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Few studies exist that document how high-elevation Andean ecosystems recover naturally after the cessation of human activities and this can limit the implementation of cost-effective restoration actions. We assessed Andean forest (Polylepis stands) and páramo grassland recovery along an elevation gradient (3,600–4,350 m.a.s.l.) in the Yanacocha Reserve (Ecuador) where natural recovery has been allowed since 1995. Within the Yanacocha Reserve in 2012 and 2014 the aboveground biomass (AGB), aboveground necromass (AGN) and belowground biomass (BGB) carbon (C) stocks were measured and C sequestration rates calculated as proxy of ecosystem recovery. The soil organic carbon (SOC) stock to 36-cm depth was also quantified during the 2012 survey. To explore potential drivers of spatiotemporal variation of the forest and páramo C stocks they were related to abiotic and biotic variables. Andean forest C stocks were influenced mainly by disturbance history and tree-species composition. Páramo C stocks´ spatial variation were related to the elevation gradient; we found a positive significant trend in páramo AGB-C stocks with elevation, whereas we found a significant negative trend in AGN-C stocks. Likewise, significant temporal changes were found for AGB-C and AGN-C stocks. Net increases in AGB-C stocks were the largest in the Andean forest and páramo, 2.5 Mg C ha-1 year-1 and 1.5 Mg C ha-1 year-1 respectively. Carbon sequestration rates were partly explained by environmental variables. In the Andean forest, plots with low dominance of Baccharis padifolia were observed to present higher AGB-C and lower BGB-C sequestration rates. In the páramo, higher sequestration rates for AGB-C were found at higher elevations and associated with higher levels of growth-forms diversity. Temporal changes in BGB-C stocks on the contrary were non-significant. Our results indicated that terrestrial aboveground C sequestration rates might be an appropriate indicator for assessing Andean forest and páramo recovery after human disturbance. [ABSTRACT FROM AUTHOR]

Predicting future land change is crucial in anticipating societal and environmental impacts and informing responses at different scales. We designed an integrated, high-resolution, land-change model and forecasted Australia's land change for the years 2020, 2025 and 2030 for Cropland, Forest, Grassland, and Built-up land-uses using cloud-based and high-performance computing. A spatially explicit set of drivers was fed into a random forest classifier to generate 30-m per-class suitability layers for the country, which were then used for allocating land-use. The model was validated against 2015 data, then land-use was projected until 2030. Accuracy at the national level was ∼94%. Forecasts showed increases in Grassland and Built-up areas and decreases in Forest and Cropland. Our modelling framework expands the current capabilities of large-scale land-change models and provides a first-of-its-kind multiclass land forecast for Australia that can inform land policy at multiple scales in Australia. • We developed a continental-scale multiclass land-change model at 30-m resolution. • Land change for Australia was forecast for 2020, 2025 and 2030. • Overall accuracy at national level was 93.8% and not lower than 86% at State level. • Grassland and Built-up areas increased, while Forest and Cropland areas decreased. • The outputs provide new insights into future Australian land trajectories. [ABSTRACT FROM AUTHOR]

Computational and data handling limitations have constrained time-series analyses of land-cover change at high-spatial resolution over large (e.g., continental) extents. However, a new set of cloud-computing services offer an opportunity for improving knowledge of land change at finer grain. We constructed a historical set of seven high-resolution wall-to-wall land-cover maps at continental scale for Australia and analyzed temporal and spatial changes of land-cover from 1985 to 2015 at 5-year time-steps using Google Earth Engine (GEE). We used 281,962 Landsat scenes for producing median cloud-free composites at each time-step. We established a pseudo ground-truth dataset and used a PCA-based outlier detection method to reduce its uncertainty. A random forest model was trained at each time-step for classifying raw data into six land-cover classes: Cropland, Forest, Grassland, Built-up, Water, and Other areas, using 49 predictor datasets and nearly 20,000 training points. We further constructed uncertainty maps at each time-step as a proxy of per-pixel confidence. The average overall accuracy of the seven 30 m-resolution land-cover maps was ~93%. Built-up and Water areas displayed the highest user and producer accuracies (>93%), with Grasslands and Other areas slightly lower (~82–88%). Classification uncertainty was lower in more homogeneous landscapes (i.e., large expanses of a single land-cover class). Around 510,975 km2 (±69,877 km2) of land changed over the 30 years at an average of ~17,033 km2 yr−1 (±2329 km2 yr−1). Cropland and Forests declined by ~64,836 km2 (±16,437 km2) and ~ 152,492 km2 (±24,749 km2) over 30 years, mainly converting to Grassland. Built-up areas experienced the highest relative increases, increasing from 12,320 km2 in 1985 to 15,013 km2 in 2015 (~19.2%, ±3.1%). The sensitivity, i.e., proportion of pixels correctly classified as having changed, was over 96%, whereas the specificity, i.e., the proportion of pixels correctly classified as no-change, was over 68%. Numerous potential applications of these first-of-their-kind, detailed spatiotemporal maps of land use and land-change assessment exist spanning many areas of environmental impact assessment, policy, and management. Similarly, this methodological framework can provide a useful template for assessing continental-scale, high-resolution land dynamics more broadly. • We mapped Australia's land-cover at 30 m resolution every 5 years from 1985 to 2015. • Overall accuracy exceeded 90% and the sensitivity for representing change was >96%. • Land-cover changed at a rate of ~17,033 km2 yr−1 (±2329 km2 yr−1). • Forests and Cropland area declined, Built-up had the highest rate of increase. • Maps provide a new high-resolution time-series land-cover product for Australia. [ABSTRACT FROM AUTHOR]

Plot-based monitoring has yielded much information on the taxonomic diversity and carbon (C) storage in tropical lowland forests of the Amazon basin. This has resulted in an improved understanding of the relationship between lowland forest biomass dynamics and global change drivers, such as climate change and atmospheric CO2 concentration. Much less attention has been paid to the mountain ecosystems of South America that comprise montane forests and alpine vegetation (páramo, puna, high Andean grasslands, wetlands, and alpine heath). This vegetation complex provides a variety of ecosystem services and forms a natural laboratory along various physiographic, geological and evolutionary history/biogeography, and land use history gradients. Here, we review existing empirical understanding and model-based approaches to quantify the contribution of mountain ecosystems to ecosystem service provision in the rapidly changing socioecological setting of the South American mountains. The objective of this paper is to outline a broad road map for the implementation of mountain vegetation into dynamic global vegetation models (DGVM) for use in Earth System Models (ESM), based on our current understanding of their structure and function and of their responsiveness to global change drivers. We also identify treeline processes, critical in mountain ecosystems, as key missing elements in DGVMs/ESMs, and thus explore in addition a treeline model. Stocktaking of the availability of empirical data was undertaken from eight research sites along the Andes and in south-eastern Brazil. Out of eight sites, two (one each in Venezuela and Brazil) had some climate, ecological and ecophysiological data potentially suitable to parametrise a DGVM. Tree biomass data were available for six sites. A preliminary assessment of the Joint UK Land Environment Simulator (JULES) DGVM was made to identify gaps in available data and their impacts on model parametrisation and calibration. Additionally, the potential climate-determined elevation of the treeline was modelled to check the DGVM for its ability to identify the transition between the montane forest and alpine vegetation. Outcomes of the evaluation of the JULES land surface model identified the following key processes in montane forests: temperature-related decrease in net primary production, respiration, and allocation to above-ground biomass and increase in soil C stocks with elevation. There was a variable agreement between simulated biomass and those derived from field measurements via allometric equations. We identified major gaps between data availability and the needs for process-based modelling of South American mountain vegetation and its dynamics in DGVMs. To bridge this gap, we propose a transdisciplinary network, composed of members of the theoretical/modelling and empirical scientific communities, to study the natural dynamics of mountain ecosystems and their responses to global change drivers locally, regionally and at the continental scale, within a social-ecological system framework. The work presented here forms the basis for the design of data collection from field measurements and instrumental monitoring stations to parametrise and verify DGVMs. The network is designed to collaborate with and complement existing long-term research initiatives in the region and will adopt existing standard field protocols. Complementary protocols will ensure compatibility between field data collection and data needed for process-based and empirical models. [ABSTRACT FROM AUTHOR]

Aquatic insects in the order Trichoptera are extremely diverse in number of species and their trophic roles. However, their distribution and diversity patterns are poorly known in the Neotropics, including the species restricted to tropical mountain ecosystems. Recent studies in tropical mountains have shown high levels of endemism of aquatic insects and changes in the composition of communities over short distances. Still, the incidence of environmental filters that explain such patterns has not been addressed quantitatively. Given the relevance of understanding Trichoptera spatial diversity patterns to prioritize conservation areas for freshwaters, as well as to obtain baseline information to predict changes in aquatic communities facing global environmental changes, we assessed the species distribution and assemblages of caddisflies along an elevational gradient from 600 to 3,600 m a.s.l. on the equatorial Andes. In this area, we had long-term continuous climate data with hourly resolution. We collected adult caddisflies in seven localities along this gradient using light traps. We sampled each locality for two hours after sunset for three consecutive days. All specimens collected were identified to species or morphospecies. Our results showed an increase in species and genera numbers with decreasing altitude, albeit no significant. Minimum air temperature is the main environmental variable explaining Trichoptera community assemblages. β‐diversity (taxon turnover among sites), as opposed to species richness, increased with altitude and showed a bimodal distribution along the elevation gradient for both genera and species assemblages, which resulted in a significant shift in community composition of species and genera at 2,000 m a.s.l. Our null-models confirm the observed patterns of B-diversity are non-random and suggest a strong environmental filtering of tropical caddisflies community assemblies and turnover. Geographic distance coupled with changes in environmental conditions along the elevation gradient explained a high percentage of community variance, as documented for other taxa (e.g., vascular plants), suggesting the importance of securing habitat connectivity along the altitudinal gradient to protect aquatic insect diversity effectively. [ABSTRACT FROM AUTHOR]

Our knowledge about the structure and function of Andean forests at regional scales remains limited. Current initiatives to study forests over continental or global scales still have important geographical gaps, particularly in regions such as the tropical and subtropical Andes. In this study, we assessed patterns of structure and tree species diversity along ~ 4000 km of latitude and ~ 4000 m of elevation range in Andean forests. We used the Andean Forest Network (Red de Bosques Andinos, https://redbosques.condesan.org/) database which, at present, includes 491 forest plots (totaling 156.3 ha, ranging from 0.01 to 6 ha) representing a total of 86,964 identified tree stems ≥ 10 cm diameter at breast height belonging to 2341 identified species, 584 genera and 133 botanical families. Tree stem density and basal area increases with elevation while species richness decreases. Stem density and species richness both decrease with latitude. Subtropical forests have distinct tree species composition compared to those in the tropical region. In addition, floristic similarity of subtropical plots is between 13 to 16% while similarity between tropical forest plots is between 3% to 9%. Overall, plots ~ 0.5-ha or larger may be preferred for describing patterns at regional scales in order to avoid plot size effects. We highlight the need to promote collaboration and capacity building among researchers in the Andean region (i.e., South-South cooperation) in order to generate and synthesize information at regional scale. [ABSTRACT FROM AUTHOR]