A new framework of spatial targeting for single-species conservation planning

Context

Organisations acting to conserve and protect species across large spatial scales prioritise to optimise use of resources. Spatial conservation prioritization tools typically focus on identifying areas containing species groups of interest, with few tools used to identify the best areas for single-species conservation, in particular, to conserve currently widespread but declining species.

Objective

A single-species prioritization framework, based on temporal and spatial patterns of occupancy and abundance, was developed to spatially prioritize conservation action for widespread species by identifying smaller areas to work within to achieve predefined conservation objectives.

Methods

We demonstrate our approach for 29 widespread bird species in the UK, using breeding bird atlas data from two periods to define distribution, relative abundance and change in relative abundance. We selected occupied 10-km squares with abundance trends that matched species conservation objectives relating to maintaining or increasing population size or range, and then identified spatial clusters of squares for each objective using a Getis-Ord-Gi* or near neighbour analysis.

Results

For each species, the framework identified clusters of 20-km squares that enabled us to identify small areas in which species recovery action could be prioritized.

Conclusions

Our approach identified a proportion of species’ ranges to prioritize for species recovery. This approach is a relatively quick process that can be used to inform single-species conservation for any taxa if sufficiently fine-scale occupancy and abundance information is available for two or more time periods. This is a relatively simple first step for planning single-species focussed conservation to help optimise resource use.

     

Identifying climate refugia for 30 Australian rainforest plant species,from the last glacial maximum to 2070

Landscape Ecology - Climate refugia—areas that remain suitable for species during periods of climate disruption—have played an important role in species persistence over time....     

Genetic Advancement of Newly Developed Wheat Populations Under Drought-Stressed and Non-Stressed Conditions

Journal of Crop Science and Biotechnology - Genetic variation and heritability estimates in early generations are important in identifying superior families that can be targeted for genetic...     

Determining corn nitrogen rates using multiple prediction models

Weather uncertainty and soil spatial variability impact nitrogen (N) cycling and corn (Zea mays L.) growth, making accurate N predictions a challenge. Field studies were conducted in Lansing, Michigan, to evaluate a computer model (i.e., Adapt-N), a preseason year-based model (i.e., maximum return to N [MRTN]), and a crop sensor model (i.e., active canopy sensor with algorithm) for recommending corn N rates. To determine site-specific economic optimum N rates (EONR), five N rates were also applied (0, 33%, 66%, 133%, and 166% of the suggested MRTN) as starter + sidedress (SD) at V4. In a wet year (i.e., 2015), Adapt-N increased V8 SD N rates 35 kg N ha^?1 relative to the MRTN V4 SD N application. Although the greater rate of N may have provided additional yield protection, no statistical yield differences were observed between the two models. The MRTN model increased partial factor productivity (PFP) 20% relative to Adapt-N. Limited expression of V8 corn N deficiency reduced crop sensor total N rates (21–56 kg N ha^?1) and yield (0.82–1.05 Mg ha^?1) relative to other models. In a drier year (i.e., 2016), N demand was reduced (EONR 64 kg N ha^?1 less than 2015), resulting in similar corn response to all three models. Despite differences in actual corn N rate recommendations, all three models resulted in similar economic net returns across study years.

Abbreviations: EONR, economic optimum nitrogen rate; MRTN, Maximum Return to Nitrogen; NUE, nitrogen-use efficiency; PFP, partial factor productivity; SBNRC, sensor-based nitrogen rate calculator; SD, side-dress     

Production of Lectins from Marine Algae: Current Status,Challenges, and Opportunities for Non-Destructive Extraction

Marine algae are an excellent source of novel lectins. The isolation of lectins from marine algae expands the diversity in structure and carbohydrate specificities of lectins isolated from other sources. Marine algal lectins have been reported to have antiviral, antitumor, and antibacterial activity. Lectins are typically isolated from marine algae by grinding the algal tissue with liquid nitrogen and extracting with buffer and alcohol. While this method produces higher yields, it may not be sustainable for large-scale production, because a large amount of biomass is required to produce a minute amount of compound, and a significant amount of waste is generated during the extraction process. Therefore, non-destructive extraction using algal culture water could be used to ensure a continuous supply of lectins without exclusively disrupting the marine algae. This review discusses the traditional and recent advancements in algal lectin extraction methods over the last decade, as well as the steps required for large-scale production. The challenges and prospects of various extraction methods (destructive and non-destructive) are also discussed.     

免耕对华北地区潮土碳库特征的影响

以实施7年的中国科学院禹城综合试验站冬小麦-夏玉米轮作免耕长期定位试验场为对象,系统研究免耕条件下土壤总碳(TC)、有机碳(SOC)、无机碳(SIC)的变化,为进一步评价免耕措施对华北地区潮土碳库的影响提供数据支持。研究设置免耕秸秆覆盖(NTRC)、免耕施用有机肥(NTRR)、常规耕作(CT)3种处理,分析表层(0-20cm)及深层(20-60cm)土壤TC、SOC及SIC的变化特征和影响因素。主要结果为:NTRC和NTRR能够增加0-20cm土层TC含量及储量,但降低20-60cm土层TC含量及储量,0-60cm总碳储量表现为NTRC>CT>NTRR;与CT相比,NTRC能够显著增加0-20cm而降低20-60cm土层SOC含量及储量,NTRR增加了0-5cm土层SOC含量及储量,在5-60cm则呈降低趋势,0-60cm土层SOC储量表现为CT>NTRC>NTRR;NTRC增加了0-60cm土层SIC储量,而NTRR则影响较小。TC与SOC呈显著正相关(P<0.05),而与SIC呈显著负相关(P<0.05),说明总碳的变化趋势与SOC一致,与SIC相反。     

Effect of glyphosate on the tripartite symbiosis formed by Glomus intraradices,Bradyrhizobium japonicum,and genetically modified soybean

Most soybeans grown in North America are genetically modified (GM) to tolerate applications of the broad-spectrum herbicide glyphosate; as a result, glyphosate is now extensively used in soybean cropping systems. Soybean roots form both arbuscular mycorrhizal (AM) and rhizobial symbioses. In addition to individually improving host plant fitness, these symbioses also interact to influence the functioning of each symbiosis, thereby establishing a tripartite symbiosis. The objectives of this study were to (1) estimate the effects of glyphosate on the establishment and functioning of AM and rhizobial symbioses with GM soybean, and (2) to estimate the interdependence of the symbioses in determining the response of each symbiosis to glyphosate. These objectives were addressed in two experiments; the first investigated the importance of the timing of glyphosate application in determining the responses of the symbionts and the second varied the rate of glyphosate application. Glyphosate applied at recommended field rates had no effect on Glomus intraradices or Bradyrhizobium japonicum colonization of soybean roots, or on soybean foliar tissue [P]. N₂-fixation was greater for glyphosate-treated soybean plants than for untreated-plants in both experiments, but only when glyphosate was applied at the first trifoliate soybean growth stage. These data deviate from previous studies estimating the effect of glyphosate on the rhizobial symbiosis, some of which observed negative effects on rhizobial colonization and/or N₂-fixation. We did observe evidence of the response of one symbiont (stimulation of N₂-fixation following glyphosate) being dependent on co-inoculation with the other; however, this interactive response appeared to be contextually dependent as it was not consistent between experiments. Future research needs to consider the role of environmental factors and other biota when evaluating rhizobial responses to herbicide applications.     

Predicting percentage of intramuscular fat using two types of real-time ultrasound equipment

In the present study, 500 steers were used to develop models for predicting the percentage of intramuscular fat (PIMF) in live beef cattle. Before slaughter, steers were scanned across the 11th and 13th ribs using Aloka 500V (AL-500) and Classic Scanner 200 (CS-200) machines. Four to five images were collected per individual steer using each machine. After slaughter, a cross-sectional slice of the longissimus muscle from the 12th rib facing was used for chemical extraction to determine actual carcass percentage of intramuscular fat (CPIMF). Texture analysis software was used by two interpreters to select a region for determination of image parameters, which included Fourier, gradient, histogram, and co-occurrence parameters. Four prediction models were developed separately for each of AL-500 and CS-200 based on images captured by the respective machines. These included models developed without transformation of CPIMF (Model I), models based on logarithmic transformation of CPIMF (Model II), ridge regression procedure (Model III), and principal component regression procedure (Model IV). Model R2 and root mean square error of AL-500 Models I, II, III, and IV were 0.72, 0.84%; 0.72, 0.85%; 0.69, 0.91%; and 0.71, 0.86%; respectively. The corresponding R2 and root mean square error values of CS-200 Models I, II, III, and IV were 0.68, 0.87%; 0.70, 0.85%; 0.64, 0.94%; and 0.65, 0.91%; respectively. Initially, AL-500 and CS-200 prediction models were validated separately on an independent data set from 71 feedlot steers. The overall mean bias, standard error of prediction, and rank correlation coefficient across the four AL-500 models were 0.42%, 0.84%, and 0.88, respectively. For the four CS-200 models, the corresponding overall mean values were 0.67%, 0.81%, and 0.91, respectively. In a second validation test, only Model II of AL-500 and CS-200 was evaluated separately based on data from 24 feedlot steers. The overall mean bias, absolute difference, and standard error of prediction of AL-500 Model II were 0.71, 0.92, and 0.98%. For CS-200 Model II, the corresponding values were 0.59, 0.97, and 1.03%. Both AL-500 and CS-200 equipment can be used to accurately predict PIMF in live cattle. Further improvement in the accuracy of prediction equations could be achieved through increasing the development data set and the variation in PIMF of cattle used.