Disrupted montane forest recovery hinders biodiversity conservation in the tropical Andes

Aim

Andean montane forests are biodiversity hotspots and large carbon stores and they provide numerous ecosystem services. Following land abandonment after centuries of forest clearing for agriculture in the Andes, there is an opportunity for forest recovery. Field-based studies show that forests do not always recover. However, large-scale and long-term knowledge of recovery dynamics of Andean forests remains scarce. This paper analyses tropical montane forest recovery trajectories over a 15-year time frame at the landscape and tropical Andean scale to inform restoration planning.

Methods

We first detect “potential recovery” as areas that have experienced a forest transition between 2000 and 2005. Then, we use Landsat time series analysis of the normalized difference water index (NDWI) to classify four “realized recovery” trajectories (“ongoing”, “arrested”, “disrupted” and “no recovery”) based on a sequential pattern of 5-yearly Z-score anomalies for 2005–2020. We compare these results against an analysis of change in tree cover to validate against other datasets.

Results

Across the tropical Andes, we detected a potential recovery area of 274 km² over the period. Despite increases in tree cover, most areas of the Andes remained in early successional states (10–25% tree cover), and NDWI levelled out after 5–10 years. Of all potential forest recovery areas, 22% showed “ongoing recovery”, 61% showed either “disrupted” or “arrested recovery”, and 17% showed “no recovery”. Our method captured forest recovery dynamics in a Peruvian arrested succession context and in landscape-scale tree-planting efforts in Ecuador.

Main conclusions

Forest recovery across the Andes is mostly disrupted, arrested or unsuccessful, with consequences for biodiversity recovery and provision of ecosystem services. Low-recovery areas identified in this study might be good candidates for active restoration interventions in this UN Decade on Restoration. Future studies could determine restoration strategies and priorities and suggest management strategies at a local planning scale across key regions in the biodiversity hotspot.     

新一代Landsat系列卫星：Landsat8遥感影像新增特征及其生态环境意义

美国Landsat 8卫星的成功发射使一度中断的Landsat对地观测得以继续。Landsat 8除了保持Landsat 7卫星的基本特征外,还在波段的数量、波段的光谱范围和影像的辐射分辨率上进行了改进。基于该卫星的首幅影像,针对这些新的特性进行了分析和研究。研究发现:(1)新增的卷云波段有助于区别点云和高反射地物;(2)卷云波段设计的波长范围位于粘土矿物光谱反射的强吸收带,有利于土壤与建筑不透水面信息的区别;(3)新增的深蓝波段有助于水体悬浮物浓度的监测;(4)全色影像波长范围的收窄有利于该影像上植被和非植被的区别;(5)辐射分辨率的提高可避免极亮/极暗区的灰度过饱和现象,这对反射率极低的水体的细微特征识别有很大帮助。显然,Landsat 8这些新增的优点将会对全球生态环境变化的监测产生积极的作用。     

Object-oriented methods for habitat mapping at multiple scales – Case studies from Northern Germany and Wye Downs, UK

This paper presents an application of object-oriented techniques for habitat classification based on remotely sensed images and ancillary data. The study reports the results of habitat mapping at multiple scales using Earth Observation (EO) data at various spatial resolutions and multi temporal acquisition dates. We investigate the role of object texture and context in classification as well as the value of integrating knowledge from ancillary data sources. Habitat maps were produced at regional and local scales in two case studies; Schleswig-Holstein, Germany and Wye Downs, United Kingdom. At the regional scale, the main task was the development of a consistent object-oriented classification scheme that is transferable to satellite images for other years. This is demonstrated for a time series of Landsat TM/ETM+ scenes. At the local scale, investigations focus on the development of appropriate object-oriented rule networks for the detailed mapping of habitats, e.g. dry grasslands and wetlands using very high resolution satellite and airborne scanner images. The results are evaluated using statistical accuracy assessment and visual comparison with traditional field-based habitat maps. Whereas the application of traditional pixel-based classification result in a pixelised (salt and pepper) representation of land cover, the object-based classification technique result in solid habitat objects allowing easy integration into a vector-GIS for further analysis. The level of detail obtained at the local scale is comparable to that achieved by visual interpretation of aerial photographs or field-based mapping and also retains spatially explicit, fine scale information such as scrub encroachment or ecotone patterns within habitats.     

Challenges in studying the liquid-to-solid phase transitions of proteins using computer simulations

“Membraneless organelles,” also referred to as biomolecular condensates, perform a variety of cellular functions and their dysregulation is implicated in cancer and neurodegeneration. In the last two decades, liquid-liquid phase separation (LLPS) of intrinsically disordered and multidomain proteins has emerged as a plausible mechanism underlying the formation of various biomolecular condensates. Further, the occurrence of liquid-to-solid transitions within liquid-like condensates may give rise to amyloid structures, implying a biophysical link between phase separation and protein aggregation. Despite significant advances, uncovering the microscopic details of liquid-to-solid phase transitions using experiments remains a considerable challenge and presents an exciting opportunity for the development of computational models which provide valuable, complementary insights into the underlying phenomenon. In this review, we first highlight recent biophysical studies which provide new insights into the molecular mechanisms underlying liquid-to-solid (fibril) phase transitions of folded, disordered and multi-domain proteins. Next, we summarize the range of computational models used to study protein aggregation and phase separation. Finally, we discuss recent computational approaches which attempt to capture the underlying physics of liquid-to-solid transitions along with their merits and shortcomings.     

Assessing spatiotemporal eco-environmental vulnerability by Landsat data

An indicator of quantifying eco-environmental vulnerability was established by synthesizing 12 variables, mainly retrieved from satellite data with incorporation of analytical hierarchy process (AHP). Six vulnerability levels of potential, slight, light, medium, heavy, and very heavy were graded to depict changes of vulnerability over temporal and spatial scales. The proposed approach was employed to study spatiotemporal eco-environmental vulnerability with Landsat data acquired in 1989, 2003, and 2014 for the Thua Thien – Hue Province, Vietnam. Over the time periods of 1989–2003 and 2003–2014, both heavy and very heavy vulnerability levels exhibit an increasing trend in both magnitude and spatial size: The former raised from 5.9% in 1989, to 7.9% in 2003, and 15% in 2014; and the later increased from 1.2% in 1989, to 3.2% in 2003, and 7.3% in 2014. Both levels mainly appeared on urbanized area, bare land, semi-bare land, agricultural land, and sparse forests. In contrast, there was a significant decline in potential vulnerability level with 36.4% in 1989, 30.9% in 2003, and 19.2% in 2014, while the remaining vulnerability levels slight, light, and medium fluctuated slightly, increased in 2003 and decreased in 2014. Supporting reasons for such changes include: (1) deforestation, agriculture intensification, construction of three hydro-electric projects during the period 2003–2014; and (2) significant expansion of urbanized area leading to differences in thermal signatures in urban areas as compared with rural areas. The findings demonstrate that eco-environmental vulnerability is primarily exaggerated by anthropogenic activities through land use/land cover (LULC) changes and further enhanced by natural processes including disasters in the Thua Thien – Hue Province of Vietnam. The correlation between land surface temperature (LST) and Normalized Difference Built-up Index (NDBI) is found to be positively correlated with 0.87, 0.89, and 0.84 for 1989, 2003, and 2014, respectively. In contrast, LST-Normalized Difference Vegetation Index (NDVI) is found negatively correlated with respect to the spatiotemporal trend of environmental vulnerability with −0.81, −0.81, and −0.76 in 1989, 2003, and 2014, respectively. In addition, areas having potential, slight, and medium thermal environmental levels are decreased from 1989 to 2003 to 2003–2014. At the regional scale, increased anthropogenic activities through land’s modification have intensified the eco-environmental vulnerability in the study area. The currently proposed methodology is feasible for evaluating long-term eco-environmental changes processes by using remote sensing data, and valid for the other regions.     

Large drought‐induced aboveground live biomass losses in southern Rocky Mountain aspen forests

Widespread drought‐induced forest mortality has been documented across multiple tree species in North America in recent decades, but it is a poorly understood component in terrestrial carbon (C) budgets. Recent severe drought in concert with elevated temperature likely triggered widespread forest mortality of trembling aspen (Populus tremuloides), the most widely distributed tree species in North America. The impact on the regional C budgets and spatial pattern of this drought‐induced tree mortality, which has been termed ‘sudden aspen decline (SAD)’, is not well known and could contribute to increased regional C emissions, an amplifying feedback to climate change. We conducted a regional assessment of drought‐induced live aboveground biomass (AGB) loss from SAD across 915 km² of southwestern Colorado, USA, and investigated the influence of topography on the severity of mortality by combining field measures, remotely sensed nonphotosynthetically active vegetation and a digital elevation model. Mean [± standard deviation (SD)] remote sensing estimate of live AGB loss was 60.3 ± 37.3 Mg ha^?1, which was 30.7% of field measured AGB, totaling 2.7 Tg of potential C emissions from this dieback event. Aspen forest health could be generally categorized as healthy (0–30% field measured canopy dieback), intermediate (31–50%), and SAD (51–100%), with the remote sensing estimated mean (± SD) live AGB losses of 26.4 ± 15.1, 64.5 ± 9.2, and 108.5 ± 24.0 Mg ha^?1, respectively. There was a pronounced clustering pattern of SAD on south‐facing slopes due to relatively drier and warmer conditions, but no apparent spatial gradient was found for elevation and slope. This study demonstrates the feasibility of utilizing remote sensing to assess the ramification of climate‐induced forest mortality on ecosystems and suggests promising opportunities for systematic large‐scale C dynamics monitoring of tree dieback, which would improve estimates of C budgets of North America with climate change.