Regional scale relationships between ecosystem structure and functioning: the case of the Patagonian steppes

Aims 1. To characterize ecosystem functioning by focusing on above‐ground net primary production (ANPP), and 2. to relate the spatial heterogeneity of both functional and structural attributes of vegetation to environmental factors and landscape structure. We discuss the relationship between vegetation structure and functioning found in Patagonia in terms of the capabilities of remote sensing techniques to monitor and assess desertification. Location Western portion of the Patagonian steppes in Argentina (39°30′ S to 45°27′ S). Methods We used remotely‐sensed data from Landsat TM and AVHRR/NOAA sensors to characterize vegetation structure (physiognomic units) and ecosystem functioning (ANPP and its seasonal and interannual variation). We combined the satellite information with floristic relevés and field estimates of ANPP. We built an empirical relationship between the Landsat TM‐derived normalized difference vegetation index (NDVI) and field ANPP. Using stepwise regressions we explored the relationship between ANPP and both environmental variables (precipitation and temperature surrogates) and structural attributes of the landscape (proportion and diversity of different physiognomic classes (PCs)). Results PCs were quite heterogeneous in floristic terms, probably reflecting degradation processes. Regional estimates of ANPP showed differences of one order of magnitude among physiognomic classes. Fifty percent of the spatial variance in ANPP was accounted for by longitude, reflecting the dependency of ANPP on precipitation. The proportion of prairies and semideserts, latitude and, to a lesser extent, the number of PCs within an 8 × 8 km cell accounted for an additional 33% of the ANPP variability. ANPP spatial heterogeneity (calculated from Landsat TM data) within an 8 × 8 km cell was positively associated with the mean AVHRR/NOAA NDVI and with the diversity of physiognomic classes. Main conclusions Our results suggest that the spatial and temporal patterns of ecosystem functioning described from ANPP result not only from water availability and thermal conditions but also from landscape structure (proportion and diversity of different PCs). The structural classification performed using remotely‐sensed data captured the spatial variability in physiognomy. Such capability will allow the use of spectral classifications to monitor desertification.     

Integrating fluxes from heterogeneous vegetation

The vegetated landscape of Europe has been strongly impacted by human management to produce a heterogeneous patchwork of semi‐natural and agricultural vegetation varying over a wide range of spatial scales. A model is described for averaging vegetation fluxes from a landscape of forest and grassland into the planetary boundary layer (PBL). At a scale of 1 km, model simulations indicate that vegetation heterogeneity exerts little effect on the PBL and regional fluxes will be simple areal averages of the different vegetation types. Above 5 km the model simulates significant effects of different vegetation types on the whole PBL. Averaging fluxes to the regional scale will therefore need to consider explicitly the nature, extent and behaviour of different vegetation types.     

Spatial variability of above‐ground net primary production in Uruguayan grasslands: a remote sensing approach

Question: How does above‐ground net primary production (ANPP) differ (estimated from remotely sensed data) among vegetation units in sub‐humid temperate grasslands? Location: Centre‐north Uruguay. Methods: A vegetation map of the study area was generated from LANDSAT imagery and the landscape configuration described. The functional heterogeneity of mapping units was analysed in terms of the fraction of photosynthetically active radiation absorbed by green vegetation (fPAR), calculated from the normalized difference vegetation index (NDVI) images provided by the moderate resolution imaging spectroradiometer (MODIS) sensor. Finally, the ANPP of each grassland class was estimated using NDVI and climatic data. Results: Supervised classification presented a good overall accuracy and moderate to good average accuracy for grassland classes. Meso‐xerophytic grasslands occupied 45% of the area, Meso‐hydrophytic grasslands 43% and Lithophytic steppes 6%. The landscape was shaped by a matrix of large, unfragmented patches of Meso‐xerophytic and Meso‐hydrophytic grasslands. The region presented the lowest anthropic fragmentation degree reported for the Rio de la Plata grasslands. All grassland units showed bimodal annual fPAR seasonality, with spring and autumn peaks. Meso‐hydrophytic grasslands showed a radiation interception 10% higher than the other units. On an annual basis, Meso‐hydrophytic grasslands produced 3800 kg dry matter (DM) ha^?1 yr^?1 and Meso‐xerophytic grasslands and Lithophytic steppes around 3400 kg·DM·ha^?1·yr^?1. Meso‐xerophytic grasslands had the largest spatial variation during most of the year. The ANPP temporal variation was higher than the fPAR variability. Conclusions: Our results provide valuable information for grazing management (identifying spatial and temporal variations of ANPP) and grassland conservation (identifying the spatial distribution of vegetation units).     

Using Information Theory to Determine Optimum Pixel Size and Shape for Ecological Studies: Aggregating Land Surface Characteristics in Arctic Ecosystems

Quantifying vegetation structure and function is critical for modeling ecological processes, and an emerging challenge is to apply models at multiple spatial scales. Land surface heterogeneity is commonly characterized using rectangular pixels, whose length scale reflects that of remote sensing measurements or ecological models rather than the spatial scales at which vegetation structure and function varies. We investigated the ‘optimum’ pixel size and shape for averaging leaf area index (LAI) measurements in relatively large (85 m² estimates on a 600 × 600-m² grid) and small (0.04 m² measurements on a 40 × 40-m² grid) patches of sub-Arctic tundra near Abisko, Sweden. We define the optimum spatial averaging operator as that which preserves the information content (IC) of measured LAI, as quantified by the normalized Shannon entropy (E _S,n) and Kullback–Leibler divergence (D _KL), with the minimum number of pixels. Based on our criterion, networks of Voronoi polygons created from triangulated irregular networks conditioned on hydrologic and topographic indices are often superior to rectangular shapes for averaging LAI at some, frequently larger, spatial scales. In order to demonstrate the importance of information preservation when upscaling, we apply a simple, validated ecosystem carbon flux model at the landscape level before and after spatial averaging of land surface characteristics. Aggregation errors are minimal due to the approximately linear relationship between flux and LAI, but large errors of approximately 45% accrue if the normalized difference vegetation index (NDVI) is averaged without preserving IC before conversion to LAI due to the nonlinear NDVI-LAI transfer function. Author Contributions:  PS devised and undertook the analyses and wrote the paper. MW devised and implemented the measurement plan, and reviewed the analysis. LS assisted in the spatial data analysis and derivation of the terrain indices. RAB provided the macro-scale dataset. APB generated the DEM from aircraft data. JGE provided meteorological data. MvW generated the micro-scale field data with the help of Lorna Street and Sven Rasmussen. All authors contributed to the text.     

Modelling bird habitat suitability based on landscape parameters at different scales

Habitat suitability as characterized by the presence of a species and by reproductive success was modelled in relation to different parameters of landscape diversity at different scales. Parameters characterizing landscape pattern were determined for the UTM 10 km × 10 km cells covering the whole of Estonia and were correlated to the distribution of over 30 forest bird species. The landscape parameters include areas of lakes, mires and built-up areas, length of borders between different land cover units and length of selected line elements, share of different kinds of forest and peatland. The bird species are grouped by correlation into three major groups: (a) independent, (b) wetland preferring (with subgroups of those avoiding built-up areas and roads and those not) and (c) built-up area dependent (with subgroups depending on the importance of line elements). For selected predator species, nesting success was related to landscape parameters on a finer scale, using cells of 10 km². Land pattern was characterized by total length of line elements (streams, roads, borders, ecotones) and certain areal coverage (forest, mires, fields, built-up areas) within the cell. The impact of variations of food availability was linked to the relation. Possibilities for downscaling and upscaling of relations determined at different scales and areal coverage are discussed.     

Multi-scale species density model for conserving an endangered songbird

Habitat modeling across a landscape that has gradients of habitat conditions requires potential predictor data that can be quantified at biologically relevant scales. We used remotely sensed data to develop a multi-scale density model in 2018 for the golden-cheeked warbler (Setophaga chrysoparia; warbler), a species that breeds in Ashe juniper (Juniperus ashei)-oak (Quercus spp.) woodlands in central Texas, USA. We first classified Ashe juniper and broadleaf tree cover at a 1-m resolution and used this to map potential habitat across the warbler's >67,000-km² breeding range. We then designed a survey for estimating warbler density based on hierarchical distance sampling. We used stratified random sampling to survey for male warblers at 1,804 points across the continuum of tree canopy cover and composition and detected 810 warblers during our surveys. We developed a suite of potential predictor variables for modeling warbler density that reflected vegetation, topography, climate, and anthropogenic land use conditions across the breeding range and developed these at 3 scales representing the territory, site, and landscape. We modeled warbler density and used the best fit model to produce a spatially explicit estimate. Predicted warbler density was influenced by tree canopy cover and canopy height at the territory scale (100-m radius); tree canopy cover, percent of the canopy comprised of juniper, and an interaction between canopy cover and compound topographic index at the site scale (1-km radius); and annual temperature range at the landscape scale (5-km radius). We estimated a population size of 217,444 male warblers (95% CI = 153,917–311,965) and >3,000 males in each recovery unit. After controlling for the duration of point count surveys, our estimate of population size was similar to that reported from the only previous breeding range survey conducted in 2008–2009. Our model results indicated that management activities to increase warbler density should promote woodlands with high tree canopy cover, approximately 60–80% Ashe juniper composition, and tree heights >3 m. In contrast to a patch-based approach, our treatment of habitat variables as continuous helped to credibly map the warbler distribution across areas with broad transitions from woodlands to shrublands. By measuring these predictor variables at biologically relevant scales, we allowed the warbler survey data to define habitat relationships instead of using anthropogenically defined habitat patches. Outcomes from our study show the benefits of developing spatial products tailored to individual species of interest for conservation and management decisions.     

Challenges and opportunities in synthesizing historical geospatial data using statistical models

We classified land cover types from 1940s historical aerial imagery using Object Based Image Analysis (OBIA) and compared these maps with data on recent cover. Few studies have used these kinds of maps to model drivers of cover change, partly due to two statistical challenges: 1) appropriately accounting for spatial autocorrelation and 2) appropriately modeling percent cover which is bounded between 0 and 100 and not normally distributed. We studied the change in woody cover at four sites in California's North Coast using historical (1948) and recent (2009) high spatial resolution imagery. We classified the imagery using eCognition Developer and aggregated the resulting maps to the scale of a Digital Elevation Model (DEM) in order to understand topographic drivers of woody cover change. We used Generalized Additive Models (GAMs) with a quasi-binomial probability distribution to account for spatial autocorrelation and the boundedness of the percent woody cover variable. We explored the relative influences on current percent woody cover of topographic variables (grouped using principal component analysis) reflecting water retention capacity, exposure, and within-site context, as well as historical percent woody cover and geographical coordinates. We estimated these models for pixel sizes of 20, 30, 40, 50, 60, 70, 80, 90, and 100 m, reflecting both tree neighborhood scales and stand scales. We found that historical woody cover had a consistent positive effect on current woody cover, and that the spatial autoregressive term in the model was significant even after controlling for historical cover. Specific topographic variables emerged as important for different sites at different scales, but no overall pattern emerged across sites or scales for any of the topographic variables we tested. This GAM framework for modeling historical data is flexible and could be used with more variables, more flexible relationships with predictor variables, and larger scales. Modeling drivers of woody cover change from historical ecology data sources can be a valuable way to plan restoration and enhance ecological insight into landscape change.     

Assessing the hydrologic impact of historical railroad embankments on wetland vegetation response in Canaan Valley,West Virginia: the value of high‐resolution data

The recovery of natural ecological processes after disturbance is poorly understood. Some disturbances may be so severe as to set ecosystems onto a new trajectory. The Canaan Valley National Wildlife Refuge in West Virginia protects a unique high‐altitude wetland that was heavily disturbed by logging 100 years before present (BP) and has since transitioned to a new ecological state (shrub wetland). Refuge managers interested in preserving and restoring ecosystem states expressed concern about lingering impacts of previous disturbances (logging, railroads, beaver, deer, fire). Available data suggested hydrologic impacts from a remnant railroad grade, but managers had insufficient quantitative data to assess these impacts. We initiated a fine‐scale assessment of topography, vegetation distribution, and hydrology to assess impacts from the remnant rail grade using lidar data, vegetation surveys, and piezometers. We developed topographic models, hydrological models, and mapped vegetation distribution. We developed statistical models to assess relationships between vegetation communities, hydrology, and distance to the rail grade. Surprisingly, we found that hydrologic flow paths did not conform to expectation and were not restricted by remnant land use features. For the most part, vegetation communities are responding to topographic and environmental gradients that existed prior to disturbance. Use of highly detailed topographic data (lidar), field hydrology, and vegetation studies allowed us to more accurately assess hydrologic and vegetation regimes, eliminating the need for mitigation, thus saving significant resources.     

Woody riparian plant distributions in western Oregon,USA: comparing landscape and local scale factors

We studied riparian forests along mountain streams in four large watersheds of western Oregon and far northern California, USA, to better understand the multiscale controls on woody riparian vegetation in a geographically complex region. In each of the four-study watersheds, we sampled woody riparian vegetation in161-ha sampling reaches that straddled the stream channel. Within each hectare, we sampled riparian vegetation and local environmental factors in 40 m² sampling plots arrayed along topographic transects. We also surveyed natural disturbance gaps in 6 ha in each watershed to explore the effects of fine scale disturbance on species distributions. We compared species composition across our study watersheds and used Nonmetric Multidimensional Scaling (NMS) and chi-squared analyses to compare the relative importance of landscape scale climate variables and local topographic and disturbance variables in explaining species distributions at sampling plot and hectare scales. We noted substantial turnover in the riparian flora across the region, with greatest numbers of unique species in watersheds at the ends of the regional gradient. In NMS ordinations at both scales, variation in woody riparian species composition showed strongest correlations with climatic variables and Rubus spectabilis cover, but the latter was only an important factor in the two northern watersheds. At the smaller scale, topographic variables were also important. Chi-squared analyses confirmed that more species showed landscape scale habitat preferences (watershed associations) than associations with topographic position (94.7% vs. 42.7% of species tested) or gap versus forest setting (94.7% vs. 24.6% of species tested). The woody riparian flora of western Oregon shows important biogeographic variation; species distributions showed strong associations with climatic variables, which were the primary correlates of compositional change between riparian sites at both scales analyzed. Additional local variation in composition was explained by measures of topography and disturbance.     

Environmental factors covary with plant diversity–productivity relationships among Chinese grassland sites

Aim Our objective was to document the general relationship between plant species richness (SR) and above‐ground net primary productivity (ANPP) at different spatial scales and the environmental influence on this relationship. Location Temperate and alpine grasslands of China. Methods We investigated SR and ANPP at 321 field sites (1355 plots) across the widely distributed temperate and alpine grasslands of China. Ordinary least squares (OLS) regressions were used to test SR–ANPP relationships among site means. Plot‐level data of SR and ANPP were analysed with general linear models (GLMs) and the correlation between SR and ANPP was decomposed into covariance components to test the influence of climatic variables, region, vegetation type and remaining variation among sites on SR, ANPP and their relationship. Results We found positive linear relationships between SR and ANPP among sites in both the alpine and temperate grassland regions and in different grassland vegetation types of these biomes. Environmental gradients such as growing‐season precipitation affected both SR and ANPP in parallel. However, after removing the among‐site environmental variation, residual SR and ANPP were no longer correlated at the pooled within‐site level. Main conclusions The positive SR–ANPP relationship across large‐scale environmental gradients among sites was most likely the result of climatic variables influencing SR and ANPP in parallel. Our results suggest that in China's natural grasslands there is no direct relationship between SR and ANPP, presumably because the pool of available species for local community assembly is large, in contrast to experiments where species pools are artificially reduced.     

Scaling properties of multivariate landscape structure

Analyses of landscape context is essential for understanding how ecological patterns and processes relate to space. This requires that we quantify variation patterns of different landscape parameters, which may change relative to one another at different spatial scales. Here, we analyzed how statistical relationships of land-use composition parameters changed as a function of extent in 20 real agricultural landscapes. Furthermore, we tested the generality of these scaling relations in numerical simulations using 300 artificial landscapes. We analyzed proportions of artificial habitat types at different extent and compared these patterns with three dominant habitat types in real landscapes (forest, arable land and grassland) at four spatial scales (quadrates of 1–4 km). Both real and simulated landscapes showed that variance of landscape parameters (data differentiation) decreased and their correlations (data consistency) increased as scale increased, thereby suggesting general scaling laws. The potential statistical impact of these scaling relationships is revealed in simultaneous analyses of variation of (local) site parameters of 20 arable fields and their surrounding landscape context. At small and medium extent (quadrates of 1–3 km), variability of local site parameters (e.g. fertilization, pH-value) was high relative to those of landscape parameters. In contrast, at large extent (quadrates of 4 km) variability of landscape parameters was greater than that of site parameters indicating a fundamental shift in the relationship between these sets of parameters at different scales. Hence, it is clear that there is a high risk of artefactual correlations in hierarchical multi-scale landscape analyses when ecological data are related to the landscape context. Accordingly, there is a necessity for multi-scale analyses in landscape ecology to accurately evaluate the relative importance of landscape context at different spatial scales.     

Limited benefits of non-crop vegetation on spiders in Australian vineyards: regional or crop differences?

In crops, invertebrate natural enemies such as spiders have been documented as responding to non-crop vegetation at the local and landscape scales, particularly in northern Europe. Much of this information is based on data from arable or annual crops and it is possible that spider numbers in more persistent perennial systems including vineyards may be less dependent on non-crop vegetation. To test the relationship between spider abundance and non-crop vegetation within the context of Australian vineyards, we sampled spiders in 54 vineyards with adjacent non-crop vegetation, from three different regions. Landscape composition in the area surrounding each of the 54 sites was characterized at 11 spatial scales from 95?m to 3?km radius and spiders were sampled monthly using canopy sticky traps and ground pitfall traps. There were only weak relationships between pasture or woody vegetation and the abundance of spiders in vineyards at all spatial scales. At the local scale, abundance of most spider families tended to be greater in vineyards with adjacent pasture. At the landscape scale there were inconsistent patterns. We discuss possible reasons for these apparent contrasting patterns between perennial and annual crops and European compared to Australian agroecosystems.     

Scale‐dependent occupancy patterns in reptiles across topographically different landscapes

Understanding what factors influence species occupancy in human‐modified landscapes is a central theme in ecology. We examined scale‐dependent habitat relationships and site occupancy in reptiles across three topographically different study areas in south‐eastern Australia. We collected presence–absence data on reptiles from 443 sites associated with three long‐term biodiversity monitoring programs, on four to seven occasions, between 2001 and 2013. We characterised sites by the following four variable domains: 1) field design, 2) topography, 3) local‐scale vegetation attributes and 4) landscape‐scale vegetation cover. We constructed occupancy models for 14 species and used an information‐theoretic approach to compare multiple alternative hypotheses to explain occupancy within and between study areas. We modelled detection probability and used the model with the lowest AIC in subsequent analyses. We then modelled occupancy probability against all subsets of the variable groups (field design, topography, local‐ and landscape‐scale vegetation), as well as a model that held occupancy constant (null model). We found that local‐scale vegetation attributes were important for explaining site occupancy in 12/19 possible models, although, in several cases model fit was improved by the addition of topographic variables or native vegetation cover in the surrounding landscape. Occupancy models for widespread species were broadly congruent across study areas. We demonstrate that topographic variables are important for explaining reptile occupancy in hilly landscapes, and local‐ and landscape‐scale variables are important for explaining reptile occupancy in flat or gently undulating landscapes. Management actions that improve habitat complexity at a site‐level, and encompass entire topographic gradients, will have greater benefit to woodland reptiles than simply increasing vegetation cover in the surrounding landscape.     

Vegetation controls vary across space and spatial scale in a historic grassland‐forest biome boundary

Ecological boundaries are critical landscape regions of transition between adjacent ecological systems. While environmental controls of boundaries may operate in a scale‐dependent manner, multiple‐scale comparisons of vegetation–environment relationships have been characterized for few boundary systems. We used approximately 250 000 point records on the occurrence of woody versus grassland vegetation in conjunction with climatic, topographical, and soils data to evaluate scale effects and spatial heterogeneity in a 650‐km section of the historic prairie–forest biome boundary of Minnesota, USA. We chose this as a model system because of the availability of historical vegetation data, a considerable spatial extent, a sharp ecological transition, and the ability to avoid confounding from more recent anthropogenic land use change. We developed modeling techniques using hierarchical variance partitioning in a spatially‐structured format that allowed us to simultaneously evaluate vegetation–environment relationships across two‐dimensional space (i.e. the prairie‐forest boundary) and across spatial scales (i.e. varying extents). Soils variables displayed the least spatial autocorrelation at shortest lag distances and tended to be the least important predictors of woody vegetation at all spatial extents. Topographical variables displayed greater spatial heterogeneity in regions dominated by forest compared with prairie and were more important at fine‐intermediate spatial scales, highlighting their likely control on fire regimes. An integrated climatic variable (precipitation minus potential evapotranspiration) displayed a trend of increasing spatial variance across the study region and was unambiguously the strongest biome boundary control, although its joint influence with fire was difficult to characterize. Spatially heterogeneous vegetation–environment relationships were observed at all scales, especially at finer scales. Our results suggest that the importance of environmental controls changes smoothly rather than discretely across scales and demonstrate the need to account for spatial non‐stationarity and scale to predict and understand vegetation distribution across ecological boundaries.     

Monitoring shifts in plant diversity in response to climate change: a method for landscapes

Improved sampling designs are needed to detect, monitor, and predict plant migrations and plant diversity changes caused by climate change and other human activities. We propose a methodology based on multi-scale vegetation plots established across forest ecotones which provide baseline data on patterns of plant diversity, invasions of exotic plant species, and plant migrations at landscape scales in Rocky Mountain National Park, Colorado, USA. We established forty two 1000-m² plots in relatively homogeneous forest types and the ecotones between them on 14 vegetation transects. We found that 64% of the variance in understory species distributions at landscape scales were described generally by gradients of elevation and under-canopy solar radiation. Superimposed on broad-scale climatic gradients are small-scale gradients characterized by patches of light, pockets of fertile soil, and zones of high soil moisture. Eighteen of the 42 plots contained at least one exotic species; monitoring exotic plant invasions provides a means to assess changes in native plant diversity and plant migrations. Plant species showed weak affinities to overstory vegetation types, with 43% of the plant species found in three or more vegetation types. Replicate transects along several environmental gradients may provide the means to monitor plant diversity and species migrations at landscape scales because: (1) ecotones may play crucial roles in expanding the geophysiological ranges of many plant species; (2) low affinities of understory species to overstory forest types may predispose vegetation types to be resilient to rapid environmental change; and (3) ecotones may help buffer plant species from extirpation and extinction.     

Effects of spatial heterogeneity on butterfly species richness in Rocky Mountain National Park, CO, USA

We investigated butterfly responses to plot-level characteristics (plant species richness, vegetation height, and range in NDVI [normalized difference vegetation index]) and spatial heterogeneity in topography and landscape patterns (composition and configuration) at multiple spatial scales. Stratified random sampling was used to collect data on butterfly species richness from seventy-six 20 × 50 m plots. The plant species richness and average vegetation height data were collected from 76 modified-Whittaker plots overlaid on 76 butterfly plots. Spatial heterogeneity around sample plots was quantified by measuring topographic variables and landscape metrics at eight spatial extents (radii of 300, 600 to 2,400 m). The number of butterfly species recorded was strongly positively correlated with plant species richness, proportion of shrubland and mean patch size of shrubland. Patterns in butterfly species richness were negatively correlated with other variables including mean patch size, average vegetation height, elevation, and range in NDVI. The best predictive model selected using Akaike’s Information Criterion corrected for small sample size (AIC_c), explained 62% of the variation in butterfly species richness at the 2,100 m spatial extent. Average vegetation height and mean patch size were among the best predictors of butterfly species richness. The models that included plot-level information and topographic variables explained relatively less variation in butterfly species richness, and were improved significantly after including landscape metrics. Our results suggest that spatial heterogeneity greatly influences patterns in butterfly species richness, and that it should be explicitly considered in conservation and management actions.     

多尺度生态系统服务的权衡关系及其对景观配置的响应——以湖北省为例

多种生态系统服务相互关系及其对景观配置响应的多尺度分析一直是生态系统服务研究的热点问题,对于厘清生态系统服务尺度效应和实现区域分级管理具有重要的意义。以湖北省为研究区域,采用遥感、土地利用类型、水文、气象、土壤等多元数据,从5 km×5 km、10 km×10 km、15 km×15 km、20 km×20 km、25 km×25 km、县域和市域等7个递增尺度分析4种生态系统服务(水源供给、碳固持、粮食生产、水质净化)对景观配置的响应关系。结果表明：(1)4种生态系统服务的空间分布具有较强的异质性,粮食生产服务的高值区与水质净化服务的低值区主要分布于湖北省中部和东部的耕地集中区域,碳固持与水源供给呈现相反的空间特征。(2)生态系统服务权衡关系的作用强度随尺度增加而减弱,而协同关系呈增加趋势;同时服务间相互作用强度的变化大于作用方向的变化。(3)景观配置对生态系统服务的影响程度在县域和市域尺度上大于格网尺度。结果反映了受尺度变化中“削峰填谷”过程的影响,景观配置对生态系统服务影响的大小和正负效应具有尺度差异性。研究结果可为区域生态系统服务分级管理和景观格局调控提供科学参考。     

Patterns of species density and productivity at different spatial scales in herbaceous plant communities

A major challenge in evaluating patterns of species richness and productivity involves acquiring data to examine these relationships empirically across a range of ecologically significant spatial scales. In this paper, we use data from herb‐dominated plant communities at six Long‐Term Ecological Research (LTER) sites to examine how the relationship between plant species density and above‐ground net primary productivity (ANPP) differs when the spatial scale of analysis is changed. We quantified this relationship at different spatial scales in which we varied the focus and extent of analysis: (1) among fields within communities, (2) among fields within biomes or biogeographic regions, and (3) among communities within biomes or biogeographic regions. We used species density (D=number of species per m²) as our measure of diversity to have a comparable index across all sites and scales. Although we expected unimodal relationships at all spatial scales, we found that spatial scale influenced the form of the relationship. At the scale of fields within different grassland communities, we detected a significant relationship at only one site (Minnesota old‐fields), and it was negative linear. When we expanded the extent of analyses to biogeographic regions (grasslands or North America), we found significant unimodal relationships in both cases. However, when we combined data to examine patterns among community types within different biogeographic regions (grassland, alpine tundra, arctic tundra, or North America), we did not detect significant relationships between species density and ANPP for any region. The results of our analyses demonstrate that the spatial scale of analysis – how data are aggregated and patterns examined – can influence the form of the relationship between species density and productivity. It also demonstrates the need for data sets from a broad spectrum of sites sampled over a range of scales for examining challenging and controversial ecological hypotheses.     

Plant community dynamics in a semi-arid ecosystem in relation to nutrient addition following a major disturbance

Effects of annual additions of mineral N and P (100 kg ha^–1) on plant species composition and annual aboveground net primary production (ANPP) were investigated during the first three years following disturbance in a semi-arid ecosystem. Additions of N reduced richness of perennial plant species during years 2 and 3, while P reduced the number of perennial species only in year 3. From year 1 to year 2, annual and biennial species richness declined in all treatments while ANPP of annual species increased greatly. Added N increased ANPP of annual species while it decreased ANPP of most perennial species relative to the unfertilized control treatment. Community similarities were higher for the control and native vegetation than for other pairs of treatments using both species presence and plant production data. Nitrogen additions have retarded but not completely arrested secondary succession in this system.     

Environmental and socioeconomic drivers of woody vegetation recovery in a human‐modified landscape in the Rio Grande basin (Colombian Andes)

In the tropics, some agricultural lands are abandoned for economic or technical reasons, leading to the recovery of woody vegetation. Our research aimed to identify the main drivers of spontaneous recovery of vegetation in a basin located in the Colombian Andes. This was done by combining spatially explicit environmental and socioeconomic variables at landscape (e.g. distances to human settlements, to roads, and to forests and mean annual precipitation) and local scales (e.g. depth of the organic layer, soil bulk density, and canopy openness). These variables were measured in 28 temporal plots of 500 m² each, established in land cover transitions that showed the recovery of woody vegetation. The recovered woody vegetation between 1986 and 2012 exhibited a gregarious spatial pattern at the landscape scale. Ordination analysis showed distinct floristic composition among transitions and remnant forests, and species associated to each one through an indicator species analysis. Multivariate analyses revealed the relationship between the transitions and variables at both scales. Woody vegetation recovery occurred near remnant forests and far from human settlements at the landscape scale. The soil conditions (content and depth of the organic horizon and bulk density) were the main drivers at the local scale. Our findings also highlight the necessity to define different restoration approaches such as incorporation of sites where socioeconomic and environmental conditions favor the spontaneous recovery of vegetation into the existing network of protected areas in the region, and to implement active restoration projects in perturbed sites to accelerate the recovery process of Andean forests.