四川雅安常见住区蝇类密度监测

根据全国常见住区蝇类密度监测课题组关于“全国常见住区蝇类密度监测实施计划”安排,雅安地区定力全国七个监测点之一,作者承担了这一任务。该项工作尚在继续进行中,现将1988年监测结果报道如下。     

Empirical evaluation of the conceptual model underpinning a regional aquatic long-term monitoring program using causal modelling

Conceptual models are an integral facet of long-term monitoring programs. Proposed linkages between drivers, stressors, and ecological indicators are identified within the conceptual model of most mandated programs. We empirically evaluate a conceptual model developed for a regional aquatic and riparian monitoring program using causal models (i.e., Bayesian path analysis). We assess whether data gathered for regional status and trend estimation can also provide insights on why a stream may deviate from reference conditions. We target the hypothesized causal pathways for how anthropogenic drivers of road density, percent grazing, and percent forest within a catchment affect instream biological condition. We found instream temperature and fine sediments in arid sites and only fine sediments in mesic sites accounted for a significant portion of the maximum possible variation explainable in biological condition among managed sites. However, the biological significance of the direct effects of anthropogenic drivers on instream temperature and fine sediments were minimal or not detected. Consequently, there was weak to no biological support for causal pathways related to anthropogenic drivers’ impact on biological condition. With weak biological and statistical effect sizes, ignoring environmental contextual variables and covariates that explain natural heterogeneity would have resulted in no evidence of human impacts on biological integrity in some instances. For programs targeting the effects of anthropogenic activities, it is imperative to identify both land use practices and mechanisms that have led to degraded conditions (i.e., moving beyond simple status and trend estimation). Our empirical evaluation of the conceptual model underpinning the long-term monitoring program provided an opportunity for learning and, consequently, we discuss survey design elements that require modification to achieve question driven monitoring, a necessary step in the practice of adaptive monitoring. We suspect our situation is not unique and many programs may suffer from the same inferential disconnect. Commonly, the survey design is optimized for robust estimates of regional status and trend detection and not necessarily to provide statistical inferences on the causal mechanisms outlined in the conceptual model, even though these relationships are typically used to justify and promote the long-term monitoring of a chosen ecological indicator. Our application demonstrates a process for empirical evaluation of conceptual models and exemplifies the need for such interim assessments in order for programs to evolve and persist.     

Why is biodiversity data-deficiency an ongoing conservation dilemma in Africa?

Recent reports illustrate deficiencies in knowledge about current conditions and long-term trends in population sizes of hundreds of African plants and animals’ species. In this commentary, I discuss the lack of standardized data for assessing and monitoring biodiversity in Africa. I present my own views on the causes for these knowledge and data gaps, their consequences for conservation, and future directions that could improve the current situation.There are many reasons for lack of standardized data including; ongoing conflicts and political instability in many biodiversity-rich countries; absence of regular and policy-driven monitoring programs; weak facilities; and irregular or insufficient funding. Existing biodiversity monitoring initiatives are often short-term, poorly-designed surveys, largely dependent on volunteer researchers or international partners, biased towards large “charismatic” animal species, and published in difficult-to-access outlets. Consequently, up-to-date and rigorous reports about conditions and trends of African biodiversity are limited, and conservation planning, comparative studies and accurate valuation of ecosystem services continue to be difficult.Urgent actions include: 1) commitments and support of local governments to implement effective conservation monitoring programs; 2) establishment of a network of carefully designed long-term and continent-wide monitoring initiatives for endangered species and biodiversity; and 3) involvement of universities, research centers, Non-Governmental Organizations (NGOs) and local communities in such monitoring efforts. Such actions could stimulate further in-depth studies and systematic analysis of the root causes and solutions for the decades-long African biodiversity knowledge gap. Examples of highly needed systematic analysis and documentation in the coming efforts towards filling up the biodiversity data gap in Africa should clearly define biodiversity data-deficiency by taxonomic groups and by countries.     

Occupancy in dynamic systems: accounting for multiple scales and false positives using environmental DNA to inform monitoring

Occupancy is an important metric to understand current and future trends in populations that have declined globally. In addition, occupancy can be an efficient tool for conducting landscape-scale and long-term monitoring. A challenge for occupancy monitoring programs is to determine the appropriate spatial scale of analysis and to obtain precise occupancy estimates for elusive species. We used a multi-scale occupancy model to assess occupancy of Columbia spotted frogs in the Great Basin, USA, based on environmental DNA (eDNA) detections. We collected three replicate eDNA samples at 220 sites across the Great Basin. We estimated and modeled ecological factors that described watershed and site occupancy at multiple spatial scales simultaneously while accounting for imperfect detection. Additionally, we conducted visual and dipnet surveys at all sites and used our paired detections to estimate the probability of a false positive detection for our eDNA sampling. We applied the estimated false positive rate to our multi-scale occupancy dataset and assessed changes in model selection. We had higher naïve occupancy estimates for eDNA (0.37) than for traditional survey methods (0.20). We estimated our false positive detection rate per qPCR replicate at 0.023 (95% CI: 0.016–0.033). When the false positive rate was applied to the multi-scale dataset, we did not observe substantial changes in model selection or parameter estimates. Conservation and resource managers have an increasing need to understand species occupancy in highly variable landscapes where the spatial distribution of habitat changes significantly over time due to climate change and human impact. A multi-scale occupancy approach can be used to obtain regional occupancy estimates that can account for spatially dynamic differences in availability over time, especially when assessing potential declines. Additionally, this study demonstrates how eDNA can be used as an effective tool for improved occupancy estimates across broad geographic scales for long-term monitoring.     

An Optimized Proteomics Approach Reveals Novel Alternative Proteins in Mouse Liver Development

Alternative ORFs (AltORFs) are unannotated sequences in genome that encode novel peptides or proteins named alternative proteins (AltProts). Although ribosome profiling and bioinformatics predict a large number of AltProts, mass spectrometry as the only direct way of identification is hampered by the short lengths and relative low abundance of AltProts. There is an urgent need for improvement of mass spectrometry methodologies for AltProt identification. Here, we report an approach based on size-exclusion chromatography for simultaneous enrichment and fractionation of AltProts from complex proteome. This method greatly simplifies the variance of AltProts discovery by enriching small proteins smaller than 40 kDa. In a systematic comparison between 10 methods, the approach we reported enabled the discovery of more AltProts with overall higher intensities, with less cost of time and effort compared to other workflows. We applied this approach to identify 89 novel AltProts from mouse liver, 39 of which were differentially expressed between embryonic and adult mice. During embryonic development, the upregulated AltProts were mainly involved in biological pathways on RNA splicing and processing, whereas the AltProts involved in metabolisms were more active in adult livers. Our study not only provides an effective approach for identifying AltProts but also novel AltProts that are potentially important in developmental biology.     

Mysterious bio-duck sound attributed to the Antarctic minke whale (Balaenoptera bonaerensis)

For decades, the bio-duck sound has been recorded in the Southern Ocean, but the animal producing it has remained a mystery. Heard mainly during austral winter in the Southern Ocean, this ubiquitous sound has been recorded in Antarctic waters and contemporaneously off the Australian west coast. Here, we present conclusive evidence that the bio-duck sound is produced by Antarctic minke whales (Balaenoptera bonaerensis). We analysed data from multi-sensor acoustic recording tags that included intense bio-duck sounds as well as singular downsweeps that have previously been attributed to this species. This finding allows the interpretation of a wealth of long-term acoustic recordings for this previously acoustically concealed species, which will improve our understanding of the distribution, abundance and behaviour of Antarctic minke whales. This is critical information for a species that inhabits a difficult to access sea-ice environment that is changing rapidly in some regions and has been the subject of contentious lethal sampling efforts and ongoing international legal action.     

No Evidence‐Based Restoration Without a Sound Evidence Base: A Reply to Guldemond et al.

Evidence‐based practice is not possible without an evidence base. Guldemond et al. confuse our attempt at assessing the status of the evidence base of restoration programs in South Africa with attempting to assess whether restoration is evidence‐based. While we fully agree with them that there is a need to assess whether practitioners use evidence in their decision‐making, we assert that use of evidence is the last step in the evidence‐based approach. It is preceded by the generation (and documentation) of evidence through baseline condition assessment, proper goal setting, sound monitoring of the impacts of the chosen intervention as well as effective dissemination of resulting evidence. To answer the question whether restoration is evidence‐based would require the assessment of all stages from generation to use. We chose to start at the beginning, a logical place to start.     

Group versus population level demographics: An analysis of comparability using long term data on wild white‐faced capuchin monkeys (Cebus capucinus imitator)

Primates have long been used as indicator species for assessing overall ecosystem health. However, area‐wide census methods are time consuming, costly, and not always feasible under many field conditions. Therefore, it is important to establish whether monitoring a subset of a population accurately reflects demographic changes occurring in the population at large. Over the past 35 years, we have conducted 15 area‐wide censuses in Sector Santa Rosa, Costa Rica. These efforts have revealed important trends in population growth patterns of capuchin monkeys following the protection and subsequent regeneration of native forests. During this same period, we have also intensively studied a subset of the capuchin groups. Comparing these two datasets, we investigate whether the population structures of the closely monitored groups are reliable indicators of area‐wide demographic patterns. We compare the overall group size and the individual age/sex class compositions of study groups and nonstudy groups (i.e., those contacted during area‐wide censuses only). Our study groups contained more individuals overall with a larger proportion of infants, and there were indications that the proportion of adult and subadult males was lower. These differences can be ascribed either to sampling errors or real differences attributable to human presence and/or better habitat quality for the study groups. No other sex/age classes differed, and major demographic changes were simultaneously evident in both study and nonstudy groups. This study suggests that the Santa Rosa capuchin population is similarly impacted by large‐scale ecological patterns observable within our study groups.     

Monitoring an invasive perennial at the landscape scale with remote sensing

Summary   Worldwide, invasive weeds threaten agricultural, natural and urban ecosystems. In Australia's agricultural and grazing regions, invasive species often establish across extensive areas where weed management is hampered by an inability to detect the location and timing of an outbreak. In these vast landscapes, an effective detection and monitoring system is required to delineate the extent of the invasion and identify spatial and temporal factors associated with weed establishment and thickening. In this study, we utilize a time series of remote sensing imagery to detect the spatial and temporal patterns of Prickly Acacia ( Acacia nilotica ) invasion in the Mitchell grass plains of North Queensland. We develop a spectral index from Landsat images which is applied to images from 1989 to 2004, in combination with a classification mask, to identify locations and monitor changes in Prickly Acacia density across 29 000 km² of Mitchell grass plains. The approach identified spectral and temporal signatures consistent with Prickly Acacia infestation on 1.9% of this landscape. Field checking of results confirmed presence of the weed in previously unrecorded locations. The approach may be used to evaluate future spread, or outcomes of management strategies for Prickly Acacia in this landscape and could be employed to detect and monitor invasions in other extensive landscapes.     

Estimating linear temporal trends from aggregated environmental monitoring data

Trend estimates are often used as part of environmental monitoring programs. These trends inform managers (e.g., are desired species increasing or undesired species decreasing?). Data collected from environmental monitoring programs is often aggregated (i.e., averaged), which confounds sampling and process variation. State-space models allow sampling variation and process variations to be separated. We used simulated time-series to compare linear trend estimations from three state-space models, a simple linear regression model, and an auto-regressive model. We also compared the performance of these five models to estimate trends from a long term monitoring program. We specifically estimated trends for two species of fish and four species of aquatic vegetation from the Upper Mississippi River system. We found that the simple linear regression had the best performance of all the given models because it was best able to recover parameters and had consistent numerical convergence. Conversely, the simple linear regression did the worst job estimating populations in a given year. The state-space models did not estimate trends well, but estimated population sizes best when the models converged. We found that a simple linear regression performed better than more complex autoregression and state-space models when used to analyze aggregated environmental monitoring data.