Human-modified landscapes driving the global primate extinction crisis

The world's primates have been severely impacted in diverse and profound ways by anthropogenic pressures. Here, we evaluate the impact of various infrastructures and human-modified landscapes on spatial patterns of primate species richness, at both global and regional scales. We overlaid the International Union for the Conservation of Nature (IUCN) range maps of 520 primate species and applied a global 100 km² grid. We used structural equation modeling and simultaneous autoregressive models to evaluate direct and indirect effects of six human-altered landscapes variables (i.e., human footprint [HFP], croplands [CROP], road density [ROAD], pasture lands [PAST], protected areas [PAs], and Indigenous Peoples' lands [IPLs]) on global primate species richness, threatened and non-threatened species, as well as on species with decreasing and non-decreasing populations. Two-thirds of all primate species are classified as threatened (i.e., Critically Endangered, Endangered, and Vulnerable), with ~86% experiencing population declines, and ~84% impacted by domestic or international trade. We found that the expansion of PAST, HFP, CROP, and road infrastructure had the most direct negative effects on primate richness. In contrast, forested habitat within IPLs and PAs was positively associated in safeguarding primate species diversity globally, with an even stronger effect at the regional level. Our results show that IPLs and PAs play a critical role in primate species conservation, helping to prevent their extinction; in contrast, HFP growth and expansion has a dramatically negative effect on primate species worldwide. Our findings support predictions that the continued negative impact of anthropogenic pressures on natural habitats may lead to a significant decline in global primate species richness, and likely, species extirpations. We advocate for stronger national and international policy frameworks promoting alternative/sustainable livelihoods and reducing persistent anthropogenic pressures to help mitigate the extinction risk of the world's primate species.     

Change in climatically suitable breeding distributions reduces hybridization potential between Vermivora warblers

Aim

Climate change is affecting the distribution of species and subsequent biotic interactions, including hybridization potential. The imperiled Golden-winged Warbler (GWWA) competes and hybridizes with the Blue-winged Warbler (BWWA), which may threaten the persistence of GWWA due to introgression. We examined how climate change is likely to alter the breeding distributions and potential for hybridization between GWWA and BWWA.

Location

North America.

Methods

We used GWWA and BWWA occurrence data to model climatically suitable conditions under historical and future climate scenarios. Models were parameterized with 13 bioclimatic variables and 3 topographic variables. Using ensemble modeling, we estimated historical and modern distributions, as well as a projected distribution under six future climate scenarios. We quantified breeding distribution area, the position of and amount of overlap between GWWA and BWWA distributions under each climate scenario. We summarized the top explanatory variables in our model to predict environmental parameters of the distributions under future climate scenarios relative to historical climate.

Results

GWWA and BWWA distributions are projected to substantially change under future climate scenarios. GWWA are projected to undergo the greatest change; the area of climatically suitable breeding season conditions is expected to shift north to northwest; and range contraction is predicted in five out of six future climate scenarios. Climatically suitable conditions for BWWA decreased in four of the six future climate scenarios, while the distribution is projected to shift east. A reduction in overlapping distributions for GWWA and BWWA is projected under all six future climate scenarios.

Main Conclusions

Climate change is expected to substantially alter the area of climatically suitable conditions for GWWA and BWWA, with the southern portion of the current breeding ranges likely to become climatically unsuitable. However, interactions between BWWA and GWWA are expected to decline with the decrease in overlapping habitat, which may reduce the risk of genetic introgression.     

Development and use of probability models: The industry perspective

Summary In the processed meat industry, food safety and microbiological shelf life issues lend themselves to the use of probability modeling. Our research concentrated on predicting the effectiveness of sodium lactate as an antibotulinal agent in vacuum packaged, uncured and cured turkey breast model systems. In uncured turkey breast containing 1.4% NaCl, 0.3% Na phosphate, and 0–3% Na lactate, the antibotulinal effect of sodium lactate can be predicted using the following model: Days to toxicity = 3.13+0.39(Na lactate)². Using cured turkey breast with 0.3% Na phosphate, 0.2% sucrose, 0–3% Na lactate, the time to toxicity can be predicted from the following model: Days to toxicity = 1.69+4.88(NaCl)–11.16(Na lactate)+7.23(Na lactate)². Probability models have also been developed to predict the refrigerated shelf life of specific processed meat products. The usefulness of the predictive modeling for food safety and quality in the food industry will also be discussed.This paper was presented at The International Conference on the Application of Predictive Microbiology and Computer Modeling Techniques to the Food Industry, April 12–15 1992, Hyatt Regency Hotel, Tampa, FL, USA.     

The greenhouse effect: the impacts of carbon dioxide (CO2), ultraviolet-B (UV-B) radiation and ozone (O3) on vegetation (crops)

Man's influence on the greenhouse effect, the heating of the atmosphere due to increasing concentrations of tropospheric trace gases, is of much international concern. Among the climatic variables, elevated levels of carbon dioxide (CO₂), ultraviolet-B (UV-B) radiation and ozone (O₃) are known to have a direct effect on vegetation. Our current knowledge of these effects is mainly based on studies involving single stress mode. Thus, the joint effects of CO₂, UV-B and O₃ on vegetation are poorly understood. Nevertheless, based on the literature analysis of plant response to individual stress factors, it can be concluded that sorghum, pea, bean, potato, oat, lettuce, cucumber, rice and tomato are among the crop species potentially sensitive to the joint effects of the aforementioned three variables. Similar information for tree species is essentially lacking.At least with some climatic variables such as O₃, present modeling efforts of cause-effect relationships have proven to be controversial. While at a regional geographic scale ambient CO₂ concentrations appear to be relatively homogeneous, ambient concentrations of O₃ exhibit significant temporal and spatial variability. Because of the protective action of O₃ against UV-B, similar but inverse temporal and spatial variability is expected in the surface levels of UV-B. Thus, future experimental designs should consider these exposure dynamics and modeling cuase-effect relationships should be directed to stochastic processes.     

A data-driven framework to model the organism–environment system

Organisms modify their development and function in response to the environment. At the same time, the environment is modified by the activities of the organism. Despite the ubiquity of such dynamical interactions in nature, it remains challenging to develop models that accurately represent them, and that can be fitted using data. These features are desirable when modeling phenomena such as phenotypic plasticity, to generate quantitative predictions of how the system will respond to environmental signals of different magnitude or at different times, for example, during ontogeny. Here, we explain a modeling framework that represents the organism and environment as a single coupled dynamical system in terms of inputs and outputs. Inputs are external signals, and outputs are measurements of the system in time. The framework uses time-series data of inputs and outputs to fit a nonlinear black-box model that allows to predict how the system will respond to novel input signals. The framework has three key properties: it captures the dynamical nature of the organism–environment system, it can be fitted with data, and it can be applied without detailed knowledge of the system. We study phenotypic plasticity using in silico experiments and demonstrate that the framework predicts the response to novel environmental signals. The framework allows us to model plasticity as a dynamical property that changes in time during ontogeny, reflecting the well-known fact that organisms are more or less plastic at different developmental stages.     

珠三角城市群区域生态系统服务供需均衡关系

生态系统服务供需均衡关系分析为生态系统管理提供了详实的科学基础信息。基于土地利用、气象和社会经济等多源数据,采用遥感反演、水量平衡方程、修正的通用土壤流失方程和植被净生产力模型(CASA)等方法,分别评估了1990—2015年珠三角城市群粮食供给、产水服务、固碳释氧和土壤保持服务;采用相关社会经济指标核算了生态系统服务的需求量;进而揭示了栅格、县域和市域多尺度的生态系统服务供需均衡关系及其时空演变特征。研究发现：(1)除粮食供给外,产水服务、固碳释氧和土壤保持服务呈波动性增加的趋势,增幅分别为52.2%、21.8%和73.4%;在空间上表现为中部平原地区服务水平低,低山丘陵地带高的分布特征。(2)除土壤保持服务外,粮食供给、产水服务和固碳释氧服务需求量不断增加,增幅分别为10.1%、17.5%和769.4%;中部平原地区生态系统服务需求量大,低山丘陵地带小。(3)除土壤保持和产水服务外,粮食供给、固碳释氧及综合服务供需指数不断下降,其中,2015年粮食供给和固碳释氧供需指数分别为-0.47和-0.71。研究结果可为决策者了解区域的生态系统服务供需均衡匹配状况提供科学基础信息,直接服务并...     

成渝城市群区域生态与城镇发展双网络格局分析及时空演变

协调生态网络格局与城镇网络格局是城市可持续发展的重要课题。基于2000、2010、2020年成渝城市群3期土地利用数据,应用形态空间格局分析(MSPA)、景观格局指数、景观连通性、最小累计阻力(MCR)模型等方法构建研究区生态源地与生态廊道、经济社会源地与交通廊道形成的区域生态网络格局与城镇发展网络格局,分析节点、廊道、源地、网络等“点、线、面、网”4类空间要素特征与演变趋势,探讨区域双网络格局空间关系与干扰时空变化情况。结果表明：(1)2000—2020年,研究区生态源地与生态廊道受到不同程度城镇化影响,两者总面积分别减少2660.26 km²、1169.9 km²,经济社会源地与交通廊道总面积则分别增长4324.19 km²、2392.14 km²;(2)以重庆中部、四川西南部为核心的高密度区生态关键点在2000—2020年间重心呈现先由东至西南迁移,后又向东及东北回移的趋势,数量减少了311,同时2020年生态干扰点数量达到2000年的3倍;(3)2000—2020年交通廊道对生态源地的干扰不...     

Comparison of conformational characteristics in structurally similar protein pairs.

Although it is known that three-dimensional structure is well conserved during the evolutionary development of proteins, there have been few studies that consider other parameters apart from divergence of the main-chain coordinates. In this study, we align the structures of 90 pairs of homologous proteins having sequence identities ranging from 5 to 100%. Their structures are compared as a function of sequence identity, including not only consideration of C alpha coordinates but also accessibility, Ooi numbers, secondary structure, and side-chain angles. We discuss how these properties change as the sequences become less similar. This will be of practical use in homology modeling, especially for modeling very distantly related or analogous proteins. We also consider how the average size and number of insertions and deletions vary as sequences diverge. This study presents further quantitative evidence that structure is remarkably well conserved in detail, as well as at the topological level, even when the sequences do not show similarity that is significant statistically.     

An automated method for modeling proteins on known templates using distance geometry.

We present an automated method incorporated into a software package, FOLDER, to fold a protein sequence on a given three-dimensional (3D) template. Starting with the sequence alignment of a family of homologous proteins, tertiary structures are modeled using the known 3D structure of one member of the family as a template. Homologous interatomic distances from the template are used as constraints. For nonhomologous regions in the model protein, the lower and the upper bounds for the interatomic distances are imposed by steric constraints and the globular dimensions of the template, respectively. Distance geometry is used to embed an ensemble of structures consistent with these distance bounds. Structures are selected from this ensemble based on minimal distance error criteria, after a penalty function optimization step. These structures are then refined using energy optimization methods. The method is tested by simulating the alpha-chain of horse hemoglobin using the alpha-chain of human hemoglobin as the template and by comparing the generated models with the crystal structure of the alpha-chain of horse hemoglobin. We also test the packing efficiency of this method by reconstructing the atomic positions of the interior side chains beyond C beta atoms of a protein domain from a known 3D structure. In both test cases, models retain the template constraints and any additionally imposed constraints while the packing of the interior residues is optimized with no short contacts or bond deformations. To demonstrate the use of this method in simulating structures of proteins with nonhomologous disulfides, we construct a model of murine interleukin (IL)-4 using the NMR structure of human IL-4 as the template. The resulting geometry of the nonhomologous disulfide in the model structure for murine IL-4 is consistent with standard disulfide geometry.