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Aquatic Functional Biodiversity - 1st Edition

The grid allows geographic space to be collapsed into one dimension with, for example, neighbouring cells arranged in roughly sequential order along the vertical axis and over time for graphical presentation. This species—space—time—gram enables the visual characterization of available data for the two dimensions space and time and multiple species. Cells can be spatially aggregated at governance scales, such as regions, countries or counties and, potentially, themselves be politically defined. In our example circled numbers on both sides of the figure , an expert synthesis map 1 characterizes the static species ranges for the year period and is deemed by experts to reliably separate presence green and absence orange for the chosen space—time grid.

This is complemented by two large-area inventories 2 , developed before the year , indicating — with little spatiotemporal specificity — species presence in parts of Country C, and likely absence in all of Country A. Two sets of small-area inventories exist, including single-year visits 3 and multi-year atlas efforts 4 , all providing spatiotemporally explicit evidence that may hold reliable absence information. Finally, multiple incidental records 5 provide presence data for select grid cells and years.

Functional ecology

Assuming, for now, a high suitability of small-scale inventories for grid-level absence inference, the raw occurrence data does enable the detection of local extinctions i or non-native occurrence or invasions ii. These are single records that lack information about co-observed species, taxonomic scope and, usually, sampling protocol, such as most museum records and many citizen-science contributions. Thanks to increasing amateur data collection 24 , 27 , advance of animal tracking 43 , 44 , activities of aggregators like the Global Biodiversity Information Facility GBIF and the Ocean Biogeographic Information System OBIS , existing protocols facilitating sharing and interoperability DarwinCore 37 , and easy-to-use modelling tools 45 , this data type and its direct use have seen strong growth.

However, major taxonomic, ecological and geographic biases in data availability still exist 31 , 46 , impeding straightforward interpretation. This data type differs in two key aspects — it has a defined taxonomic and spatiotemporal scope that is larger than that of a single observed individual or species Fig. Inventories may address all members of a taxon for example, mammals or bryophytes or a group defined in another way for example, trees or phytoplankton above a certain size.

Such non-detections can provide information about absences, but the reliability of such inference depends on the overall survey effort and effectiveness and sampling protocol. For small-area inventories addressing relatively immobile or readily detected organisms, such as vegetation plots or a short survey transect, a given effort may provide very spatiotemporally specific evidence and potentially reliable absences. Depending on effort and rigor, they may provide both reliable presence and absence information, but usually with limited spatial or temporal specificity.

Despite the fundamental role of inventories for ascertaining potential absences, especially in the context of growing modelling methodology 47 , they have seen limited mobilization and integration with other data types. Key past causes for this include the lack of 1 data and metadata, 2 infrastructure to facilitate the capture and use of this information and 3 community-wide appreciation and incentives for data and metadata sharing.

With a prototype inventory data standard Humboldt Core 38 and associated informatics tools at Map of Life and format extensions at GBIF and OBIS in place, future growth in the capture of effort and complete metadata looks promising. These are binary or categorical distribution maps that are developed by species experts. They aim to separate coarsely occupied areas from those without species occurrence and typically cover a longer timeframe, often decades rather than years 48 , Similar to large-area inventories but addressing only a single species at a time, they usually summarize multiple sources and data types from multiple time points with details and provenance usually not retained.

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These data are based on by-region expert determinations or are interpolated to inform occurrence boundaries that are often hand-drawn based on sources including taxonomic monographs, handbooks, large-scale field guides and conservation reports. Such expert predictions are now sometimes quantitatively supported by species distribution modelling 50 , 51 , 52 , 53 , 54 with pixel suitability scores subsequently thresholded, masked or otherwise modified by the expert to exclude areas presumed to be unoccupied This often implies broad temporal scope and thus limited opportunity for direct change inference and a lack of spatial detail: hand-drawn presence—absence boundaries may have substantial inaccuracies, often include substantial areas of false presence and sometimes also include false absences 49 , 56 , However, other data may allow validation of a reliable spatial grain for this data type to be used for presence—absence information, for example ca.

General reasons include sparse data and taxonomically, spatially and environmentally uneven coverage 31 ; ecological, environmental or phenological variation in species detectability 59 ; and highly heterogeneous spatial and temporal grains of available data. Thus, on their own, raw data fail some or all of our initially formulated four key criteria for SP EBVs.

Status and trend metrics and indicators that are mere aggregates of raw data are likely to be biased and sub-optimal and potentially may mislead downstream inference. This can be addressed through the use of spatiotemporally contiguous environmental and other species-level covariates in a statistical framework Fig. Observations representing different data types collected over heterogeneous spatiotemporal scales are unified in a space—time analysis grid or, in aggregate form, a species—space—time cube in which cells represent a model-based measure of presence or abundance.

Individual cells may refer to space of any dimensionality, including linear or three-dimensional in the case of aquatic habitats. Cell size would ideally represent the relevant scale of occurrence or change processes 60 or, more operationally, be adaptively driven by available data, intended output and acceptable uncertainty see below and may thus vary by species group. Applied contiguously over an extent encompassing the geographic ranges of species in the taxonomic scope, this enables assessment and monitoring of occupied areas and associated statistical signals of local, regional and global change Fig.

Bottom, Heterogeneous occurrence data, remotely sensed environmental conditions and ecological species attributes facilitate global predictions of species distributions in space and time. For example, when data are aggregated for a single species, the SD EBV measures changes in distribution or population size. Centre, When data are aggregated for single cells, the SD EBV informs about community change in, for example, species richness or compositional similarity, or — via ancillary data — functional or phylogenetic turnover.

On their own, or combined with ancillary data on species or locations as well as EBVs from different classes, the SP EBVs underpin a large range of uses in policy, conservation, management, research and society. Inspired by earlier work 42 , the species—space—time—gram cube concept and graph were originally developed by the authors of the present work and then shared with the GEO BON community in We thus define the essential biodiversity variable for species distributions SD EBV as the probability of occurrence over contiguous spatial and temporal units addressing the global extent of a species group consisting of one to many members.

With support from models, this space—time—species cube is characterized for all members of a taxonomically or ecologically defined species group over their respective global extent, with a cell size that is potentially variable. Together, they and their potential spatial aggregates or combinations with species attributes see below represent the species populations EBV class, as in ref. This conceptualization of the SP EBVs is uniquely facilitated by the environmental data revolution, specifically the availability of worldwide high-resolution remote sensing products. The fine spatial and temporal resolution of data allows an environmental characterization of biodiversity data at spatiotemporal grains near that of in situ records.

This enables an increasingly scale-conscious niche capture and provides critical spatiotemporal sensitivity and flexibility for inferring and predicting distributions and their change 62 , And these environmental measurements are increasingly relevant for species population processes, addressing biological drivers such as land cover, topographic and habitat heterogeneity, fine-scale weather variation, plant functional traits or productivity 64 , 65 , 66 , 67 , 68 and, in select cases, even species directly 63 , 69 , Data across the depth-gradient for the oceans are more limited, but the first near-global distribution modeling 71 , 72 , 73 and the first near-global characterizations of freshwater conditions are emerging The underlying modelling concepts and techniques supporting our approach are based broadly on species distribution models 50 , 51 , 52 , 54 , which identify the environmental conditions associated with species occurrence and allow their mapping in space.

Dispersal and biotic constraints limit actual distributions, and for SP EBVs, predictions of the realized niche in the existing biological and spatial template is required, rather than the fundamental niche, which is more appropriate for projection into different time and space Expert-assessed, data-driven or phylogenetically inferred biotic species dependencies can be linked to hosts, prey, predators or other actors to inform distribution or abundance predictions 78 , These methods are particularly promising for the strength of information gained across species in the face of limited occurrence data.

This temporal stationarity assumption represents a key constraint for assessing temporal change With sufficient data, a more appropriate, yet somewhat inefficient, approach is to run separate models for different time periods. More desirable are models that are fit across the entire spatial and temporal scope of available data and that explicitly address spatiotemporal co-dependencies and signals of change This is addressed by dynamic distribution or dynamic occupancy models that set out to parameterize and predict variation in occurrence jointly in space and time 82 , 87 , 88 , 89 , 90 as well as assemblage dissimilarity models applied to temporal turnover with no explicit consideration of species-level patterns or separation of spatial and temporal drivers 91 , The first large-scale demonstrations of dynamic occupancy approaches with spatiotemporal change assessment are now emerging 93 , We see great potential to extend and implement these methods as the backbone for addressing species occurrence in contiguous space and time.

The vast majority of species distribution prediction efforts to date are based on presence-only models using environmental covariates alone. This constrains delineation of non-environmental distribution limits, such as past or current physical or ecological barriers 95 , With sufficient data, this limitation can be addressed through hierarchical spatial models 97 or alternatively by combining presences with an expert synthesis map data to restrict model predictions Presence-only models are limited in that they only provide relative cell suitability and use binary presence—absence thresholds contingent on ancillary information, expert judgment and assumptions 99 , We suggest that here the inclusion of inventory data will be critical and lead to a new generation of species distribution modelling approaches.

Inventory data implicitly provide information on species absences and, through the use of an occupancy modelling framework 40 , enable the assessment of species- and environment-specific detection probabilities , and a quantification of absolute occurrence probabilities , While repeated sampling following a standardized protocol may be ideal, such data is obviously often limited to few or unrepresentative species and regions. We therefore argue that in many cases, alternative or even self-assessed information on the completeness of an inventory, and implicitly the level of detection or absence information afforded by it, may strengthen occurrence predictions We highlight the need for more statistical work to address this potential and acknowledge that species with extremely limited observational data will continue to pose a challenge, especially for trend and aggregate assemblage metrics.

click here Combined with in situ abundance data or size estimates of home range, the same framework can address abundance , Point process models in particular unify distribution and abundance predictions and seem especially suited to be a statistical framework 39 , , , in principle enabling smooth transition between the SD EBV and SA EBV. A key challenge for model-based integration, exacerbating the known issue of gaps and biases in spatial biodiversity data, is the heterogeneity of taxa and sampling methods and of spatial and temporal grain of available data.

Notably, presence and absence evidence have fundamentally different spatiotemporal grain properties. The presence of a single individual for a given place and time automatically implies the presence of the species for all larger spatial or temporal units that contain it. In contrast, an observed absence at the level of a small plot or short trawl does not imply absence at the level of an encompassing coarser-grain grid cell.

Conserving Biodiversity: A Global Priority

Equally, for mobile organisms a reliable absence during a weeklong survey does not imply absence in that year. Hierarchical statistical models, often using Bayesian approaches, have been developed specifically to address the cross-type and cross-scale nature of occurrence data and used to combine inventory and incidental data or data from disparate spatial scales Such approaches enable predictions at a common spatial and hypothetically temporal resolution , that is, the up- or downscaling of underlying heterogeneous data to a single spatiotemporal prediction grid for both species distribution and abundance , The issue of scale is intimately connected to that of uncertainty, as occurrence at the continental or centennial scale is naturally fraught with less uncertainty than that at the km or annual scale.

For most uses, predictions at finer scales are preferred as long as uncertainty is captured, which is increasingly being facilitated by Bayesian and related modelling techniques , We consider the capture, reporting, spatial visualization and cascading of uncertainty into aggregate products as key for supporting effective data collection and sound policy and management decisions. We note that the interconnections between the scale of process, evidence and predictions and the trade-offs between scale, uncertainty and sensitivity are key areas in need of further research.

The envisioned essential species distribution information, or SD EBV, offers an exceptional breadth of applications in biodiversity and ecosystem monitoring and assessment Fig.

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Consider the idealized case of data and models providing annual occurrence probabilities and associated uncertainties for hundreds of species globally over a medium spatial grid sized at km, 10 km or even 1 km. Such an empirically driven SD EBV enables the monitoring of species distribution dynamics contractions, expansions and redistributions and of the sizes and levels of fragmentation of geographic ranges. For any cell location, it provides information about community richness, composition and its change thus addressing variables in the community EBV class sensu 6 , including immigration and loss of native species.

When aggregated with data from regions or the globe, it offers compound characterizations of both species and community change in a larger-scale context and is thus able to directly inform global indicators of change, such as the suite of GEO-BON-endorsed biodiversity indicators Ancillary data on species and places allow for enriched characterizations.

The SD EBV joined with data on traits or functional roles of species may, for example, support inference about functional biodiversity losses 10 , or the potential ecological impacts of species invasions Combined with species-level estimates of life history and home range sizes, the SD EBV has the potential to support more temporally sensitive and accurate estimates of species extinction risk.

Linking in spatial data on environmental change enables the identification of drivers of change. When combined with spatial protected area information, the SD EBV can support monitoring of progress for international biodiversity conservation targets or help identify new conservation opportunities, including in support of the Half-Earth Project or related aspirations. Extending the SD EBV to address abundance estimates for the same space—time—species cells, the SA EBV can offer even greater ecological and conservation relevance for example, see refs. Where attributes have high intraspecific variation, for example, due to local adaptation, these extended uses of SD EBV and SA EBV will benefit from in situ species-, community-, or even ecosystem-level measurements that can offer vital local detail.


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At the heart of the SP EBVs is the recognition of harmonization and model-based integration of multiple types of biodiversity and environmental data from heterogeneous sources that address different scales and are stored in multiple formats. This requires workflows that connect data to models in order to produce and disseminate credible and transparent modelled products. Such workflows necessitate a network of tools and infrastructure to address four main steps Fig. Unfortunately, much data still remain unavailable owing to lack of sharing or restrictive licensing, but data contributions are facilitated by a range of platforms for a review of examples and associated standard and workflow issues, see ref.

For incidental records, data and metadata standards are enabling such data to effectively support EBV development.

Such global networks are important infrastructures to ensure availability, repeatability, standardization and archiving in support of downstream data integration and use However, still nascent and of immediate need is infrastructure that can play a similar role for more complex data types, such as select inventory data that require detailed metadata to most effectively feed into models, data-type or model-focused effort.