Limits to the accurate and generalizable use of soundscapes to monitor biodiversity

Although eco-acoustic monitoring has the potential to deliver biodiversity insight on vast scales, existing analytical approaches behave unpredictably across studies. We collated 8,023 audio recordings with paired manual avifaunal point counts to investigate whether soundscapes could be used to monitor biodiversity across diverse ecosystems. We found that neither univariate indices nor machine learning models were predictive of species richness across datasets but soundscape change was consistently indicative of community change. Our findings indicate that there are no common features of biodiverse soundscapes and that soundscape monitoring should be used cautiously and in conjunction with more reliable in-person ecological surveys

Joint Sentinel-1 and SMAP data assimilation to improve soil moisture estimates

SMAP (Soil Moisture Active and Passive) radiometer observations at similar to 40 km resolution are routinely assimilated into the NASA Catchment Land Surface Model to generate the 9 km SMAP Level-4 Soil Moisture product. This study demonstrates that adding high-resolution radar observations from Sentinel-1 to the SMAP assimilation can increase the spatiotemporal accuracy of soil moisture estimates. Radar observations were assimilated either separately from or simultaneously with radiometer observations. Assimilation impact was assessed by comparing 3-hourly, 9 km surface and root-zone soil moisture simulations with in situ measurements from 9 km SMAP core validation sites and sparse networks, from May 2015 to December 2016. The Sentinel-1 assimilation consistently improved surface soil moisture, whereas root-zone impacts were mostly neutral. Relatively larger improvements were obtained from SMAP assimilation. The joint assimilation of SMAP and Sentinel-1 observations performed best, demonstrating the complementary value of radar and radiometer observations

Highway increases concentrations of toxic metals in giant panda habitat

The Qinling panda subspecies (Ailuropoda melanoleuca qinlingensis) is highly endangered with fewer than 350 individuals inhabiting the Qinling Mountains. Previous studies have indicated that giant pandas are exposed to heavy metals, and a possible source is vehicle emission. The concentrations of Cu, Zn, Mn, Pb, Cr, Ni, Cd, Hg, and As in soil samples collected from sites along a major highway bisecting the panda's habitat were analyzed to investigate whether the highway was an important source of metal contamination. There were 11 sites along a 30-km stretch of the 108th National Highway, and at each site, soil samples were taken at four distances from the highway (0, 50, 100, and 300 m) and at three soil depths (0, 5, 10 cm). Concentrations of all metals except As exceeded background levels, and concentrations of Cu, Zn, Mn, Pb, and Cd decreased significantly with increasing distance from the highway. Geo-accumulation index indicated that topsoil next to the highway was moderately contaminated with Pb and Zn, whereas topsoil up to 300 m away from the highway was extremely contaminated with Cd. The potential ecological risk index demonstrated that this area was in a high degree of ecological hazards, which were also due to serious Cd contamination. And, the hazard quotient indicated that Cd, Pb, and Mn especially Cd could pose the health risk to giant pandas. Multivariate analyses demonstrated that the highway was the main source of Cd, Pb, and Zn and also put some influence on Mn. The study has confirmed that traffic does contaminate roadside soils and poses a potential threat to the health of pandas. This should not be ignored when the conservation and management of pandas is considered

Remote sensing to shape the next generation species distribution models

Current species distribution modeling (SDM) research calls for more realistic approaches that better integrate biotic interactions and a robust modelling framework flexible enough to accommodate multiple scales. One of the current limitations of SDMs relates to a lack of availability of ecologically and spatially explicit parameters to feed into the considered models, especially when these models are defined at high spatial and temporal resolutions. In this respect, we believe that novel remote sensing data provide a unique opportunity for biogeographers to progress from classical SDMs to a new generation of SDMs. We here propose a new class of modelling approach, the next generation species distribution models (NG-SDMs

Impact of livestock on giant pandas and their habitat

Livestock production is one of the greatest threats to biodiversity worldwide. However, impacts of livestock on endangered species have been understudied, particularly across the livestock-wildlife interface in forested protected areas. We investigated the impact of an emerging livestock sector in China's renowned Wolong Nature Reserve for giant pandas. We integrated empirical data from field surveys, remotely sensed imagery, and GPS collar tracking to analyze (1) the spatial distribution of horses in giant panda habitat, (2) space use and habitat selection patterns of horses and pandas, and (3) the impact of horses on pandas and bamboo (panda's main food source). We discovered that the horse distribution overlapped with suitable giant panda habitat. Horses had smaller home ranges than pandas but both species showed similarities in habitat selection. Horses consumed considerable amounts of bamboo, and may have resulted in a decline in panda habitat use. Our study highlights the need to formulate policies to address this emerging threat to the endangered giant panda. It also has implications for understanding livestock impacts in other protected areas across the globe

Incorporating Ecosystem Functional Diversity into Geographic Conservation Priorities Using Remotely Sensed Ecosystem Functional Types

Conservation biology must set geographic conservation priorities not only based on the compositional or structural but also on the functional dimensions of biodiversity. However, assessing functional diversity is challenging at the regional scale. We propose the use of satellite-derived Ecosystem Functional Types (EFTs), defined here as patches of land surface that share similar primary production dynamics, to incorporate such aspects of ecosystem functional diversity into the selection of protected areas. We applied the EFT approach to the Baja California Peninsula, Mexico, to characterize the regional heterogeneity of primary production dynamics in terms of EFTs; to set conservation priorities based on EFT richness and rarity; and to explore whether such EFT-based conservation priorities were consistent with and/or complementary to previous assessments focused on biodiversity composition and structure. EFTs were identified based on three ecosystem functional attributes derived from seasonal dynamics of the Enhanced Vegetation Index: the annual mean (proxy of primary production), the seasonal coefficient of variation (descriptor of seasonality), and the date of maximum (indicator of phenology). EFT-based priorities identified 26% of the peninsula as being of extreme or high priority and reinforced the value of the ecosystem functional diversity of areas already prioritized by traditional conservation assessments. In addition, our study revealed that biodiversity composition- and structure-based assessments had not identified the full range of important areas for EFT diversity and tended to better capture areas of high EFT rarity than those of high EFT richness. Our EFT-based assessment demonstrates how remotely sensed regional heterogeneity in ecosystem functions could reinforce and complement traditional conservation priority setting.European Union (EU)Spanish MINECO CGL2014-61610-EXPUniversity of Almeria (PhD contract: research training program)European Union (EU) 641762NASA 2016 GEOBON Work Programme Grant 80NSSC18K044