the NOAA Advanced Very High Resolution Radiometer (AVHRR) Pathfinder record are utilized to assess annual variability in vegetation productivity for Alaska and northwest Canada in association with the Western Arctic Linkage Experiment (WALE). These results are compared with satellite microwave remote sensing measurements of springtime thaw from the Special Sensor Microwave Imager (SSM/I). The SSM/I-derived timing of the primary springtime thaw event was well correlated with annual anomalies in maximum LAI in spring and summer (P ≤ 0.009; n = 13), and GPP and NPP (P ≤ 0.0002) for the region. Mean annual variability in springtime thaw was on the order of ±7 days, with corresponding impacts to annual productivity of approximately 1% day−1. Years with relatively early seasonal thawing showed generally greater LAI and annual productivity, while years with delayed seasonal thawing showed corresponding reductions in canopy cover and productivity. The apparent sensitivity of LAI and vegetation productivity to springtime thaw indicates that a recent advance in the seasonal thaw cycle and associated lengthening of the potential period of photosynthesis in spring is sufficient to account for the sign and magnitude of an estimated positive vegetation productivity trend for the western Arctic from 1982 to 2000.

Sub‐saharan desertification and productivity are linked to hemispheric climate variability

SummaryVegetation productivity and desertification in sub‐Saharan Africa may be influenced by global climate variability attributable to the North Atlantic Oscillation (NAO) and El Niño Southern Oscillation (ENSO). Combined and individual effects of the NAO and ENSO indices revealed that 75% of the interannual variation in the area of Sahara Desert was accounted for by the combined effects, with most variance attributable to the NAO. Effects were shown in the latitudinal variation on the 200 mm isocline, which was influenced mostly by the NAO. The combined indices explained much of the interannual variability in vegetation productivity in the Sahelian zone and southern Africa, implying that both the NAO and ENSO may be useful for monitoring effects of global climate change in sub‐Saharan Africa.