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Global and national trends, gaps, and opportunities in documenting and monitoring species distributions

['Ruth Y. Oliver', 'Department Of Ecology', 'Evolutionary Biology', 'Yale University', 'New Haven', 'Connecticut', 'United States Of America', 'Center For Biodiversity', 'Global Change', 'Carsten Meyer']

Date: 2021-08

Conserving and managing biodiversity in the face of ongoing global change requires sufficient evidence to assess status and trends of species distributions. Here, we propose novel indicators of biodiversity data coverage and sampling effectiveness and analyze national trajectories in closing spatiotemporal knowledge gaps for terrestrial vertebrates (1950 to 2019). Despite a rapid rise in data coverage, particularly in the last 2 decades, strong geographic and taxonomic biases persist. For some taxa and regions, a tremendous growth in records failed to directly translate into newfound knowledge due to a sharp decline in sampling effectiveness. However, we found that a nation’s coverage was stronger for species for which it holds greater stewardship. As countries under the post-2020 Global Biodiversity Framework renew their commitments to an improved, rigorous biodiversity knowledge base, our findings highlight opportunities for international collaboration to close critical information gaps.

Funding: This study is supported by the EO Wilson Biodiversity Foundation, National Science Foundation grant DEB-1441737 and National Aeronautics and Space Administration grants 80NSSC17K0282 and 80NSSC18K0435 to W.J. C.M. acknowledges funding by the Volkswagen Foundation through a Freigeist Fellowship (A118199), and additional support by iDiv, funded by the German Research Foundation (DFG–FZT 118, 202548816). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Copyright: © 2021 Oliver et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Introduction

Detection, understanding, and management of global biodiversity change and its manifold consequences [1,2] in a rapidly transforming world rely on comprehensive evidence to establish baselines and assess changes. As discussions of the post-2020 Global Biodiversity Framework of the Convention on Biological Diversity (CBD) enter their final stage, the availability of data and metrics to assess progress toward agreed-upon targets has taken a central role [3–7]. The fundamental need for an improved and shared knowledge base of global biodiversity is recognized in the proposed Target 19, which requires the availability of reliable information on biodiversity status and trends [8].

Descriptions of species’ geographical ranges and their temporal dynamics are fundamental biodiversity measures [9], as captured in the species distribution Essential Biodiversity Variable [10]. The status and trends of species’ geographic distributions are directly related to species’ ecological relevance, population size, and extinction risk, and are thus central to the conservation and management of species and their ecological functions [11–13]. Ambitions to limit threats to species and ensure the integrity of ecosystems, which are central goals of the post-2020 Global Biodiversity Framework under discussion [8], critically rely on effective documentation and monitoring of species distributions and changes over time [6,7,14].

Thanks to significant advances in data collection, mobilization, and aggregation [15–17], publicly accessible occurrence data are growing rapidly [9,14,18], with over 1.6 billion occurrence records across sources and taxa available in the Global Biodiversity Information Facility (GBIF). These data represent an increasing array of sources, including museum specimens, field observations, acoustic and visual sensors, and citizen science efforts [19]. Digital platforms such as Map of Life (MOL) have begun to integrate these data through models to bolster a multitude of research and conservation applications [10,20].

Increases in data quantity alone, however, provide little information about overall progress toward an effective spatial biodiversity knowledge base, as records may be highly redundant and cover a limited set of species and regions [21]. Indeed, prior work has revealed significant taxonomic and geographic gaps in the existing data [9,21–26] and highlighted the importance of accounting for expected diversity and scale sensitivity in data coverage assessments [19,21,27–29]. Scientists have identified a range of socioeconomic, linguistic, and ecological drivers for gaps and biases in the current data and identified geographical access, availability of local funding resources, and participation in data-sharing networks as key correlates of data gaps [21,30].

The aforementioned gaps in knowledge highlight the importance of a more informed and coordinated approach to developing an effective spatial biodiversity evidence base. Developing such an evidence base requires metrics that allow changes in biodiversity data coverage over time to inform decision-making. As political units responsible for coordination and stewards of their biodiversity, nations hold the key to incentivizing an improved information base and stand to gain the greatest benefits from broadly improved biodiversity information by enabling monitoring and robust management decisions. For example, the activities of the Mexican National Commission on Biodiversity (CONABIO), a permanent commission of the Mexican federal government, have led to strongly increased biodiversity information in that country that supports conservation decisions in the region [31]. Despite the urgent need to meet international targets and numerous documentations of growing data [32,33], published work has yet to provide quantitative metrics to track nations’ progress in closing spatiotemporal biodiversity data gaps [27,34–36].

Here, we provide 2 national indicators in support of the global assessment, monitoring, and decision-support around annual trends in spatiotemporal biodiversity information. These metrics are integrated within a flexible, updatable analytical framework. Specifically, we present and globally implement the MOL Species Status Information Index (SSII), which was developed under the auspices of the GEO Biodiversity Observation Network [37] (https://mol.org/indicators/coverage) in support of IPBES reporting (https://ipbes.net/core-indicators) and global assessment processes [8], as well as the Species Sampling Effectiveness Index (SSEI). We use the indicator framework to compare global and national trends in spatiotemporal biodiversity knowledge since 1950 for over 31,000 terrestrial vertebrate species and over 450 million verified and taxonomically harmonized occurrence records at the level of species, nations, and the globe. We provide a first global assessment for trends in data coverage and sampling effectiveness for terrestrial vertebrates as well as infrastructure to continuously track these indices into the future at MOL (https://mol.org/indicators/coverage).

The SSII quantifies spatiotemporal biodiversity data coverage for a particular grid resolution and species geographic range expectation (Fig 1A). The Global SSII tracks the proportion of expected range cells with records, either for a single species or averaged across multiple species (Fig 1B). The National SSII is calculated using the same method as the Global SSII but is restricted to the range cells inside a particular country (Fig 1B). Steward’s SSII follows the National SSII calculation but additionally applies a species-level weight to account for different national stewardships of species (Fig 1B). Nations’ varying responsibilities are determined by the portion of a species’ global range they hold (e.g., 1 for country endemics; see Fig 1A for illustration and Text A in S1 File for formal description). For a given species, SSII quantifies the proportion of the range with data but not how effectively these data are distributed across the proportion of the range it covers. We characterize sampling effectiveness by relating the realized spatial distribution of records to the ideal uniform distribution based on Shannon’s entropy (Fig 1C, Text A in S1 File) normalized to vary between 0 and 1, a metric we call the SSEI. The SSEI is similar to other information theoretic evenness metrics, such as Pielou’s index of species evenness, which is also based on normalized entropy [38]. SSEI has the same properties as SSII and can be calculated at the species, national, or global level and additionally can be adjusted by national stewardship for species.

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larger image TIFF original image Download: Fig 1. SSII and SSEI metrics of biodiversity data coverage and effectiveness. The metrics are illustrated for 2 hypothetical species with geographic range delineated by binary (e.g., expert range) maps and are assessed for an example 110-km equal-area grid. (a) National stewardship of species is calculated based on the relative portion of species’ ranges falling inside a country. (b) At the species level, the SSII is given as the proportion of cells expected occupied with records in a given year. In this hypothetical example, coverage is 0.83 and 0.67 for species where 5 out of 6 and 2 out 3 expected grid cells have data. Steward’s SSII adjusts this coverage by their respective national stewardship (0.83 and 0.2). Species-level SSII can be aggregated to the national level via 2 formulations. National SSII for a given taxonomic group takes the mean coverage across all species expected in a country (0.75). Steward’s SSII adjusts the mean coverage across species by their respective national stewardship (0.8). (c) SSEI compares the entropy of the realized distribution of records to that of the ideal distribution (see Text A in S1 File), where uneven sampling (lower SSEI) is considered less effective than more even sampling (higher SSEI). National SSEI takes the mean across all species expected in a country. (d) Glossary of relevant terms. Artwork from plylopics.org (see Text A in S1 File). GBIF, Global Biodiversity Information Facility; MOL, Map of Life; SSEI, Species Sampling Effectiveness Index; SSII, Species Status Information Index. https://doi.org/10.1371/journal.pbio.3001336.g001

We illustrate the SSII and SSEI for the years 2000 to 2019 for the jaguar (Panthera onca) and collared peccary (Pecari tajacu), 2 widely distributed species with heterogeneous sampling (Fig 2, Table A in S1 File). The number of records collected annually for the peccary was substantially higher than for the jaguar, ranging from 2- to 10-fold higher data collection (Fig 2A–2C). Subsequently, Global SSII was consistently higher for the peccary than the jaguar, but the difference in values was narrower than the difference in data collection would suggest (Fig 2D).

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larger image TIFF original image Download: Fig 2. Species and national example patterns and trends. SSII and SSEI trends illustrated for 2 species, the jaguar (Panthera onca) and collared peccary (Pecari tajacu). (a, b) The expected occupied cells are shown in dark gray, and total number of records collected 2010–2019 in color. (c–e) Species-level time series of the total number of records (c), Global SSII for the whole species range (i.e., all countries with expected range) (d), and Global SSEI (e) across their expected range. (f, g) Resulting National and Steward’s SSII (f) and SSEI (g) for 4 countries. Photographs from Wikimedia (see Text A in S1 File). National boundaries from gadm.org. Numerical values available in Tables A and B in S1 File. The data underlying this figure may be found in https://mol.org/indicators/coverage and https://github.com/MapofLife/biodiversity-data-gaps. SSEI, Species Sampling Effectiveness Index; SSII, Species Status Information Index. https://doi.org/10.1371/journal.pbio.3001336.g002

Such results suggest a much lower sampling effectiveness, as indexed by the SSEI, for the peccary compared to the jaguar, indicating that many peccary records were concentrated in the same regions. SSII improved markedly for the peccary in recent years, reaching 0.03 (i.e., 3% global range cells with annual records). This increase was associated with increasing SSEI, as the number of records collected were only slightly elevated (Fig 2E). National and Steward’s SSII calculated for these 2 species was highest in Costa Rica and lowest in Brazil (Fig 2F, Table B in S1 File). National SSEI was generally highest in Brazil and lowest in Colombia (Fig 2G).

Typologies of national monitoring efforts National biodiversity monitoring is influenced by a myriad of social, political, economic, and geographic factors [21,22,30,48,49]. We categorized nations into the following 4 main types based on Steward’s SSII status and trends over the previous decade: (1) coverage less than the global mean with no or decreasing trend (2010 to 2019) (42% of nations); (2) coverage less than the global mean with an increasing trend (24%); (3) coverage greater than the global mean with no or decreasing trend (17%); and (4) coverage greater than the global mean with an increasing trend (17%) (Fig 5A). We highlight national trajectory examples from each group (Fig 5B). Status and trends in Steward’s SSII differed strongly among continents (Fig 5C). PPT PowerPoint slide

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larger image TIFF original image Download: Fig 5. Typologies of nations’ data coverage and trends. (a) Mean values and change rates in Steward’s SSII over the previous decade (2010–2019). Horizontal dashed line represents the global mean of Steward’s SSII. Left panels show nations with no significant or decreasing trends in coverage. Right panels show nations with significant (p < 0.01) increasing trends in coverage. We categorized nations into the following 4 main types based on Steward’s SSII status and trends over the previous decade: (1) coverage less than the global mean with no or decreasing trend (2010–2019) (42% of nations); (2) coverage less than the global mean with an increasing trend (24%); (3) coverage greater than the global mean with no or decreasing trend (17%); and (4) coverage greater than the global mean with an increasing trend (17%). (b) Example time series for nations within each type. (c) National assignment to quadrants. Bar plot shows percentages of nations within each quadrant. National boundaries from gadm.org. The data underlying this figure may be found in https://mol.org/indicators/coverage and https://github.com/MapofLife/biodiversity-data-gaps. SSII, Species Status Information Index. https://doi.org/10.1371/journal.pbio.3001336.g005 Biodiversity data coverage within Mexico has followed a strong, and increasing, trajectory in both the 20th and 21st centuries. Despite lower coverage through periods of the 20th century, South Africa has had similarly strong and increasing data coverage over the previous decade. Many nations that had historically limited data coverage showed recent increases in coverage, for example, Brazil. These trajectories in data coverage may be due to political decisions and national infrastructure, which supports biodiversity data collection and mobilization. For example, the establishment of a national biodiversity program (CONABIO) [31] and large-scale atlasing efforts, such as the Southern African Bird Atlas Project [50,51]. Through their national commitment to the CBD targets to decrease species extinctions, nations are asked to monitor the species for which they hold greatest responsibility, or, in the case of endemic species, full responsibility. By comparing National and Steward’s SSII, we found that a majority of nations (50%) preferentially survey species for which they hold a high proportion of the global ranges (Fig B in S1 File). This may reflect a tendency for endemic biodiversity to confer special cultural importance and for societal interests to influence research agendas [49] or simply reflect the preferences of citizen scientists aiming to boost their life lists. Selective monitoring based on nations’ stewardship of species may beneficially promote conservation agendas within nations that have primary control of habitats that species rely on. With this goal in mind, our analysis highlights when nations fall behind on sampling species for which they have high stewardship and thus play a particularly large role in species’ conservation (e.g., Indonesia and Costa Rica).

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