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Remote sensing methods are commonly used to assess and monitor ecosystem conditions in drylands, but accurate classification and detection of ecological state change are challenging due to sparse vegetation cover, high spatial heterogeneity, and high interannual variability in production. We evaluated whether phenological metrics are effective for distinguishing dryland ecological states using imagery from near-surface camera (PhenoCam) and satellite (Harmonized Landsat 8 and Sentinel-2, hereafter HLS) sources, and how effectiveness varied across wet and dry rainfall years. We analyzed time series over 92 site-years at a site in southern New Mexico undergoing transitions from grassland to shrubland on different soil types. Rainfall was a driver of phenological response across all ecological states, with wet years correlating with later start of season, later peak, higher peak greenness, and shorter growing season. This rainfall response was strongest in shrub-invaded grasslands on sandy soils. PhenoCam estimated significantly earlier start of season than HLS for shrublands on gravelly soils and earlier end of season than HLS for shrub-invaded grasslands on sandy soils. We propose integrating seasonal metrics from high-frequency PhenoCam time series with satellite assessments to improve monitoring efforts in drylands, use phenological differences across variable rainfall years to measure differences in ecosystem function among states, and use the timing and strength of peak greenness of key plant functional groups (grasses in our study site) as an indicator of ecological state change. 

Introduction In dryland systems, biological soil crusts (biocrusts) can occupy large areas of plant interspaces, where they fix carbon following rain. Although distinct biocrust types contain different dominant photoautotrophs, few studies to date have documented carbon exchange over time from various biocrust types. This is especially true for gypsum soils. Our objective was to assess the carbon exchange of biocrust types established at the world’s largest gypsum dune field at White Sands National Park. Methods We sampled five different biocrust types from a sand sheet location in three different years and seasons (summer 2020, fall 2021, and winter 2022) for carbon exchange measurements in controlled lab conditions. Biocrusts were rehydrated to full saturation and light incubated for 30 min, 2, 6, 12, 24, and 36 h. Samples were then subject to a 12-point light regime with a LI-6400XT photosynthesis system to determine carbon exchange. Results Biocrust carbon exchange values differed by biocrust type, by incubation time since wetting, and by date of field sampling. Lichens and mosses had higher gross and net carbon fixation rates than dark and light cyanobacterial crusts. High respiration rates were found after 0.5 h and 2 h incubation times as communities recovered from desiccation, leveling off after 6 h incubation. Net carbon fixation of all types increased with longer incubation time, primarily as a result of decreasing respiration, which suggests rapid recovery of biocrust photosynthesis across types. However, net carbon fixation rates varied from year to year, likely as a product of time since the last rain event and environmental conditions preceding collection, with moss crusts being most sensitive to environmental stress at our study sites. Discussion Given the complexity of patterns discovered in our study, it is especially important to consider a multitude of factors when comparing biocrust carbon exchange rates across studies. Understanding the dynamics of biocrust carbon fixation in distinct crust types will enable greater precision of carbon cycling models and improved forecasting of impacts of global climate change on dryland carbon cycling and ecosystem functioning. 

Introduction Soil microbial communities, including biological soil crust microbiomes, play key roles in water, carbon and nitrogen cycling, biological weathering, and other nutrient releasing processes of desert ecosystems. However, our knowledge of microbial distribution patterns and ecological drivers is still poor, especially so for the Chihuahuan Desert. Methods This project investigated the effects of trampling disturbance on surface soil microbiomes, explored community composition and structure, and related patterns to abiotic and biotic landscape characteristics within the Chihuahuan Desert biome. Composite soil samples were collected in disturbed and undisturbed areas of 15 long-term ecological research plots in the Jornada Basin, New Mexico. Microbial diversity of cross-domain microbial groups (total Bacteria, Cyanobacteria, Archaea, and Fungi) was obtained via DNA amplicon metabarcode sequencing. Sequence data were related to landscape characteristics including vegetation type, landforms, ecological site and state as well as soil properties including gravel content, soil texture, pH, and electrical conductivity. Results Filamentous Cyanobacteria dominated the photoautotrophic community while Proteobacteria and Actinobacteria dominated among the heterotrophic bacteria. Thaumarchaeota were the most abundant Archaea and drought adapted taxa in Dothideomycetes and Agaricomycetes were most abundant fungi in the soil surface microbiomes. Apart from richness within Archaea ( p  = 0.0124), disturbed samples did not differ from undisturbed samples with respect to alpha diversity and community composition ( p  ≥ 0.05), possibly due to a lack of frequent or impactful disturbance. Vegetation type and landform showed differences in richness of Bacteria, Archaea, and Cyanobacteria but not in Fungi. Richness lacked strong relationships with soil variables. Landscape features including parent material, vegetation type, landform type, and ecological sites and states, exhibited stronger influence on relative abundances and microbial community composition than on alpha diversity, especially for Cyanobacteria and Fungi. Soil texture, moisture, pH, electrical conductivity, lichen cover, and perennial plant biomass correlated strongly with microbial community gradients detected in NMDS ordinations. Discussion Our study provides first comprehensive insights into the relationships between landscape characteristics, associated soil properties, and cross-domain soil microbiomes in the Chihuahuan Desert. Our findings will inform land management and restoration efforts and aid in the understanding of processes such as desertification and state transitioning, which represent urgent ecological and economical challenges in drylands around the world. 

Abstract Prediction of abrupt ecosystem transitions resulting from climatic change will be an essential element of adaptation strategies in the coming decades. In the arid southwest USA, the collapse and recovery of long‐lived perennial grasses have important effects on ecosystem services, but the causes of these variations have been poorly understood. Here we use a quality‐controlled vegetation monitoring dataset initiated in 1915 to show that grass cover dynamics during the 20th century were closely correlated to the Pacific decadal oscillation (PDO) index. The relationship out‐performed models correlating grasses to yearly precipitation and drought indices, suggesting that ecosystem transitions attributed only to local disturbances were instead influenced by climate teleconnections. Shifts in PDO phase over time were associated with the persistent loss of core grass species and recovery of transient species, so recovery of grasses in aggregate concealed significant changes in species composition. However, the relationship between PDO and grass cover broke down after 1995; grass cover is consistently lower than PDO would predict. The decoupling of grass cover from the PDO suggests that a threshold had been crossed in which warming or land degradation overwhelmed the ability of any grass species to recover during favorable periods. 

ABSTRACT MotivationHere, we make available a second version of the BioTIME database, which compiles records of abundance estimates for species in sample events of ecological assemblages through time. The updated version expands version 1.0 of the database by doubling the number of studies and includes substantial additional curation to the taxonomic accuracy of the records, as well as the metadata. Moreover, we now provide an R package (BioTIMEr) to facilitate use of the database. Main Types of Variables IncludedThe database is composed of one main data table containing the abundance records and 11 metadata tables. The data are organised in a hierarchy of scales where 11,989,233 records are nested in 1,603,067 sample events, from 553,253 sampling locations, which are nested in 708 studies. A study is defined as a sampling methodology applied to an assemblage for a minimum of 2 years. Spatial Location and GrainSampling locations in BioTIME are distributed across the planet, including marine, terrestrial and freshwater realms. Spatial grain size and extent vary across studies depending on sampling methodology. We recommend gridding of sampling locations into areas of consistent size. Time Period and GrainThe earliest time series in BioTIME start in 1874, and the most recent records are from 2023. Temporal grain and duration vary across studies. We recommend doing sample‐level rarefaction to ensure consistent sampling effort through time before calculating any diversity metric. Major Taxa and Level of MeasurementThe database includes any eukaryotic taxa, with a combined total of 56,400 taxa. Software Formatcsv and. SQL.