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The Amazon River Basin's fish diversity is shaped by its dynamic flood-pulse system, critical for hydrological and ecological connectivity. This study examines the Napo Moist Forest (NMF) ecoregion, mapping permanent and seasonally flooded areas from 2018 to 2021 using remote sensing and deep learning models. We aimed to map these areas at a high spatial resolution (10-m pixels) and analyse their role in maintaining lateral connectivity essential for fish diversity. Using synthetic aperture radar data from Sentinel-1 combined with deep learning algorithms, we produced high-accuracy flood maps to assess landscape connectivity between rivers and floodplains. Our methodology included creating a ground truth dataset with the Normalized Difference Water Index and integrating high-resolution optical data for model training, overcoming challenges of cloud coverage and dense vegetation. Our predictive model achieved high accuracy (mean pixel accuracy = 97 %) and consistently predicted 4801 km² of surface water, with only 3 % (130 km²) being seasonally flooded areas over four years. The Caquetá, Bajo Marañón, Napo, and Pastaza watersheds accounted for nearly 60 % of the flooded areas, highlighting their ecological importance. Connectivity analysis in three areas of interest within the NMF ecoregion revealed important seasonal and interannual fluctuations in hydrological connectivity due to changes in flooded patch characteristics. Reductions in the number and flooded patch area during low water seasons increased the distance between patches, leading to a disconnection between flooded areas, channels and rivers. Despite hydrological fluctuations, certain patches maintained consistent flooding, critical for lateral connectivity and sustaining aquatic biodiversity. These seasonally flooded areas act as connectors, influencing patch dynamics and connectivity with tributaries. Seasonal and interannual variations in hydrological connectivity are crucial for sustaining fish diversity. Conserving dynamic floodplains supports migratory fish life cycles and biodiversity. This study underscores the importance of high-resolution temporal and spatial data in conservation planning for aquatic ecosystems.

Abstract State-of-the-art cloud computing platforms such as Google Earth Engine (GEE) enable regional-to-global land cover and land cover change mapping with machine learning algorithms. However, collection of high-quality training data, which is necessary for accurate land cover mapping, remains costly and labor-intensive. To address this need, we created a global database of nearly 2 million training units spanning the period from 1984 to 2020 for seven primary and nine secondary land cover classes. Our training data collection approach leveraged GEE and machine learning algorithms to ensure data quality and biogeographic representation. We sampled the spectral-temporal feature space from Landsat imagery to efficiently allocate training data across global ecoregions and incorporated publicly available and collaborator-provided datasets to our database. To reflect the underlying regional class distribution and post-disturbance landscapes, we strategically augmented the database. We used a machine learning-based cross-validation procedure to remove potentially mis-labeled training units. Our training database is relevant for a wide array of studies such as land cover change, agriculture, forestry, hydrology, urban development, among many others.

Patterns of species diversity have been associated with changes in climate across latitude and elevation. However, the ecological and evolutionary mechanisms underlying these relationships are still actively debated. Here, we present a complementary view of the well-known tropical niche conservatism (TNC) hypothesis, termed the multiple zones of origin (MZO) hypothesis, to explore mechanisms underlying latitudinal and elevational gradients of phylogenetic diversity in tree communities. The TNC hypothesis posits that most lineages originate in warmer, wetter, and less seasonal environments in the tropics and rarely colonize colder, drier, and more seasonal environments outside of the tropical lowlands, leading to higher phylogenetic diversity at lower latitudes and elevations. In contrast, the MZO hypothesis posits that lineages also originate in temperate environments and readily colonize similar environments in the tropical highlands, leading to lower phylogenetic diversity at lower latitudes and elevations. We tested these phylogenetic predictions using a combination of computer simulations and empirical analyses of tree communities in 245 forest plots located in six countries across the tropical and subtropical Andes. We estimated the phylogenetic diversity for each plot and regressed it against elevation and latitude. Our simulated and empirical results provide strong support for the MZO hypothesis. Phylogenetic diversity among co-occurring tree species increased with both latitude and elevation, suggesting an important influence on the historical dispersal of lineages with temperate origins into the tropical highlands. The mixing of different floras was likely favored by the formation of climatically suitable corridors for plant migration due to the Andean uplift. Accounting for the evolutionary history of plant communities helps to advance our knowledge of the drivers of tree community assembly along complex climatic gradients, and thus their likely responses to modern anthropogenic climate change.

Here, the authors investigate the aboveground carbon sink efficiency of Andean forests. The study shows the high potential of these forests to serve as future carbon refuges, and urges to reduce deforestation and increase restoration.

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.

Humans place strong pressure on land and have modified around 75% of Earth’s terrestrial surface. In this context, ecoregions and biomes, merely defined on the basis of their biophysical features, are incomplete characterizations of the territory. Land system science requires classification schemes that incorporate both social and biophysical dimensions. In this study, we generated spatially explicit social-ecological land system (SELS) typologies for South America with a hybrid methodology that combined data-driven spatial analysis with a knowledge-based evaluation by an interdisciplinary group of regional specialists. Our approach embraced a holistic consideration of the social-ecological land systems, gathering a dataset of 26 variables spanning across 7 dimensions: physical, biological, land cover, economic, demographic, political, and cultural. We identified 13 SELS nested in 5 larger social-ecological regions (SER). Each SELS was discussed and described by specific groups of specialists. Although 4 environmental and 1 socioeconomic variable explained most of the distribution of the coarse SER classification, a diversity of 15 other variables were shown to be essential for defining several SELS, highlighting specific features that differentiate them. The SELS spatial classification presented is a systematic and operative characterization of South American social-ecological land systems. We propose its use can contribute as a reference framework for a wide range of applications such as analyzing observations within larger contexts, designing system-specific solutions for sustainable development, and structuring hypothesis testing and comparisons across space. Similar efforts could be done elsewhere in the world.

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.

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.