Effect of sampling strategy on estimation of fine‐scale spatial genetic structure in Androsace tapete (Primulaceae), an alpine plant endemic to Qinghai‐Tibetan Plateau

Abstract The fine‐scale spatial genetic structure (SGS) of alpine plants is receiving increasing attention, from which seed and pollen dispersal can be inferred. However, estimation of SGS may depend strongly on the sampling strategy, including the sample size and spatial sampling scheme. Here, we examined the effects of sample size and three spatial schemes, simple‐random, line‐transect, and random‐cluster sampling, on the estimation of SGS in Androsace tapete, an alpine cushion plant endemic to Qinghai‐Tibetan Plateau. Using both real data and simulated data of dominant molecular markers, we show that: (i) SGS is highly sensitive to sample strategy especially when the sample size is small (e.g., below 100); (ii) the commonly used SGS parameter (the intercept of the autocorrelogram) is more susceptible to sample error than a newly developed Sp statistic; and (iii) the random‐cluster scheme is susceptible to obvious bias in parameter estimation even when the sample size is relatively large (e.g., above 200). Overall, the line‐transect scheme is recommendable, in that it performs slightly better than the simple‐random scheme in parameter estimation and is more efficient to encompass broad spatial scales. The consistency between simulated data and real data implies that these findings might hold true in other alpine plants and more species should be examined in future work.     

Optimal sampling strategy for estimation of spatial genetic structure in tree populations

Fine-scale spatial genetic structure (SGS) in natural tree populations is largely a result of restricted pollen and seed dispersal. Understanding the link between limitations to dispersal in gene vectors and SGS is of key interest to biologists and the availability of highly variable molecular markers has facilitated fine-scale analysis of populations. However, estimation of SGS may depend strongly on the type of genetic marker and sampling strategy (of both loci and individuals). To explore sampling limits, we created a model population with simulated distributions of dominant and codominant alleles, resulting from natural regeneration with restricted gene flow. SGS estimates from subsamples (simulating collection and analysis with amplified fragment length polymorphism (AFLP) and microsatellite markers) were correlated with the 'real' estimate (from the full model population). For both marker types, sampling ranges were evident, with lower limits below which estimation was poorly correlated and upper limits above which sampling became inefficient. Lower limits (correlation of 0.9) were 100 individuals, 10 loci for microsatellites and 150 individuals, 100 loci for AFLPs. Upper limits were 200 individuals, five loci for microsatellites and 200 individuals, 100 loci for AFLPs. The limits indicated by simulation were compared with data sets from real species. Instances where sampling effort had been either insufficient or inefficient were identified. The model results should form practical boundaries for studies aiming to detect SGS. However, greater sample sizes will be required in cases where SGS is weaker than for our simulated population, for example, in species with effective pollen/seed dispersal mechanisms.     

Extensive spatial genetic structure revealed by AFLP but not SSR molecular markers in the wind-pollinated tree, Fagus sylvatica

Studies of fine-scale spatial genetic structure (SGS) in wind-pollinated trees have shown that SGS is generally weak and extends over relatively short distances (less than 30-40 m) from individual trees. However, recent simulations have shown that detection of SGS is heavily dependent on both the choice of molecular markers and the strategy used to sample the studied population. Published studies may not always have used sufficient markers and/or individuals for the accurate estimation of SGS. To assess the extent of SGS within a population of the wind-pollinated tree Fagus sylvatica, we genotyped 200 trees at six microsatellite or simple sequence repeat (SSR) loci and 250 amplified fragment length polymorphisms (AFLP) and conducted spatial analyses of pairwise kinship coefficients. We re-sampled our data set over individuals and over loci to determine the effect of reducing the sample size and number of loci used for SGS estimation. We found that SGS estimated from AFLP markers extended nearly four times further than has been estimated before using other molecular markers in this species, indicating a persistent effect of restricted gene flow at small spatial scales. However, our SSR-based estimate was in agreement with other published studies. Spatial genetic structure in F. sylvatica and similar wind-pollinated trees may therefore be substantially larger than has been estimated previously. Although 100-150 AFLP loci and 150-200 individuals appear sufficient for adequately estimating SGS in our analysis, 150-200 individuals and six SSR loci may still be too few to provide a good estimation of SGS in this species.     

Spatial auto-correlation and auto-regressive models estimation from sample survey data

Maximum likelihood estimation of the model parameters for a spatial population based on data collected from a survey sample is usually straightforward when sampling and non-response are both non-informative, since the model can then usually be fitted using the available sample data, and no allowance is necessary for the fact that only a part of the population has been observed. Although for many regression models this naive strategy yields consistent estimates, this is not the case for some models, such as spatial auto-regressive models. In this paper, we show that for a broad class of such models, a maximum marginal likelihood approach that uses both sample and population data leads to more efficient estimates since it uses spatial information from sampled as well as non-sampled units. Extensive simulation experiments based on two well-known data sets are used to assess the impact of the spatial sampling design, the auto-correlation parameter and the sample size on the performance of this approach. When compared to some widely used methods that use only sample data, the results from these experiments show that the maximum marginal likelihood approach is much more precise.     

Spatial genetic structure in continuous and fragmented populations of Pinus pinaster Aiton

Habitat fragmentation, i.e., the reduction of populations into small isolated remnants, is expected to increase spatial genetic structure (SGS) in plant populations through nonrandom mating, lower population densities and potential aggregation of reproductive individuals. We investigated the effects of population size reduction and genetic isolation on SGS in maritime pine ( Pinus pinaster Aiton) using a combined experimental and simulation approach. Maritime pine is a wind-pollinated conifer which has a scattered distribution in the Iberian Peninsula as a result of forest fires and habitat fragmentation. Five highly polymorphic nuclear microsatellites were genotyped in a total of 394 individuals from two population pairs from the Iberian Peninsula, formed by one continuous and one fragmented population each. In agreement with predictions, SGS was significant and stronger in fragments ( Sp  = 0.020 and Sp  = 0.026) than in continuous populations, where significant SGS was detected for one population only ( Sp  = 0.010). Simulations suggested that under fat-tailed dispersal, small population size is a stronger determinant of SGS than genetic isolation, while under normal dispersal, genetic isolation has a stronger effect. SGS was always stronger in real populations than in simulations, except if unrealistically narrow dispersal and/or high variance of reproductive success were modelled (even when accounting for potential overestimation of SGS in real populations as a result of short-distance sampling). This suggests that factors such as nonrandom mating or selection not considered in the simulations were additionally operating on SGS in Iberian maritime pine populations.     

Performance of sampling strategies in the presence of known spatial patterns

In crop protection and ecology accurate and precise estimates of insect populations are required for many purposes. The spatial pattern of the organism sampled, in relation to the sampling scheme adopted, affects the difference between the actual and estimated population density, the bias, and the variability of that estimate, the precision. Field monitoring schemes usually adopt time‐efficient sampling regimes involving contiguous units rather than the most efficient for estimation, the completely random sample. This paper uses spatially‐explicit ecological field data on aphids and beetles to compare common sampling regimes. The random sample was the most accurate method and often the most precise; of the contiguous schemes the line transect was superior to more compact arrangements such as a square block. Bias depended on the relationship between the size and shape of the group of units comprising the sample and the dominant cluster size underlying the spatial pattern. Existing knowledge of spatial pattern to inform the choice of sampling scheme may provide considerable improvements in accuracy. It is recommended to use line transects longer than the grain of the spatial pattern, where grain is defined as the average dimension of clusters over both patches and gaps, and with length at least twice the dominant cluster size.     

Plant dispersal, neighbourhood size and isolation by distance

A theoretical relationship between isolation by distance or spatial genetic structure (SGS) and seed and pollen dispersal is tested using extensive spatial-temporal simulations. Although for animals Wright's neighbourhood size N(e) = 4pisigma(2)(t) has been ascertained also, where sigma(2)(t) is the axial variance of distances between parents and offspring, and it was recently confirmed that N(e) = 4pi(sigma(2)(f) + sigma(2)(m))/2 when dispersal of females and males differ, the situation for plants had not been established. This article shows that for a very wide range of conditions, neighbourhood size defined by Crawford's formula N(e) = 4pi(sigma(2)(s) + sigma(2)(p)/2) fully determines SGS, even when dispersal variances of seed (sigma(2)(s)) and pollen sigma(2)(p)) differ strongly. Further, self-fertilization with rate s acts as zero-distance pollen dispersal, and N(e) = 4pi[sigma(2)(s) + sigma(2)(p)(1 - s)/2] fully determines SGS, for most cases where there are both likely parameter values and substantial SGS. Moreover, for most cases, there is a loglinear relationship, I(1) = 0.587 - 0.117 ln(N(e)), between SGS, as measured by I(1), Moran's coefficient for adjacent individuals, and N(e). However, there are several biologically significant exceptions, namely for very low or large N(e), SGS exceeds the loglinear values. There are also important exceptions to Crawford's formula. First, plants with low seed dispersal, high outcross pollen dispersal and high selfing rate show larger SGS than predicted. Second, in plants with very low (near zero) seed dispersal, selfing decreases SGS, opposite expectations. Finally, in some cases seed dispersal is more critical than pollen dispersal, in a manner inconsistent with Crawford's formula.     

Wide variation in spatial genetic structure between natural populations of the European beech (Fagus sylvatica) and its implications for SGS comparability

Identification and quantification of spatial genetic structure (SGS) within populations remains a central element of understanding population structure at the local scale. Understanding such structure can inform on aspects of the species' biology, such as establishment patterns and gene dispersal distance, in addition to sampling design for genetic resource management and conservation. However, recent work has identified that variation in factors such as sampling methodology, population characteristics and marker system can all lead to significant variation in SGS estimates. Consequently, the extent to which estimates of SGS can be relied on to inform on the biology of a species or differentiate between experimental treatments is open to doubt. Following on from a recent report of unusually extensive SGS when assessed using amplified fragment length polymorphisms in the tree Fagus sylvatica, we explored whether this marker system led to similarly high estimates of SGS extent in other apparently similar populations of this species. In the three populations assessed, SGS extent was even stronger than this previously reported maximum, extending up to 360 m, an increase in up to 800% in comparison with the generally accepted maximum of 30-40 m based on the literature. Within this species, wide variation in SGS estimates exists, whether quantified as SGS intensity, extent or the Sp parameter. Consequently, we argue that greater standardization should be applied in sample design and SGS estimation and highlight five steps that can be taken to maximize the comparability between SGS estimates.     

Unifying conspecific-encounter index v and Moran's I index

Single-species distributional patterns have been well studied and quantified using a negative binomial model. However, it is still unclear how to quantify multi-species distribution patterns. A previous study (Chen et al. 2019) developed a simple conspecific-encounter index for quantifying multi-species distributional aggregation patterns when biodiversity data are collected in a consecutive scheme along line transects. Here, through simple derivation, we reported that the previously proposed conspecific-encounter index had a close association with Moran's I index, which has been widely applied in spatial ecology for describing spatial autocorrelation of ecological data. Our proof unified the conspecific-encounter index that is applied under the context of line-transect sampling and Moran's I index that is applied under the context of spatial ecology. The unification sheds light on the development of new statistical metrics for quantifying biological diversity and distribution patterns when line transects are used in field surveys.     

Associated LAH Numbers,Factorial Series Distributions and Unbiased Estimation of a Size Parameter

A finite population consists of kN individuals of N different categories with k individuals each. It is required to estimate the unknown parameter N, the number of different classes in the population. A sequential sampling scheme is considered in which individuals are sampled until a preassigned number of repetitions of already observed categories occur in the sample. Corresponding fixed sample size schemes were considered by Charalambides (1981). The sequential sampling scheme has the advantage of always allowing unbiased estimation of the size parameter N. It is shown that relative to Charalambides' fixed sample size scheme only minor adjustments are required to account for the sequential scheme. In particular, MVU estimators of parametric functions are expressible in terms of the C-numbers introduced by Charalambides.     

Spatial variability of arbuscular mycorrhizal fungal spores in two natural plant communities

Geostatistical techniques were used to assess the spatial patterns of spores of arbuscular mycorrhizal fungi (AMF) in soils from two contrasting plant communities: a salt marsh containing only arbuscular mycorrhizal and non-mycorrhizal plants in a distinct clumped distribution pattern and a maquis with different types of mycorrhiza where most plants were relatively randomly distributed. Also evaluated was the relationship between the spatial distribution of spores and AM plant distribution and soil properties. A nested sampling scheme was applied in both sites with sample cores taken from nested grids. Spores of AMF and soil characteristics (organic matter and moisture) were quantified in each core, and core sample location was related to plant location. Semivariograms for spore density indicated strong spatial autocorrelation and a patchy distribution within both sites for all AM fungal genera found. However, the patch size differed between the two plant communities and AM fungal genera. In the salt marsh, AM fungal spore distribution was correlated with distance to AM plants and projected stand area of AM plants. In maquis, spatial AM fungal spore distribution was correlated with organic matter. These results suggest that spore distribution of AMF varied between the two plant communities according to plant distribution and soil properties.     

Inferring recruitment history from spatial genetic structure within populations of the colonizing tree Albizia julibrissin (Fabaceae)

Comparative analyses of spatial genetic structure (SGS) among species, populations, or cohorts give insight into the genetic consequences of seed dispersal in plants. We analysed SGS of a weedy tree in populations with known and unknown recruitment histories to first establish patterns in populations with single vs. multiple founders, and then to infer possible recruitment scenarios in populations with unknown histories. We analysed SGS in six populations of the colonizing tree Albizia julibrissin Durazz. (Fabaceae) in Athens, Georgia. Study sites included two large populations with multiple, known founders, two small populations with a single, known founder, and two large populations with unknown recruitment histories. Eleven allozyme loci were used to genotype 1385 individuals. Insights about the effects of colonization history from the SGS analyses were obtained from correlograms and Sp statistics. Distinct differences in patterns of SGS were identified between populations with multiple founders vs. a single founder. We observed significant, positive SGS, which decayed with increasing distance in the populations with multiple colonists, but little to no SGS in populations founded by one colonist. Because relatedness among individuals is estimated relative to a local reference population, which usually consists of those individuals sampled in the study population, SGS in populations with high background relatedness, such as those with a single founder, may be obscured. We performed additional analyses using a regional reference population and, in populations with a single founder, detected significant, positive SGS at all distances, indicating that these populations consist of highly related descendants and receive little seed immigration. Subsequent analyses of SGS in size cohorts in the four large study populations showed significant SGS in both juveniles and adults, probably because of a relative lack of intraspecific demographic thinning. SGS in populations of this colonizing tree is pronounced and persistent and is determined by the number and relatedness of founding individuals and adjacent seed sources. Patterns of SGS in populations with known histories may be used to indirectly infer possible colonization scenarios for populations where it is unknown.     

Effects of zoochory on the spatial genetic structure of plant populations

Spatial genetic structure (SGS) of plants results from the nonrandom distribution of related individuals. SGS provides information on gene flow and spatial patterns of genetic diversity within populations. Seed dispersal creates the spatial template for plant distribution. Thus, in zoochorous plants, dispersal mode and disperser behaviour might have a strong impact on SGS. However, many studies only report the taxonomic group of seed dispersers, without further details. The recent increase in studies on SGS provides the opportunity to review findings and test for the influence of dispersal mode, taxonomic affiliation of dispersers and their behaviour. We compared the proportions of studies with SGS among groups and tested for differences in strength of SGS using Sp statistics. The presence of SGS differed among taxonomic groups, with reduced presence in plants dispersed by birds. Strength of SGS was instead significantly influenced by the behaviour of seed dispersal vectors, with higher SGS in plant species dispersed by animals with behavioural traits that result in short seed dispersal distances. We observed high variance in the strength of SGS in plants dispersed by animals that actively or passively accumulate seeds. Additionally, we found SGS was also affected by pollination and marker type used. Our study highlights the importance of vector behaviour on SGS even in the presence of variance created by other factors. Thus, more detailed information on the behaviour of seed dispersers would contribute to better understand which factors shape the spatial scale of gene flow in animal‐dispersed plant species.     

Sample size under inverse negative binomial group testing for accuracy in parameter estimation

Background

The group testing method has been proposed for the detection and estimation of genetically modified plants (adventitious presence of unwanted transgenic plants, AP). For binary response variables (presence or absence), group testing is efficient when the prevalence is low, so that estimation, detection, and sample size methods have been developed under the binomial model. However, when the event is rare (low prevalence <0.1), and testing occurs sequentially, inverse (negative) binomial pooled sampling may be preferred.

Methodology/Principal Findings

This research proposes three sample size procedures (two computational and one analytic) for estimating prevalence using group testing under inverse (negative) binomial sampling. These methods provide the required number of positive pools (), given a pool size (k), for estimating the proportion of AP plants using the Dorfman model and inverse (negative) binomial sampling. We give real and simulated examples to show how to apply these methods and the proposed sample-size formula. The Monte Carlo method was used to study the coverage and level of assurance achieved by the proposed sample sizes. An R program to create other scenarios is given in Appendix S2.

Conclusions

The three methods ensure precision in the estimated proportion of AP because they guarantee that the width (W) of the confidence interval (CI) will be equal to, or narrower than, the desired width (), with a probability of . With the Monte Carlo study we found that the computational Wald procedure (method 2) produces the more precise sample size (with coverage and assurance levels very close to nominal values) and that the samples size based on the Clopper-Pearson CI (method 1) is conservative (overestimates the sample size); the analytic Wald sample size method we developed (method 3) sometimes underestimated the optimum number of pools.     

Geostatistical approaches and optimal additional sampling schemes for spatial patterns and future sampling of bird diversity

Aim To evaluate geostatistical approaches, namely kriging, co‐kriging and geostatistical simulation, and to develop an optimal sampling design for mapping the spatial patterns of bird diversity, estimating their spatial autocorrelations and selecting additional samples of bird diversity in a 2450 km² basin. Location Taiwan. Methods Kriging, co‐kriging and simulated annealing are applied to estimate and simulate the spatial patterns of bird diversity. In addition, kriging and co‐kriging with a genetic algorithm are used to optimally select further samples to improve the kriging and co‐kriging estimations. The association between bird diversity and elevation, and bird diversity and land cover, is analysed with estimated and simulated maps. Results The Simpson index correlates spatially with the normalized difference vegetation index (NDVI) within the micro‐scale and the macro‐scale in the study basin, but the Shannon diversity index only correlates spatially with NDVI within the micro‐scale. Co‐kriging and simulated annealing simulation accurately simulate the statistical and spatial patterns of bird diversity. The mean estimated diversity and the simulated diversity increase with elevation and decrease with increasing urbanization. The proposed optimal sampling approach selects 43 additional sampling sites with a high spatial estimation variance in bird diversity. Main conclusions Small‐scale variations dominate the total spatial variation of the observed diversity due to a lack of spatial information and insufficient sampling. However, simulations of bird diversity consistently capture the sampling statistics and spatial patterns of the observed bird diversity. The data thus accumulated can be used to understand the spatial patterns of bird diversity associated with different types of land cover and elevation, and to optimize sample selection. Co‐kriging combined with a genetic algorithm yields additional optimal sampling sites, which can be used to augment existing sampling points in future studies of bird diversity.     

Spatial correlations at different spatial scales are themselves highly correlated in isolation by distance processes

Although many properties of spatial autocorrelation statistics are well characterized, virtually nothing is known about possible correlations among values at different spatial scales, which ultimately would influence how inferences about spatial genetics are made at multiple spatial scales. This article reports the results of stochastic space-time simulations of isolation by distance processes, having a very wide range of amounts of dispersal for plants or animals, and analyses of the correlations among Moran's I-statistics for different mutually exclusive distance classes. In general, the stochastic correlations are extremely large (>0.90); however, the correlations bear a complex relationship with level of dispersal, spatial scale and spatial lag between distance classes. The correlations are so large that any existing or conceived statistical method that employs more than one distance class (or spatial scale) should not ignore them. This result also suggests that gains in statistical power via increasing sample size are limited, and that increasing numbers of assayed loci generally should be preferred. To the extent that sampling error for real data sets can be treated as white noise, it should be possible to account for stochastic correlations in formulating more precise statistical methods. Further, while the current results are for isolation by distance processes, they provide some guidance for some more complex stochastic space-time processes of landscape genetics. Moreover, the results hold for several popular measures other than Moran's I. In addition, in the results, the signal to noise ratios strongly decreased with distance, which also has several implications for optimal statistical methods using correlations at multiple spatial scales.     

Alpine Grassland Soil Organic Carbon Stock and Its Uncertainty in the Three Rivers Source Region of the Tibetan Plateau

Alpine grassland of the Tibetan Plateau is an important component of global soil organic carbon (SOC) stocks, but insufficient field observations and large spatial heterogeneity leads to great uncertainty in their estimation. In the Three Rivers Source Region (TRSR), alpine grasslands account for more than 75% of the total area. However, the regional carbon (C) stock estimate and their uncertainty have seldom been tested. Here we quantified the regional SOC stock and its uncertainty using 298 soil profiles surveyed from 35 sites across the TRSR during 2006–2008. We showed that the upper soil (0–30 cm depth) in alpine grasslands of the TRSR stores 2.03 Pg C, with a 95% confidence interval ranging from 1.25 to 2.81 Pg C. Alpine meadow soils comprised 73% (i.e. 1.48 Pg C) of the regional SOC estimate, but had the greatest uncertainty at 51%. The statistical power to detect a deviation of 10% uncertainty in grassland C stock was less than 0.50. The required sample size to detect this deviation at a power of 90% was about 6–7 times more than the number of sample sites surveyed. Comparison of our observed SOC density with the corresponding values from the dataset of Yang et al. indicates that these two datasets are comparable. The combined dataset did not reduce the uncertainty in the estimate of the regional grassland soil C stock. This result could be mainly explained by the underrepresentation of sampling sites in large areas with poor accessibility. Further research to improve the regional SOC stock estimate should optimize sampling strategy by considering the number of samples and their spatial distribution.     

Bayesian Estimation of Hunting Success Rate and Harvest for Spatially Correlated Post‐stratified Data

We consider the estimation of success rate and harvest under post survey stratification at the sub‐domain (county) level. Often in this situation, the population size for the sub‐domain is unknown and the random mechanism that dictates the sample size for sub‐domains is ignored. Finding good estimators of success rate and harvest is very important for wildlife abundance. A Bayesian hierarchical model is developed to estimate both success rate and harvest simultaneously. The model includes a random sub‐domain sample size correlated with the number of successes in the sub‐domain, fixed week effects, random geographic effects, and spatial correlations between neighboring sub‐domains. The computation is done by Gibbs sampling and adaptive rejection sampling techniques. The method developed is illustrated using data from the Missouri Turkey Hunting Survey. The estimation of success rate is improved by treating the the sub‐domain sample size as a random variable instead of a fixed constant. The Bayesian model yields a reasonable harvest estimation. The spatial pattern of the estimated harvest matches the pattern of the check station data.     

The spatial pattern of grassland aboveground biomass on Xizang Plateau and its climatic controls

Aims Grassland is the most widely distributed vegetation type on the Xizang Plateau. Accurate remote sensing estimation of the grassland aboveground biomass (AGB) in this region is influenced by the types of vegetation indexes (VIs) used, the grain size (resolution) of the remote sensing data and the targeted ecosystem features. This study attempts to answer the following questions: (i) Which VI can most accurately reflect the grassland AGB distribution on the Xizang Plateau? (ii) How does the grain size of remote sensing imagery affect AGB reflection? (iii) What is the spatial distribution pattern of the grassland AGB on the plateau and its relationship with the climate?Methods We investigated 90 sample sites and measured site-specific AGBs using the harvest method for three grassland types (alpine meadow, alpine steppe and desert steppe). For each sample site, four VIs, namely, Normalized Difference VI (NDVI), Enhanced VI, Normalized Difference Water Index (NDWI) and Modified Soil-Adjusted VI (MSAVI) were extracted from the Moderate Resolution Imaging Spectroradiometer (MODIS) products with grain sizes of 250 m and 1 km. Linear regression models were employed to identify the best estimator of the AGB for the entire grassland and the three individual grassland types. Paired Wilcoxon tests were applied to assess the grain size effect on the AGB estimation. General linear models were used to quantify the relationships between the spatial distribution of the grassland AGB and climatic factors.Important findings The results showed that the best estimator for the entire grassland AGB on the Xizang Plateau was MSAVI at a 250 m grain size (MSAVI 250 m). For each individual grassland type, the best estimator was MSAVI at a grain size of 250 m for alpine meadow, NDWI at a grain size of 1 km for alpine steppe and NDVI at a grain size of 1 km for desert steppe. The explanation ability of each VI for the grassland AGB did not significantly differ for the two grain sizes. Based on the best fit model (AGB =^-10.80 + 139.13 MSAVI 250 m), the spatial pattern of the grassland AGB on the plateau was characterized. The AGB varied from 1 to 136g m ?2. Approximately 59% of total spatial variation in the AGB for the entire grassland was explained by the combination of the mean annual precipitation (MAP) and mean annual temperature. The explanatory power of MAP was weaker for each individual grassland type than that for the entire grassland. This study illustrated the high efficiency of the VIs derived from MODIS data in the grassland AGB estimation on the Xizang Plateau due to the vegetation homogeneity within a 1×1 km pixel in this region. Furthermore, MAP is a primary driver on the spatial variation of AGB at a regional scale.