Toward reassessing data‐deficient species

One in 6 species (13,465 species) on the International Union for Conservation of Nature (IUCN) Red List is classified as data deficient due to lack of information on their taxonomy, population status, or impact of threats. Despite the chance that many are at high risk of extinction, data‐deficient species are typically excluded from global and local conservation priorities, as well as funding schemes. The number of data‐deficient species will greatly increase as the IUCN Red List becomes more inclusive of poorly known and speciose groups. A strategic approach is urgently needed to enhance the conservation value of data‐deficient assessments. To develop this, we reviewed 2879 data‐deficient assessments in 6 animal groups and identified 8 main justifications for assigning data‐deficient status (type series, few records, old records, uncertain provenance, uncertain population status or distribution, uncertain threats, taxonomic uncertainty, and new species). Assigning a consistent set of justification tags (i.e., consistent assignment to assessment justifications) to species classified as data deficient is a simple way to achieve more strategic assessments. Such tags would clarify the causes of data deficiency; facilitate the prediction of extinction risk; facilitate comparisons of data deficiency among taxonomic groups; and help prioritize species for reassessment. With renewed efforts, it could be straightforward to prevent thousands of data‐deficient species slipping unnoticed toward extinction.     

Influence of manure application on surface energy and snow cover: model development and sensitivities

Winter landspreading is an important part of manure management in the U.S. Upper Midwest. Although the practice is thought to lead to excessive P runoff losses, surprisingly little has been learned from field experiments or current water quality models. We captured knowledge gained from winter manure landspreading experiments by modifying a mechanistic snow ablation model to include manure. The physically based, modified model simulated the observed delay in snow cover disappearance and surface energy balance changes caused by application of the manure. Additional model simulations of surface energy balance estimates of radiation and turbulent fluxes showed that during intense melting events the manure on top of snow significantly reduced the energy available for melt of the snow underneath, slowing melt. The effect was most pronounced when snowmelt was driven by both relatively high solar radiation and turbulent heat fluxes. High absorbed shortwave radiation caused significant warming of the manure, which led to substantial losses in turbulent fluxes and longwave radiation. Simulations of snowmelt also showed that manure applications between 45 and 100 Mg ha(-1) significantly reduced peak snowmelt rates, in proportion to the manure applied. Lower snowmelt rates beneath manure may allow more infiltration of meltwater compared with bare snow. This infiltration and attenuated snowmelt runoff may partially mitigate the enhanced likelihood of P runoff from unincorporated winter-spread manure.     

Using decision science to evaluate global biodiversity indices

Global biodiversity indices are used to measure environmental change and progress toward conservation goals, yet few indices have been evaluated comprehensively for their capacity to detect trends of interest, such as declines in threatened species or ecosystem function. Using a structured approach based on decision science, we qualitatively evaluated 9 indices commonly used to track biodiversity at global and regional scales against 5 criteria relating to objectives, design, behavior, incorporation of uncertainty, and constraints (e.g., costs and data availability). Evaluation was based on reference literature for indices available at the time of assessment. We identified 4 key gaps in indices assessed: pathways to achieving goals (means objectives) were not always clear or relevant to desired outcomes (fundamental objectives); index testing and understanding of expected behavior was often lacking; uncertainty was seldom acknowledged or accounted for; and costs of implementation were seldom considered. These gaps may render indices inadequate in certain decision-making contexts and are problematic for indices linked with biodiversity targets and sustainability goals. Ensuring that index objectives are clear and their design is underpinned by a model of relevant processes are crucial in addressing the gaps identified by our assessment. Uptake and productive use of indices will be improved if index performance is tested rigorously and assumptions and uncertainties are clearly communicated to end users. This will increase index accuracy and value in tracking biodiversity change and supporting national and global policy decisions, such as the post-2020 global biodiversity framework of the Convention on Biological Diversity.     

Influence of manure application on surface energy and snow cover: field experiments

Application of manure to frozen and/or snow-covered soils of high-latitude, continental climate regions is associated with enhanced P losses to surface water bodies, but the practice is an essential part of most animal farming systems in these regions. Field experiments of the fates of winter-applied manure P are so difficult as to make them essentially impractical, so a mechanistic, modeling approach is required. Central to a mechanistic understanding of manure P snow-melt runoff is knowledge of snowpack disappearance (ablation) as affected by manure application. The objective of this study was to learn how solid manure applied to snow-covered fields modulates the surface energy balance and thereby snow cover ablation. Manure landspreading experiments were conducted in Arlington, WI during the winters of 1998 and 1999. Solid dairy manure was applied on top of snow at a rate of 70 Mg ha(-1) in 1998, and at 45 and 100 Mg ha(-1) in 1999. Results showed that the manure retarded melt, in proportion to the rate applied. The low-albedo manure increased absorption of shortwave radiation compared with snow, but this extra energy was lost in longwave radiation and turbulent flux of sensible and latent heat. These losses result in significant attenuation of melt peaks, retarding snowmelt. Lower snowmelt rates beneath manure may allow more infiltration of meltwater compared with bare snow. This infiltration and attenuated snowmelt runoff may partially mitigate the enhanced likelihood of P runoff from unincorporated winter-spread manure.     

Window of opportunity of <Emphasis Type="Italic">Liparis loeselii</Emphasis> populations during vegetation succession on the Wadden Sea islands

The present study aims at assessing the life span of populations of the orchid L. loeselii during successional in dune slacks on the Wadden Sea islands. An inventory of Liparis loeselii populations was carried out on 6 islands, while the population structure was assessed in more detail on the island of Texel. The occurrence of the orchid was related to soil factors and hydrological conditions. Groundwater levels were measured in a dune area with natural dune slack formation. The groundwater composition was analyses and the freshwater lenses were modelled. Various scenarios were investigated, including sea level rise and sand nourishment. The window of opportunity for L. loeselii to colonize a new slack is relatively narrow. Under favorable management, the population can survive c. 20 years. Soil pH was positively correlated with the occurrence of L. loeselii populations, while a high organic matter content was negatively correlated. Sites with large populations of L. loeselii, were situated in young dune slacks, that functioned as flow-through lakes and has top soilsinfluenced by anoxic calcareous groundwater. Modelling showed that the freshwater lens would decrease due to sea level rise, while artificial sand nourishment could counteract this effect. The populations of the Hors area on Texel, the Netherlands, can survive for several decades due to continuous formation of new slacks. Discharge of calcareous groundwater is essential to keep the pH on a high level. The hydrological systems that supply dune slack with groundwater are relatively small and are vulnerable for changes in sea level rise.     

A guide to representing variability and uncertainty in biodiversity indicators

Biodiversity indicators are used to inform decisions and measure progress toward global targets, such as the United Nations Sustainable Development Goals. Indicators aggregate and simplify complex information, so underlying information influencing its reliability and interpretation (e.g., variability in data and uncertainty in indicator values) can be lost. Communicating uncertainty is necessary to ensure robust decisions and limit misinterpretations of trends, yet variability and uncertainty are rarely quantified in biodiversity indicators. We developed a guide to representing uncertainty and variability in biodiversity indicators. We considered the key purposes of biodiversity indicators and commonly used methods for representing uncertainty (standard error, bootstrap resampling, and jackknife resampling) and variability (quantiles, standard deviation, median absolute deviation, and mean absolute deviation) with intervals. Using 3 high-profile biodiversity indicators (Red List Index, Living Planet Index, and Ocean Health Index), we tested the use, suitability, and interpretation of each interval method based on the formulation and data types underpinning the indicators. The methods revealed vastly different information; indicator formula and data distribution affected the suitability of each interval method. Because the data underpinning each indicator were not normally distributed, methods relying on normality or symmetrical spread were unsuitable. Quantiles, bootstrapping, and jackknifing provided useful information about the underlying variability and uncertainty. We built a decision tree to inform selection of the appropriate interval method to represent uncertainty or variation in biodiversity indicators, depending on data type and objectives. Our guide supports transparent and effective communication of biodiversity indicator trends to facilitate accurate interpretation by decision makers.     

The marine bacterium Leucothrix mucor as an algal epiphyte

Leucothrix mucor, a filamentous marine heterotrophic bacterium, is found in nature primarily as an algal epiphyte. Its distinctive characteristics make it easily recognizable by dir ect light-microscopic examination, and a method was develop ed to quantify natural populations of L. mucor on algae by direct microscopic counts. L. mucor was found to be widely distributed in the intertidal environment, especially in the mid-to-upper intertidal regions. Populations were most abundant on members of Rhodophyta, particularly on the alga Bangia juscopurpurea. The nature of the L. mucor-algal relationship was investigated by choosing an artificial plastic substrate to which L. mucor would attach. The use of the plastic substrate, both alone, and in conjunction with a natural algal host, gave results supporting the conclusion that L. mucor obtains its nutrients for growth from the alga rather than from seawater. Algae in the upper intertidal region may also protect attached L. mucor from desiccation.     

黏性颗粒在滤料表面沉积的动态模拟及规律

为研究颗粒黏性对过滤除尘性能的影响,分析了颗粒沉积过程的受力情况,应用颗粒流计算软件PFC2D对颗粒在滤料表面的沉积行为进行了数值模拟。结果表明:颗粒形成粉尘层的过程中主要受到风流力、惯性力等压缩力和范德华力、滚动摩擦力偶矩等压缩阻力的作用,颗粒黏性的增加,会使颗粒间滚阻系数和摩擦系数显著增加;PFC可实现粉尘颗粒在滤料表面沉积过程的可视化,得到不同黏性颗粒在滤料表面的沉积形态,滚阻系数或摩擦系数越大,粉尘层孔隙率越大,过滤阻力越小,其中,摩擦系数的影响作用弱于滚阻系数。     

Predicting the conservation status of data‐deficient species

There is little appreciation of the level of extinction risk faced by one‐sixth of the over 65,000 species assessed by the International Union for Conservation of Nature. Determining the status of these data‐deficient (DD) species is essential to developing an accurate picture of global biodiversity and identifying potentially threatened DD species. To address this knowledge gap, we used predictive models incorporating species’ life history, geography, and threat information to predict the conservation status of DD terrestrial mammals. We constructed the models with 7 machine learning (ML) tools trained on species of known status. The resultant models showed very high species classification accuracy (up to 92%) and ability to correctly identify centers of threatened species richness. Applying the best model to DD species, we predicted 313 of 493 DD species (64%) to be at risk of extinction, which increases the estimated proportion of threatened terrestrial mammals from 22% to 27%. Regions predicted to contain large numbers of threatened DD species are already conservation priorities, but species in these areas show considerably higher levels of risk than previously recognized. We conclude that unless directly targeted for monitoring, species classified as DD are likely to go extinct without notice. Taking into account information on DD species may therefore help alleviate data gaps in biodiversity indicators and conserve poorly known biodiversity. Predección del Estado de Conservación de Especies con Deficiencia de Datos