Community structure of benthic invertebrates in the Allipén River basin, North Patagonia, Araucania region (38º S, Chile)

One of the biological indicators most used to determine the health of a fluvial ecosystem are the benthic macroinvertebrates. The presence of recurrent species in a wide gradient of latitudes, dominates the biogeographic pattern of the benthic macroinvertebrates in Chilean fresh waters, nevertheless the knowledge on the communitarian ecology of these in the Chilean rivers continues to be scarce. Null models became a powerful statistical tool for describing the ecological mechanisms that drive the structure of an ecological community and the underlying patterns of diversity. The objective of this study was to determine the community structure of benthic invertebrates in the Allipén River by describing their composition, richness and abundance of species through richness models and null models based on presence/absence. The results reveal a high family richness and low diversity, three phyla, five classes, 11 orders and 28 families were identified in the study area during the four seasons of the year. The Arthropoda phylum was the most representative in abundance and richness. Regarding to richness, Trichoptera (7 families) and Diptera (6 families) followed by Ephemeroptera (3 families) were the orders that showed the greatest diversity of families, however, a low diversity with a H’≤ 1.5 nit was registered in the study area. We demonstrated through the null models, the randomization in the species associations corresponding to the three analyzed sites. The information provided here contributes to the understanding of the ecological patterns of the invertebrate communities in the Allipén River, establishing the basis for more complex ecological studies.

There and Back to the Present: An Evolutionary Tale on Biological Diversity

Ecologists often agree on the importance of macroevolution for niche-mediated distribution of biological diversity along environmental gradients. Yet, macroevolutionary diversification and dispersal in time and space generate uneven geographic distribution of phylogenetic pools, which affects the imprint let by macroevolution on local species pools. In this article we introduce an individual-based simulation approach coupled to Approximate Bayesian Computation (ABC) that allows to parameterize the adaptation rate of species niche positions along the evolution of a monophyletic lineage, and the intensity of dispersal limitation, associated with the distribution of biological diversity between assemblages potentially connected by dispersal (metacommunity). The analytical tool was implemented in an R package called mcfly. We evaluated the statistical performance of the analytical framework using simulated datasets, which confirmed the suitability of the analysis to estimate adaptation rate and dispersal limitation parameters. Further, we evaluated the role played by niche evolution and dispersal limitation on species diversity distribution of Phyllostomidae bats across the Neotropics. The framework proposed here shed light on the links between niche evolution, dispersal limitation and the distribution of biological diversity, and thereby improved our understanding of evolutionary imprints on ecological patterns. Perhaps more importantly, it offers new possibilities for solving the eco-evolutionary puzzle.

Mesoscale bird distribution pattern in montane phytophysiognomies along an ecotone between two hotspots

Brazil has a high diversity of birds and presents the largest number of threatened bird species in the neotropical region. Even so, there are gaps in the bird knowledge, especially in ecotonal montane regions. Given this panorama, this study aimed to analyse the bird community distribution (richness, composition, and beta diversity between phytophysiognomies) of an ecotonal montane landscape of southeastern Brazil, with the purpose of detecting substitution patterns of bird species on a meso-scale. Using bird data performed during the years 1998 to 2015 in 46 sampling points, we found high bird richness in montane phytophysiognomies along an ecotone between Cerrado and Atlantic Forest hotspots. The composition present species of both domains, with high turnover component. We highlight the field environments and candeais are considered homogeneous and threathened, which would directly affect birds. The present study contributes to future conservation strategies, as it demonstrates ecotonal regions as transition zones and reinforces the need to consider as particular ecological units. These ecotonal regions are key locations for understanding ecological patterns in response to environmental changes or phytophysiognomies. Knowing how partitioning of the composition occurs within an environmental mosaic is essential to understand the limits and distributions of the species and conserve them.

Minnesota peat viromes reveal terrestrial and aquatic niche partitioning for local and global viral populations

Background Peatlands are expected to experience sustained yet fluctuating higher temperatures due to climate change, leading to increased microbial activity and greenhouse gas emissions. Despite mounting evidence for viral contributions to these processes in peatlands underlain with permafrost, little is known about viruses in other peatlands. More generally, soil viral biogeography and its potential drivers are poorly understood at both local and global scales. Here, 87 metagenomes and five viral size-fraction metagenomes (viromes) from a boreal peatland in northern Minnesota (the SPRUCE whole-ecosystem warming experiment and surrounding bog) were analyzed for dsDNA viral community ecological patterns, and the recovered viral populations (vOTUs) were compared with our curated PIGEON database of 266,125 vOTUs from diverse ecosystems. Results Within the SPRUCE experiment, viral community composition was significantly correlated with peat depth, water content, and carbon chemistry, including CH4 and CO2 concentrations, but not with temperature during the first 2 years of warming treatments. Peat vOTUs with aquatic-like signatures (shared predicted protein content with marine and/or freshwater vOTUs) were significantly enriched in more waterlogged surface peat depths. Predicted host ranges for SPRUCE vOTUs were relatively narrow, generally within a single bacterial genus. Of the 4326 SPRUCE vOTUs, 164 were previously detected in other soils, mostly peatlands. None of the previously identified 202,371 marine and freshwater vOTUs in our PIGEON database were detected in SPRUCE peat, but 0.4% of 80,714 viral clusters (VCs, grouped by predicted protein content) were shared between soil and aquatic environments. On a per-sample basis, vOTU recovery was 32 times higher from viromes compared with total metagenomes. Conclusions Results suggest strong viral “species” boundaries between terrestrial and aquatic ecosystems and to some extent between peat and other soils, with differences less pronounced at higher taxonomic levels. The significant enrichment of aquatic-like vOTUs in more waterlogged peat suggests that viruses may also exhibit niche partitioning on more local scales. These patterns are presumably driven in part by host ecology, consistent with the predicted narrow host ranges. Although more samples and increased sequencing depth improved vOTU recovery from total metagenomes, the substantially higher per-sample vOTU recovery after viral particle enrichment highlights the utility of soil viromics.

A History of Open Weather in New Zealand (HOWNZ): an open access 1-km resolution monthly 1910–2019 time-series of interpolated temperature and rainfall grids with associated uncertainty

Long time-series of weather grids are fundamental to understanding how weather affects environmental or ecological patterns and processes such as plant distributions, plant and animal phenology, wildfires, and hydrology. Ideally such weather grids should be openly available and be associated with uncertainties so that users can understand any data quality issues. We present a History of Open Weather in New Zealand (HOWNZ) that uses climatological aided natural neighbour interpolation to provide monthly 1-km resolution grids of total rainfall, mean air temperature, mean daily maximum air temperature, and mean daily minimum air temperature across New Zealand from 1910 to 2019. HOWNZ matches the best available temporal extent and spatial resolution of any open weather grids that include New Zealand, and is unique in providing associated spatial uncertainty in appropriate units of measurement. The HOWNZ weather and uncertainty grids capture the dynamic spatial and temporal nature of the monthly weather variables and the uncertainty associated with the interpolation. We also demonstrate how to quantify and visualise temporal trends across New Zealand that recognise the temporal and spatial variation of uncertainties in the HOWNZ data. The HOWNZ data is openly available at https://doi.org/10.7931/zmvz-xf30 (Etherington et al., 2021).

Soil eukaryote community shift but not composition is consistently recovered by different OTU inference methods applied to long read metabarcoding data

Long amplicon metabarcoding has opened the door for phylogenetic analysis of the largely unknown communites of microeukaryotes in soil. Here, we amplified and sequenced the ITS and LSU regions of the rDNA operon (around 1500 bp) from grassland soils using PacBio SMRT sequencing and evaluated the performance of three different methods for generation of operational taxonomic units (OTUs). The field site at Kungsängen Nature Reserve has drawn frequent visitors since Linnaeus’s time, and its species rich vegetation includes the largest population of Fritillaria meleagris in Sweden. To test the effect of different OTU generation methods, we sampled soils across an abrupt moisture transition that divides the meadow community into a Carex acuta dominated plant community with low species richness in the wetter part, which is visually distinct from the mesic-dry part that has a species rich grass-dominated plant community including a high frequency of F. meleagris. We used the moisture and plant community transition as a framework to investigare how detected belowground microeukaryotic community composition was influenced by OTU generation methods. Soil communities in both moisture regimes were dominated by protists, a large fraction of which were taxonomically assigned to Ciliophora (Alveolata) while 30-40% of all reads were assigned to kingdom Fungi. Ecological patterns were consistently recovered irrespective of OTU generation method used. However, different methods strongly affect richness estimates and the taxonomic and phylogenetic resolution of the characterized community with implications for how well members of the microeukaryotic communities can be recognized in the data.

Machine Learning Undercounts Reproductive Organs on Herbarium Specimens but Accurately Derives Their Quantitative Phenological Status: A Case Study of Streptanthus tortuosus

Machine learning (ML) can accelerate the extraction of phenological data from herbarium specimens; however, no studies have assessed whether ML-derived phenological data can be used reliably to evaluate ecological patterns. In this study, 709 herbarium specimens representing a widespread annual herb, Streptanthus tortuosus, were scored both manually by human observers and by a mask R-CNN object detection model to (1) evaluate the concordance between ML and manually-derived phenological data and (2) determine whether ML-derived data can be used to reliably assess phenological patterns. The ML model generally underestimated the number of reproductive structures present on each specimen; however, when these counts were used to provide a quantitative estimate of the phenological stage of plants on a given sheet (i.e., the phenological index or PI), the ML and manually-derived PI’s were highly concordant. Moreover, herbarium specimen age had no effect on the estimated PI of a given sheet. Finally, including ML-derived PIs as predictor variables in phenological models produced estimates of the phenological sensitivity of this species to climate, temporal shifts in flowering time, and the rate of phenological progression that are indistinguishable from those produced by models based on data provided by human observers. This study demonstrates that phenological data extracted using machine learning can be used reliably to estimate the phenological stage of herbarium specimens and to detect phenological patterns.

Study of the diversity and mapping of xerophytic vegetation of the southeastern coast of Crimea peninsula using remote sensing data

The study of diversity and ecological-phytocenotic mapping of the vegetation cover was carried out at the key area located in the eastern part of the Echki-Dag mountain range in the territory of the Lisya Bay Reserve (Eastern Crimea). A generalization of the classification and ecological patterns of vegetation was carried out to create a legend for a large-scale ecological-phytocenotic cartographic model (at the scale of 1:10000). The ecological-geomorphological series and combinations of xerophytic and mesoxerophytic plant communities indicating erosion-denudation processes on different substrates of the underlying parent rocks were the thematic basis of the cartographic model. The developed legend is based on the units of vegetation of the association rank obtained using the Braun – Blanquet method combined into ecological series in accordance with their position on the gradients of the leading ecological factors as well as on the hierarchy of phytochories determined by the categories of erosion-denudation relief of coastal slopes. The resulting vegetation map demonstrates the main regularities of the regional phyto-diversity and serves as the basis for assessing the resource potential of vegetation, its landscape-stabilizing and nature conservation value.