Identification and siting of native vegetation types on disturbed land: demonstration of statistical methods

Question: How does one best choose native vegetation types and site them in reclamation of disturbed sites ranging from cropland and strip mines? Application: World‐wide, demonstrated in SE Montana. Methods: We assumed that pre‐disturbance native communities are the best targets for revegetation, and that the environmental facet each occupies naturally provides its optimal habitat. Given this assumption, we used pre‐strip‐mine data (800 points from a 88 km² site) to demonstrate statistical methods for identifying native communities, describing them, and determining their environments. Results and conclusions: Classification and pruning analysis provided an objective method for choosing the number of target community types to be used in reclamation. The composition of eight target types, identified with these analyses, was described with a relevé table to provide a species list, target cover levels and support the choice of species to be seeded. As a basis for siting communities, we modeled community presence as a function of topography, slope/aspect, and substrate. Logistic GLMs identified the optimal environment for each community. Classification and Regression Tree (CART) analysis identified the most probable community in each environmental facet. Topography and slope were generally the best predictors in these models. Because our analyses relate native vegetation to undisturbed environments, our results may apply best to sites with minimal substrate disturbance (i.e. better to abandoned cropland than to strip‐mined sites).     

Assessing the effect of prevalence on the predictive performance of species distribution models using simulated data

Aim The proportion of sampled sites where a species is present is known as prevalence. Empirical studies have shown that prevalence can affect the predictive performance of species distribution models. This paper uses simulated species data to examine how prevalence and the form of species environmental dependence affect the assessment of the predictive performance of models. Methods Simulated species data were based on various functions of simulated environmental data with differing degrees of spatial correlation. Seven model performance measures – sensitivity, specificity, class‐average (CA), overall prediction success, kappa (κ), normalized mutual information (NMI) and area under the receiver operating characteristic curve (AUC) – were applied to species models fitted by three regression methods. The response of the performance measures to prevalence was then assessed. Three probability threshold selection methods used to convert fitted logistic model values to presence or absence were also assessed. Results The study shows that the extent to which prevalence affects model performance depends on the modelling technique and its degree of success in capturing dominant environmental determinants. It also depends on the statistic used to measure model performance and the probability threshold method. The response based on κ generally preferred models with medium prevalence. All performance measures were least affected by prevalence when the probability threshold was chosen to maximize predictive performance or was based directly on prevalence. In these cases, the responses based on AUC, CA and NMI generally preferred models with small or large prevalence. Main conclusions The effect of prevalence on the predictive performance of species distribution models has a methodological basis. Relevant factors include the success of the fitted distribution model in capturing the dominant environmental determinant, the model performance measure and the probability threshold selection method. The fixed probability threshold method yields a marked response of model performance to prevalence and is therefore not recommended. The study explains previous empirical results obtained with real data.     

Temporal changes in species diversity and composition in abandoned fields in a <Emphasis Type="Italic">trans</Emphasis>-Himalayan landscape,Nepal

Secondary succession is an increasing phenomenon due to global changes in agriculture policies and practices. The empirical findings are biased towards the temperate zone. Abandonment of agriculture fields is less frequent in the subtropical and tropical zones where agriculture areas are, in general, expanding. But there are exceptions; a rapid rate of abandonment of agricultural fields have taken place in the arid trans-Himalayan region, due to today’s globalization of economy. We analysed agriculture fields that were abandoned between 1950 and 2003 in a large u-valley in central Nepal (3400 m a.s.l.). The potential forest vegetation is dominated by Pinus wallichina and shrubs of junipers and cotoneaster species. We tested the intermediate richness hypothesis in relation to vegetation cover, soil development and whether old-field succession is convergent or divergent with species data from 242 1 m² plots in 5 age-classes. The main species compositional turnover expressed by Detrended Correspondence Analyses (DCA) correlated, as expected, with time after abandonment. Fields that were abandoned a long time ago are closer to forest at the periphery of the agricultural landscape. Moisture of the soil significantly increased with age of abandonment, but total vegetation cover and pH were negatively related to age. Beta diversity expressed in DCA SD-units showed an increasing trend with age of abandonment, supporting the divergence pattern in old-field succession. The reason why the succession is not converging may be due to browsing by domestic animals that prevent a closed canopy of pines and juniper to develop. There was a significant hump-shaped pattern in species richness along the temporal gradient, which agrees with the intermediate species-richness hypothesis. There was a rapid increase in species richness in plots close to the villages that were used for haymaking which increased the seed input significantly.     

SNV and haplotype analysis reveals new CSRP1 variants associated with growth and carcass traits

The cysteine and glycine-rich protein 1 and 2 genes (CSRP1 and CSRP2) are an effective growth factor in promoting skeletal muscle growth in vitro and vivo. However, in cattle, the information on the CSRP1 and CSRP2 genes is very limited. The aim of this study was to examine the association of the CSRP1 and CSRP2 variants with growth and carcass traits in cattle breeds. Three single nucleotide variants (SNVs) were identified within the bovine CSRP1 gene, whereas CSRP2 gene has not detected any SNVs, using DNA pooled sequencing, PCR-RFLP, and forced PCR-RFLP methods. These SNVs include g. 801T>C (Intron 2), g. 46T>C (Exon 3) and g. 99C>G (Intron 3). Besides, we also investigated haplotype frequencies and linkage disequilibrium (LD) coefficients for three SNVs in all study populations. LD and haplotype structure of CSRP1 were different between breeds. The result of haplotype analysis demonstrated eight haplotype present in QC (Qinchuan) and one haplotype in CH (Chinese Holstein). Only haplotype 1 (TTC), shared by all two populations, comprised 10.74% and 100.00%, of all haplotypes observed in QC and CH, respectively. Haplotype 5 (CTC) had the highest haplotype frequencies in QC (30.98%) and haplotype 1 had the highest haplotype frequencies in CH (100.00%). The statistical analyses indicated that one single SNV and 19 combined haplotypes were significantly or highly significantly associated with growth and carcass traits in the QC cattle population (P < 0.05 or P < 0.01). Quantitative real-time PCR (qRT-PCR) analyses showed that the bovine CSRP1 and CSRP2 genes were widely expressed in many tissues. The results of this study suggest that the CSRP1 gene possibly is a strong candidate gene that affects growth and carcass traits in the Chinese beef cattle breeding.     

Assessing plant-nutrient relationships in highly invaded Californian grasslands using non-normal probability distributions

Question: Is native species occurrence related to soil nutrients in highly invaded Californian annual grasslands? What is the best method to analyze this relationship, given that native species occur in very low numbers and are absent from many locations? Location: California, USA. Methods: We investigated the effects of soil characteristics and livestock grazing on native plant occurrence at 40 plots from six sites during the period 2003–2005. Low absolute cover (< 5.8%) of native species resulted in strongly skewed, zero-inflated data sets. To overcome problems in the analysis created by non-normality and correlations within plots, we used GLMs and GLMMs, either with a Poisson or a negative binomial distribution, to analyse native species richness and Nassella pulchra cover. Results: N. pulchra cover was strongly associated with low phosphorus in sandy soils, whereas native species richness was highest in soils with low available nitrogen (high C:N). Conclusion: Under current conditions, phosphorus seems to be a critical factor influencing abundance of N. pulchra. Low fertility soils may provide refugia for native species in highly invaded California grasslands as they may be below a threshold required for non-native annuals to completely dominate. By using non-normal distributions in linear models with random components, we report well fitted models with more accurately tested significant covariates.     

Dealing with uncertain absences in habitat modelling: a case study of a rare ground-dwelling parrot

In the development of a species distribution model based on regression techniques such as generalized linear or additive modelling (GLM/GAM), a basic assumption is that records of species presence and absence are real. However, a common concern in many studies examining species distributions is that absences cannot be inferred with certainty. This is particularly the case where the species is rare, difficult to detect and/or does not occupy all available habitat considered suitable. The western ground parrot ( Pezoporus wallicus flaviventris ) of southern Western Australia, Australia, is a case in point, as not only is it rare and difficult to detect, but it is also unlikely to occupy all available suitable habitat. A recent survey of ground parrots provided the opportunity to develop a predictive distribution model. As the data were susceptible to false absences, these were replaced with randomly selected 'pseudo' absences and modelled using GLM. As a comparison, presence-only information was modelled using a relatively new approach, MAXENT, a machine-learning technique that has been shown to perform comparatively well. The predictive performance of both models, as assessed by the receiver operating characteristic plot (ROC) was high (AUC > 0.8), with MAXENT performing only marginally better than the GLM. These approaches both indicated that the ground parrot prefers areas relatively high in altitude, distant from rivers, gently sloping to level habitat, with an intermediate cover of vegetation and where there is a mosaic of vegetation ages. In this case, the use of presence-only information resulted in the identification of important environmental attributes defining the occurrence of the ground parrot, but additional factors that account for the inability of the bird to occupy all suitable habitat should be a component of model refinement.     

海南主要陆域自然保护地兰科植物多样性与生境的关联分析

为了掌握海南主要陆域自然保护地内野生兰科植物的物种多样性现状以及制约其发展的关键生境因子,对该地区进行兰科植物资源调查并分析兰科植物多样性的空间分布格局,进一步采用典范对应分析(CCA)探索生境因子对兰科植物组成的影响,最后运用广义线性模型(GLM)框架下的负二项回归拟合兰科植物丰富度和多度对生境变异的响应。结果表明:(1)共发现兰科植物67属193种,为海南兰科植物分布的绝对中心。(2)水平方向上,霸王岭兰科植物丰富度高但居群相对拥挤,而五指山最大的海拔落差带来了更加多样化的小生境类型和宽阔的生存空间,孕育了种类丰富且分布均匀的兰科植物资源。(3)垂直方向上,中海拔地区兰科植物种类最为丰富且种间竞争较为激烈,高海拔地区则存在明显的优势类群。(4)海拔变化对兰科植物物种组成变异有着非常高的解释率,而喀斯特和河谷地貌的显著影响也不容忽视。(5)多因子综合作用共同影响着兰科植物的多样性,其中坡度、河谷地貌、喀斯特地貌的显著正效应和枫香林的显著负效应受其他协变量的影响较小,是驱动兰科植物丰富度和多度变化的关键生境因子。综上所述,中高海拔地区以及特殊地貌(如河谷和喀斯特地貌)应作为兰科植物多样性的优先保护区域。