skip to main content

This content will become publicly available on October 12, 2024

Title: Subnational biodiversity reporting metrics for mountain ecosystems

Biodiversity indicators are used to assess progress towards conservation and sustainability goals. However, the spatial scales, methods and assumptions of the underlying reporting metrics can affect the provided information. Using mountain ecosystems as an example, we compare biodiversity protection at subnational scale using the site-based approach of the 2030 Agenda for Sustainable Development (SDG indicator 15.4.1) with an area-based approach compatible with the targets of the Kunming–Montreal Global Biodiversity Framework.

more » « less
Award ID(s):
Author(s) / Creator(s):
; ; ; ; ; ; ; ;
Publisher / Repository:
Nature Sustainability
Date Published:
Journal Name:
Nature Sustainability
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. Abstract Aim

    With plant biodiversity under global threat, there is an urgent need to monitor the spatial distribution of multiple axes of biodiversity. Remote sensing is a critical tool in this endeavour. One remote sensing approach for detecting biodiversity is based on the hypothesis that the spectral diversity of plant communities is a surrogate of multiple dimensions of biodiversity. We investigated the generality of this ‘surrogacy’ for spectral, species, functional and phylogenetic diversity across 1,267 plots in the Greater Cape Floristic Region (GCFR), a hyper‐diverse region comprising several biomes and two adjacent global biodiversity hotspots.


    The GCFR centred in south‐western and western South Africa.

    Time period

    All data were collected between 1978–2014.

    Major taxa studied

    Vascular plants within the GCFR.


    Spectral diversity was calculated using leaf reflectance spectra (450–950 nm) and was related to other dimensions of biodiversity via linear models. The accuracy of different spectral diversity metrics was compared using 10‐fold cross‐validation.


    We found that a distance‐based spectral diversity metric was a robust predictor of species, functional and phylogenetic biodiversity. This result serves as a proof‐of‐concept that spectral diversity is a potential surrogate of biodiversity across a hyper‐diverse biogeographic region. While our results support the generality of spectral diversity as a biodiversity surrogate, we also find that relationships vary between different geographic subregions and biomes, suggesting that differences in broad‐scale community composition can affect these relationships.

    Main conclusions

    Spectral diversity was shown to be a robust surrogate of multiple dimensions of biodiversity across biomes and a widely varying biogeographic region. We also extend these surrogacy relationships to ecological redundancy to demonstrate the potential for additional insights into community structure based on spectral reflectance.

    more » « less
  2. Abstract Aim

    Generalized dissimilarity modelling (GDM) is a powerful and unique method for characterizing and predicting beta diversity, the change in biodiversity over space, time and environmental gradients. The number of studies applying GDM is expanding, with increasing recognition of its value in improving our understanding of the drivers of biodiversity patterns and in implementing a wide variety of spatial assessments relevant to biodiversity conservation. However, apart from the original presentation of the GDM technique, there has been little guidance available to users on applying GDM to different situations or on the key modelling decisions required.


    We present an accessible working guide to GDM. We describe the context for the development of GDM, present a simple statistical explanation of how model fitting works, and step through key considerations involved in data preparation, model fitting, refinement and assessment. We then describe how several novel spatial biodiversity analyses can be implemented using GDM, with code to support broader implementation. We conclude by providing an overview of the range of GDM‐based analyses that have been undertaken to date and identify priority areas for future research and development.

    Main conclusions

    Our vision is that this working guide will facilitate greater and more rigorous use of GDM as a powerful tool for undertaking biodiversity analyses and assessments.

    more » « less
  3. Abstract Aim

    We may be able to buffer biodiversity against the effects of ongoing climate change by prioritizing the protection of habitat with diverse physical features (high geodiversity) associated with ecological and evolutionary mechanisms that maintain high biodiversity. Nonetheless, the relationships between biodiversity and habitat vary with spatial and biological context. In this study, we compare how well habitat geodiversity (spatial variation in abiotic processes and features) and climate explain biodiversity patterns of birds and trees. We also evaluate the consistency of biodiversity–geodiversity relationships across ecoregions.


    Contiguous USA.

    Time period


    Taxa studied

    Birds and trees.


    We quantified geodiversity with remotely sensed data and generated biodiversity maps from the Forest Inventory and Analysis and Breeding Bird Survey datasets. We fitted multivariate regressions to alpha, beta and gamma diversity, accounting for spatial autocorrelation among Nature Conservancy ecoregions and relationships among taxonomic, phylogenetic and functional biodiversity. We fitted models including climate alone (temperature and precipitation), geodiversity alone (topography, soil and geology) and climate plus geodiversity.


    A combination of geodiversity and climate predictor variables fitted most forms of bird and tree biodiversity with < 10% relative error. Models using geodiversity and climate performed better for local (alpha) and regional (gamma) diversity than for turnover‐based (beta) diversity. Among geodiversity predictors, variability of elevation fitted biodiversity best; interestingly, topographically diverse places tended to have higher tree diversity but lower bird diversity.

    Main conclusions

    Although climatic predictors tended to have larger individual effects than geodiversity, adding geodiversity improved climate‐only models of biodiversity. Geodiversity was correlated with biodiversity more consistently than with climate across ecoregions, but models tended to have a poor fit in ecoregions held out of the training dataset. Patterns of geodiversity could help to prioritize conservation efforts within ecoregions. However, we need to understand the underlying mechanisms more fully before we can build models transferable across ecoregions.

    more » « less
  4. Abstract

    Climate change is already having profound effects on biodiversity, but climate change adaptation has yet to be fully incorporated into area‐based management tools used to conserve biodiversity, such as protected areas. One main obstacle is the lack of consensus regarding how impacts of climate change can be included in spatial conservation plans. We propose a climate‐smart framework that prioritizes the protection of climate refugia—areas of low climate exposure and high biodiversity retention—using climate metrics. We explore four aspects of climate‐smart conservation planning: (1) climate model ensembles; (2) multiple emission scenarios; (3) climate metrics; and (4) approaches to identifying climate refugia. We illustrate this framework in the Western Pacific Ocean, but it is equally applicable to terrestrial systems. We found that all aspects of climate‐smart conservation planning considered affected the configuration of spatial plans. The choice of climate metrics and approaches to identifying refugia have large effects in the resulting climate‐smart spatial plans, whereas the choice of climate models and emission scenarios have smaller effects. As the configuration of spatial plans depended on climate metrics used, a spatial plan based on a single measure of climate change (e.g., warming) will not necessarily be robust against other measures of climate change (e.g., ocean acidification). We therefore recommend using climate metrics most relevant for the biodiversity and region considered based on a single or multiple climate drivers. To include the uncertainty associated with different climate futures, we recommend using multiple climate models (i.e., an ensemble) and emission scenarios. Finally, we show that the approaches we used to identify climate refugia feature trade‐offs between: (1) the degree to which they are climate‐smart, and (2) their efficiency in meeting conservation targets. Hence, the choice of approach will depend on the relative value that stakeholders place on climate adaptation. By using this framework, protected areas can be designed with improved longevity and thus safeguard biodiversity against current and future climate change. We hope that the proposed climate‐smart framework helps transition conservation planning toward climate‐smart approaches.

    more » « less
  5. Abstract Motivation

    Traits are increasingly being used to quantify global biodiversity patterns, with trait databases growing in size and number, across diverse taxa. Despite growing interest in a trait‐based approach to the biodiversity of the deep sea, where the impacts of human activities (including seabed mining) accelerate, there is no single repository for species traits for deep‐sea chemosynthesis‐based ecosystems, including hydrothermal vents. Using an international, collaborative approach, we have compiled the first global‐scale trait database for deep‐sea hydrothermal‐vent fauna – sFDvent (sDiv‐funded trait database for theFunctionalDiversity ofvents). We formed a funded working group to select traits appropriate to: (a) capture the performance of vent species and their influence on ecosystem processes, and (b) compare trait‐based diversity in different ecosystems. Forty contributors, representing expertise across most known hydrothermal‐vent systems and taxa, scored species traits using online collaborative tools and shared workspaces. Here, we characterise the sFDvent database, describe our approach, and evaluate its scope. Finally, we compare the sFDvent database to similar databases from shallow‐marine and terrestrial ecosystems to highlight how the sFDvent database can inform cross‐ecosystem comparisons. We also make the sFDvent database publicly available online by assigning a persistent, unique DOI.

    Main types of variable contained

    Six hundred and forty‐six vent species names, associated location information (33 regions), and scores for 13 traits (in categories: community structure, generalist/specialist, geographic distribution, habitat use, life history, mobility, species associations, symbiont, and trophic structure). Contributor IDs, certainty scores, and references are also provided.

    Spatial location and grain

    Global coverage (grain size: ocean basin), spanning eight ocean basins, including vents on 12 mid‐ocean ridges and 6 back‐arc spreading centres.

    Time period and grain

    sFDvent includes information on deep‐sea vent species, and associated taxonomic updates, since they were first discovered in 1977. Time is not recorded. The database will be updated every 5 years.

    Major taxa and level of measurement

    Deep‐sea hydrothermal‐vent fauna with species‐level identification present or in progress.

    Software format

    .csv and MS Excel (.xlsx).

    more » « less