Multi-scale comparisons of cliff vegetation in Colorado

We developed a nested vegetation sampling protocol to sample the plant diversity on south-facing cliffs and cliff bases in Jefferson County, Colorado. The multi-scale plots included three nested spatial scales, 1 m², 20 m², and 40 m². We compared plant species richness and species diversity among large cliffs, medium cliffs, small cliffs, and non-cliff sites using Hill's diversity numbers (N ₀, N ₁, and N ₂) for the 1-m² quadrats. Species richness (N ₀) was calculated for the 20-m² and 40-m² plots. Our results indicate that plant species diversity on the cliff faces did not increase with increasing cliff area. This pattern was consistent at all three sampling scales. A model selection was run to determine if plant species diversity values on the cliff faces were associated with cliff variables. None of the cliff variables measured were good predictors of diversity at the 1-m² scale. However, at the 20-m² scale, canyon differences and a positive relationship with increasing cliff surface roughness explained 70% of the variability in species richness. Although most plant species sampled on the cliff faces were also found in the base plots, 13 species were sampled only on the cliff faces. Additionally, dry south facing cliffs support a mix of xeric and mesic plants indicating that cliffs may provide unique microenvironments for plant establishment. This revised version was published online in August 2006 with corrections to the Cover Date.     

害虫区域性生态调控的理论、方法及实践

本文在分析害虫生态调控的生态学基础上 ,论述了害虫区域性生态调控的原理与方法 ,并以华北棉田害虫管理实践为例 ,介绍了害虫区域性生态调控的实施过程     

Mosquito community composition and abundance at contrasting sites in northern South Africa, 2014–2017

Most data on species associations and vector potential of mosquitoes in relation to arboviral infections in South Africa date back from the 1940s to late 1990s. Contextual information crucial for disease risk management and control, such as the sampling effort, diversity, abundance, and distribution of mosquitoes in large parts of South Africa still remains limited. Adult mosquitoes were collected routinely from two horse farms in Gauteng Province; two wildlife reserves in Limpopo Province, at Orpen Gate in Kruger National Park (KNP) and Mnisi Area in Mpumalanga Province between 2014–2017, using carbon dioxide‐baited light and tent traps. Mosquito diversity and richness are greater in untransformed natural and mixed rural settings. In untransformed wilderness areas, the most dominant species were Culex poicilipes, Anopheles coustani, and Aedes mcintoshi, while in mixed rural settings such as the Mnisi area, the two most abundant species were Cx. poicilipes and Mansonia uniformis. However, in peri‐urban areas, Cx. theileri, Cx. univittatus, and Cx. pipiens sensu lato were the most dominant. Aedes aegypti, Ae. mcintoshi, Ae. metallicus, Ae. vittatus, Cx. pipiens s.l., Cx. theileri, and Cx. univittatus had the widest geographical distribution in northern South Africa. Also collected were Anopheles arabiensis and An. vaneedeni, both known malaria vectors in South Africa. Arbovirus surveillance and vector control programs should be augmented in mixed rural and peri‐urban areas where the risk for mosquito‐borne disease transmission to humans and domestic stock is greater.     

Identifying and correcting spatial bias in opportunistic citizen science data for wild ungulates in Norway

Many publications make use of opportunistic data, such as citizen science observation data, to infer large‐scale properties of species’ distributions. However, the few publications that use opportunistic citizen science data to study animal ecology at a habitat level do so without accounting for spatial biases in opportunistic records or using methods that are difficult to generalize. In this study, we explore the biases that exist in opportunistic observations and suggest an approach to correct for them. We first examined the extent of the biases in opportunistic citizen science observations of three wild ungulate species in Norway by comparing them to data from GPS telemetry. We then quantified the extent of the biases by specifying a model of the biases. From the bias model, we sampled available locations within the species’ home range. Along with opportunistic observations, we used the corrected availability locations to estimate a resource selection function (RSF). We tested this method with simulations and empirical datasets for the three species. We compared the results of our correction method to RSFs obtained using opportunistic observations without correction and to RSFs using GPS‐telemetry data. Finally, we compared habitat suitability maps obtained using each of these models. Opportunistic observations are more affected by human access and visibility than locations derived from GPS telemetry. This has consequences for drawing inferences about species’ ecology. Models naïvely using opportunistic observations in habitat‐use studies can result in spurious inferences. However, sampling availability locations based on the spatial biases in opportunistic data improves the estimation of the species’ RSFs and predicted habitat suitability maps in some cases. This study highlights the challenges and opportunities of using opportunistic observations in habitat‐use studies. While our method is not foolproof it is a first step toward unlocking the potential of opportunistic citizen science data for habitat‐use studies.     

Low genetic diversity in the vulnerable Goitred Gazelle,Gazella subgutturosa (Cetartiodactyla: Bovidae), in Iran: potential genetic consequence of recent population declines

The Goitered Gazelle, Gazella subgutturosa, is the most widespread gazelle species in the Middle East and central Asia inhabiting desert and semi-desert habitats. Today it is threatened and its geographic range and population size have experienced significant decline in the last decades. In Iran, the remnant populations are confined to fragmented habitats. We aimed to characterise genetic diversity and phylogenetic status of the populations of Goitered Gazelle in Central Iran and to evaluate the potential effect of a historic population bottleneck on the genetic variation of today’s population. We used noninvasive sampling to uncover structure and level of genetic variation in a fragment of the cytochrome-b gene from 170 samples. Genealogical analyses were performed using HKY+I model and phylogenetic trees reconstructed using Bayesian inference and maximum likelihood. We found extremely low levels of genetic variation, with altogether only five haplotypes in samples from different populations. Overall haplotype diversity was 0.081 and nucleotide diversity 0.0003. The mean observed mismatch between any two sequences was 0.093 with the largest peak for small numbers. The mismatch distribution fit the model of population expansion and suggested that gazelles had experienced a sudden expansion. An unrooted median-joining network analysis of mtDNA haplotypes showed a star-like structure which few mutations steps separating the haplotypes from other regions. Our findings strengthen the urgency of preserving the species’ genetic diversity to prevent local extinction.     

A latent contingency table approach to dose finding for combinations of two agents

Summary .  Two-agent combination trials have recently attracted enormous attention in oncology research. There are several strong motivations for combining different agents in a treatment: to induce the synergistic treatment effect, to increase the dose intensity with nonoverlapping toxicities, and to target different tumor cell susceptibilities. To accommodate this growing trend in clinical trials, we propose a Bayesian adaptive design for dose finding based on latent 2 × 2 tables. In the search for the maximum tolerated dose combination, we continuously update the posterior estimates for the unknown parameters associated with marginal probabilities and the correlation parameter based on the data from successive patients. By reordering the dose toxicity probabilities in the two-dimensional space, we assign each coming cohort of patients to the most appropriate dose combination. We conduct extensive simulation studies to examine the operating characteristics of the proposed method under various practical scenarios. Finally, we illustrate our dose-finding procedure with a clinical trial of agent combinations at M. D. Anderson Cancer Center.     

ITE Merlewood Land Classification of Great Britain

Abstract. The Institute of Terrestrial Ecology (ITE) has classified the 1 km squares in Great Britain (GB) into thirty-two environmental strata, termed land classes, as a basis for ecological survey. The classes have been used in biogeographical studies of the distribution of individual species and species assemblages. The concept behind the technique is that there is an association between the environmental character of land and ecological parameters. The initial classification was based on a sample of squares drawn from a regular grid. The data for the 12121 km squares classified were drawn from published maps; the number of squares was limited by the available computing power. Subsequently the availability of more powerful computers and the need to improve geographical definition have led to the allocation of every 1 km square to its appropriate class. This paper has been written to summarise the principles involved in the development of the system and indicate the range of projects for which it has been used. The extension of the classification from a sample to the complete coverage of GB revealed the importance of the structure and style of data used to produce the classification. The significance of these conclusions for future work is discussed, with particular reference to automated methods of data capture.     

Frustration‐guided motion planning reveals conformational transitions in proteins

Proteins exist as conformational ensembles, exchanging between substates to perform their function. Advances in experimental techniques yield unprecedented access to structural snapshots of their conformational landscape. However, computationally modeling how proteins use collective motions to transition between substates is challenging owing to a rugged landscape and large energy barriers. Here, we present a new, robotics‐inspired motion planning procedure called dCC‐RRT that navigates the rugged landscape between substates by introducing dynamic, interatomic constraints to modulate frustration. The constraints balance non‐native contacts and flexibility, and instantaneously redirect the motion towards sterically favorable conformations. On a test set of eight proteins determined in two conformations separated by, on average, 7.5 Å root mean square deviation (RMSD), our pathways reduced the Cα atom RMSD to the goal conformation by 78%, outperforming peer methods. We then applied dCC‐RRT to examine how collective, small‐scale motions of four side‐chains in the active site of cyclophilin A propagate through the protein. dCC‐RRT uncovered a spatially contiguous network of residues linked by steric interactions and collective motion connecting the active site to a recently proposed, non‐canonical capsid binding site 25 Å away, rationalizing NMR and multi‐temperature crystallography experiments. In all, dCC‐RRT can reveal detailed, all‐atom molecular mechanisms for small and large amplitude motions. Source code and binaries are freely available at https://github.com/ExcitedStates/KGS/ .     

Determination of species-area relationships and minimum sampling area for the shrub communities in the arid valley in the upper reach of the Minjiang River, China

The minimum sampling areas (MSAs) for the shrub communities in the arid valley in the upper reach of the Minjiang River, China, were studied by fitting community species-area relationships using 3 types of equations. The MSAs were determined at the proportional factor (ρ) 0.6, 0.7, 0.8 and 0.9. The proportional factors represent the proportion of the number of species within a sampling plot in the total number of species. The MSAs of the shrub communities at different elevations and on different slope faces for ρ = 0.6, 0.7 and 0.8 were all around 100 m². Hence, the MSAs could be set to be 100 m² (10 m × 10 m) at 60%–80% precision levels. For ρ = 0.9, that is, for a 90% precision level, the MSAs were less than 200 m² (10 m × 20 m). The MSAs and species richness increased gradually with the rising elevation. At the elevation below 2000 m, the MSAs and species richness on the north-facing slope were larger than those on the south-facing slope. However, at the elevation around 2200 m, there was no difference amongst different facing slopes. For the shrub communities in the arid valley in the upper reach of the Minjiang River, the species-area curves by fitting the first two equations are better than that by fitting the third equation.     

Spatial sampling to detect slug abundance in an arable field

Overuse of molluscicides by farmers in arable systems can lead to environmental and product contamination. Here we assess a simple and inexpensive surface trapping method for monitoring populations of slugs (Deroceras reticulatum and Arion intermedius). This method was biased against small slugs, and against A. intermedius, when compared to direct soil sampling. Regression was used to model the relationship between the results of surface trapping and soil sampling methods. Spatial Analysis by Distance IndicEs (SADIE) algorithms were used to describe the spatial relationships between the two sets of samples. Using both traditional statistical methods and spatial statistics, the spatial information collected from surface traps was sufficient to identify patches and gaps in slug numbers and possibly to allow the spot application of slug control, and thus provide land managers who experience slug damage with a way of reducing molluscicides use, whilst maintaining slug control. Further improvements and applications of the model are discussed.