sPlotOpen – An environmentally balanced, open‐access, global dataset of vegetation plots

Abstract

Motivation Assessing biodiversity status and trends in plant communities is critical for understanding, quantifying and predicting the effects of global change on ecosystems. Vegetation plots record the occurrence or abundance of all plant species co‐occurring within delimited local areas. This allows species absences to be inferred, information seldom provided by existing global plant datasets. Although many vegetation plots have been recorded, most are not available to the global research community. A recent initiative, called ‘sPlot’, compiled the first global vegetation plot database, and continues to grow and curate it. The sPlot database, however, is extremely unbalanced spatially and environmentally, and is not open‐access. Here, we address both these issues by (a) resampling the vegetation plots using several environmental variables as sampling strata and (b) securing permission from data holders of 105 local‐to‐regional datasets to openly release data. We thus present sPlotOpen, the largest open‐access dataset of vegetation plots ever released. sPlotOpen can be used to explore global diversity at the plant community level, as ground truth data in remote sensing applications, or as a baseline for biodiversity monitoring. Main types of variable contained Vegetation plots (n = 95,104) recording cover or abundance of naturally co‐occurring vascular plant species within delimited areas. sPlotOpen contains three partially overlapping resampled datasets (c. 50,000 plots each), to be used as replicates in global analyses. Besides geographical location, date, plot size, biome, elevation, slope, aspect, vegetation type, naturalness, coverage of various vegetation layers, and source dataset, plot‐level data also include community‐weighted means and variances of 18 plant functional traits from the TRY Plant Trait Database. Spatial location and grain Global, 0.01–40,000 m². Time period and grain 1888–2015, recording dates. Major taxa and level of measurement 42,677 vascular plant taxa, plot‐level records. Software format Three main matrices (.csv), relationally linked.

Estimating alpha, beta, and gamma diversity through deep learning

“…The values of alpha, beta, and gamma diversity used in this study to train the neural network models were derived from vegetation plot data (species inventories). We downloaded these data from the sPlotOpen database (Sabatini et al, 2021), only using plots where all vascular plants had been assessed. This resulted in a total of 7,896 vegetation plots for Australia (Fig.…”

“…With the increasing availability of continental and global vegetation plot databases (Chytrý et al, 2016; Bruelheide et al, 2019; Sabatini et al, 2021), a new data source with extended spatial coverage has become widely available for the task of large-scale diversity estimation. Recently, Večeřa et al (2019) showed the potential of machine learning models (random forest models) to estimate the expected diversity for fixed size vegetation plots (alpha diversity), based on climatic and other predictors, when trained on alpha diversity data from vegetation plot databases.…”

“…We selected plot-based vegetation survey data from Australia (vascular plants; Tracheophyta) to empirically test the effectiveness of these neural network models to predict diversity patterns and validate our methodology. Australia, as an island continent, has the advantage of a clear delimitation of natural boundaries; it has high natural diversity and uneven biological sampling (González-Orozco et al, 2014; Cook et al, 2015; Laffan et al, 2016); high spatial heterogeneity with well-defined and contrasting biomes (Byrne et al, 2008, 2011; Macintyre and Mucina, 2021); a relatively well-documented vascular flora with reliable national databases (https://avh.chah.org.au/; Sparrow et al, 2021) that feed into the Global Biodiversity Information Facility (GBIF, gbif.org); good climatic data (http://www.bom.gov.au/climate/data/); and a large number of freely available plot-based vegetation records suitable for training deep learning frameworks (Sabatini et al, 2021).…”

“…We selected plot-based vegetation survey data from Australia (vascular plants; Tracheophyta) to empirically test the effectiveness of these neural network models to predict diversity patterns and validate our methodology. Australia, as an island continent, has the advantage of a clear delimitation of natural boundaries; it has high natural diversity and uneven biological sampling (González-Orozco et al, 2014;Cook et al, 2015;Laffan et al, 2016); high spatial heterogeneity with well-defined and contrasting biomes (Byrne et al, 2008(Byrne et al, , 2011Macintyre and Mucina, 2021) (Sabatini et al, 2021).…”

LOTVS: a global collection of permanent vegetation plots

“…In recent decades, vegetation science has benefited from the development and maintenance of large vegetation databases (Dengler et al 2011). Historical vegetation relevés performed by early vegetation ecologists, together with recent vegetation plot data stemming from regional, but also national or continental research and survey projects, have been carefully assembled and digitally archived in the context of centralized initiatives (Chytrý et al 2016;Wiser 2016;Bruelheide et al 2019;Sabatini et al 2021). Such global collections of vegetation plot data are essential to investigate macroecological patterns and provide spatially meaningful answers to global issues, i.e.…”