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\begin{document}

\title{Big data-big problems? How to circumvent problems in biodiversity
mapping and ensure meaningful results}



\author[1]{Alice Hughes}%
\author[2]{James Dorey}%
\author[3]{Silas Bossert}%
\author[4]{Huijie Qiao}%
\author[4]{Michael Orr}%
\affil[1]{Xishuangbanna Tropical Botanical Garden}%
\affil[2]{Flinders University of South Australia}%
\affil[3]{Washington State University}%
\affil[4]{Institute of Zoology Chinese Academy of Sciences}%


\vspace{-1em}



  
  \date{May 26, 2024}


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\selectlanguage{english}
\begin{abstract}
Our knowledge of biodiversity hinges on sufficient data, reliable
methods, and realistic models. Without an accurate assessment of species
distributions, we cannot effectively target and stem biodiversity loss.
Species range maps are the foundation of such efforts, but countless
studies have failed to account for the most basic assumptions of
reliable species mapping practices, undermining the credibility of their
results and potentially misleading and hindering conservation and
management efforts. Here, we use examples from the recent literature and
broader conservation community to highlight the substantial shortfalls
in current practices and their consequences for both analyses and
conservation management. We detail how different decisions on data
filtering impact the outcomes of analysis and provide practical
recommendations and steps for more reliable analysis, whilst
understanding the limits of what available data will reliably allow and
what methods are most appropriate. Whilst ``perfect'' analyses are not
possible for many taxa given limited data, and biases, ensuring we use
data within reasonable limits and understanding inherent assumptions is
crucial to ensure appropriate use. By embracing and enacting such best
practices, we can ensure both the accuracy and improved comparability of
biodiversity analyses going forward, ultimately enhancing our ability to
use data to facilitate our protection of the natural world.%
\end{abstract}%



\sloppy


\textbf{Big data-big problems? How to circumvent problems in
biodiversity mapping and ensure meaningful results}

\textbf{Abstract:}Our knowledge of biodiversity hinges on sufficient
data, reliable methods, and realistic models. Without an accurate
assessment of species distributions, we cannot effectively target and
stem biodiversity loss. Species range maps are the foundation of such
efforts, but countless studies have failed to account for the most basic
assumptions of reliable species mapping practices, undermining the
credibility of their results and potentially misleading and hindering
conservation and management efforts. Here, we use examples from the
recent literature and broader conservation community to highlight the
substantial shortfalls in current practices and their consequences for
both analyses and conservation management. We detail how different
decisions on data filtering impact the outcomes of analysis and provide
practical recommendations and steps for more reliable analysis, whilst
understanding the limits of what available data will reliably allow and
what methods are most appropriate. Whilst ``perfect'' analyses are not
possible for many taxa given limited data, and biases, ensuring we use
data within reasonable limits and understanding inherent assumptions is
crucial to ensure appropriate use. By embracing and enacting such best
practices, we can ensure both the accuracy and improved comparability of
biodiversity analyses going forward, ultimately enhancing our ability to
use data to facilitate our protection of the natural world.

Introduction: Changing data challenges and accelerating needs

Understanding species ranges and developing approaches to reliably
monitor distributions are essential needs of any conservation target
(Hughes et al., 2021a). This is especially important in the context of
the Post-2020 Global Biodiversity Framework (GBF) and the accompanying
Monitoring Framework, which aims to provide the metrics to measure
progress towards new targets. In recent years, biological data
availability has transformed, and we have moved from being data-poor to
data-rich. There are now 2.3 billion records on GBIF, over 1.3 billion
in eBird, and over 130 million on iNaturalist, with citizen science data
increasingly outweighing prior museum specimen data for many taxa.
Point-based data enable sophisticated modelling, and analysis of traits
and changes over time in ways unimaginable previously (GBIF Secretariat
2021). Yet greater access to data can mean that basic ecological
principles are forgotten, and analysis becomes merely a statistical
exercise. Combined with the incentivisation to publish high impact
(Eyre-Walker 2013) and global papers (Wyborn \& Evans 2022), there is a
temptation to focus on headline titles, regardless of data extent,
pervasive biases, or the specific methods required to account for these
issues (Hughes et al., 2021b). Thereafter, once such resources are
published, the path of least resistance is to reuse them rather than
reinventing the wheel (unfortunately, this often happens even if the
wheel is lopsided). More problematic, the existence of studies claiming
to do something reduces the novelty of any subsequent studies improving
prospects, greatly reducing the potential for high-impact publication
and thereby also reducing incentives to make such improvements in the
first place. Furthermore, big ``headlines'' will be requoted and taken
as accurate, and commentaries and responses are hard to publish and are
unlikely to receive such attention; meaning that like anything on the
internet today once an inaccurate analysis is published, the headline
may be taken as dogma, and if they are inaccurate the consequences of
those inaccuracies are inevitably propagated.

Complicating matters, the discrepancy between data-rich and data-poor
regions generally mirrors that of GDP, and consequently the areas with
the richest biodiversity may also have the poorest coverage in terms of
biodiversity data (Giam et al., 2012; Stork 2018; Hughes et al., 2021b).
This means that ``global studies'' disproportionately represent patterns
in higher income economies. Reconciling these biases requires both
understanding and then working to overcome sampling related issues. Such
analyses are crucial across taxa, regions, and scales, forming the
foundation of effective National Strategic Biodiversity Action Plans
(NBSAPs) (Whitehorn et al., 2019, Schmidt-Traub, 2021).

Understanding data challenges and limits, and how and which methods can
be applied, is critical. This is because every form of analysis makes
its own assumptions, and every dataset has its own biases and
inconsistencies. Depending on what data are available, modelling
species-level data may only be locally possible for many taxa, and using
data beyond sensible bounds can misdirect priorities and misinform
management plans. Without an understanding of how data were generated or
their biases and shortcomings, the likelihood of misuse increases. Thus,
with this ever-growing wealth of data, it is critical to understand how
to use it effectively, and what processes to follow to ensure that
outcomes can meaningfully guide future conservation targets.

Here, we explore different methods commonly applied to biodiversity
data, the assumptions they make, and the impact of applying them to data
that do not meet those assumptions. We then discuss approaches and
frameworks representing best practices in biodiversity data analysis,
and provide a stepwise framework which can be used to ensure
biodiversity data are used within their limits and that the assumptions
of each step are clearly understood.

Context: The current data landscape

Biodiversity mapping is a central tenet of conservation, and it is
achieved either through the use of point data or expert range maps.
First came basic repositories of museum point data such as Arctos in
1996 (Jarrell et al., 2010), followed by GBIF in 2001 and OBIS for Ocean
systems in 2002. More recently, a wealth of citizen science platforms
have grown to provide a greater spatial, temporal, and taxonomic volume
of data than has ever previously been available. Such data promised to
revolutionise our understanding of global biodiversity patterns,
enabling us to finally move away from the hand-drawn range maps in
Birdlife, IUCN, GARD and Fishbase (all of which aggregate data and map
species distributions), which themselves represented a major step
forwards in providing any spatial data to map species ranges for
thousands of species (Hughes et al., 2021c). Most global papers continue
to use these often hand-drawn polygon maps to visualise priorities or
assess gaps because they are easy to download and simple to use, despite
studies highlighting substantial scale-dependence and errors of both
omission and commission (Herkt et al. 2017; Li et al., 2020; Hughes et
al 2021c). Growing volumes of point data provide an option of better,
and more accurate species ranges analysis than polygon approaches, yet
these empirical point-based data present a different challenge; how can
we use these data without over-extending their limits?

As point data continue to increase in volume, so too will the number and
type of biases within those data, making their reconciliation more
complex. At the same time, the drive to perform large-scale mapping is
increasing (Wyborn \& Evans 2021). There has never before been such a
dire need for these data, with the failure to complete the Aichi
Biodiversity Targets, the delays in most countries completing their
National Strategic Biodiversity and Action Plans (and the need for
revisiting these plans), and the Monitoring Framework. Understanding
where we have data, what biases exist in those data, and how they can be
overcome is critical to even begin moving towards the goals of the GBF
(Mace et al. 2018). Modelling and mapping has previously even been the
subject of major assessments (i.e. IPBES 2018), and thus whilst there is
guidance on model approaches, understanding how different forms of data,
such as species distributions, can be effectively used is essential.

Understanding the dimensions of biases and their consequences

Accurate mapping of species ranges has some basic tenets that must be
followed to produce reliable results (Malavasi 2020). Depending on the
amount and quality of available data, increasingly sophisticated
approaches can be used, but even basic methods have great potential when
used carefully and appropriately. For the most basic analyses, data must
still be representative, with minimal biases, but data in popular
databases were largely collected from areas within 2km of roads (Hughes
et al., 2021b), meaning generalist and disturbance-tolerant species will
be over-represented and relatively few areas will be protected in most
cases (given the higher level of development near mapped roads; Hughes
2018). These biases can be even stronger in citizen science data or
social media data, and must be carefully accounted for (Bird et al.,
2014; Johnston et al. 2020; Hughes et al. 2021b; Barber et al., 2022;
Chowdhury et al., 2023a).

Common uses of biodiversity data include species-level (i.e. IUCN Red
List) to regional and global mapping. Thus, spatial and taxonomic biases
will be a particular problem with biodiversity mapping approaches which
do not include point projection or extrapolation to circumvent data gaps
and biases. On a species-level, approaches such as extent of occurrence
and area of occurrence (EOO and AOO; which bases ranges on minimum
spanning polygons and occupied cells, respectively) will
disproportionately represent the most disturbed areas where most data
exist, and whilst they are only sometimes calculated with available
point data (unless further additional data can be collated for
verification), they are a core component of IUCN range assessments.
Further, without careful data cleaning, errors can obscure any patterns
or trends, as common errors (switched coordinates, incorrect
georeferences, misidentifications, etc.) may mislay species distribution
points across different continents, even different hemispheres, and a
projective approach consequently risks overestimating distributions
across much of the global land-surface. For all these reasons, to assess
if a dataset can be used for meaningful analysis, we first need to
determine if the data are taxonomically or spatially representative, if
it represents the current ranges of species, and how most data were
generated, before assessing if methods used for assessing ranges can
give a useful understanding of species ranges.

Globally, biases are pervasive, and representative and accessible data
simply do not exist for many taxa, especially hyper-diverse and
challenging taxa such as insects (Garcia-Rosello et al., 2023). Most
data come from high-income countries, despite only a minority of species
coming from these regions (Hughes et al., 2021; Orr et al., 2020). For
example, Asian bees represent 15\% of globally described species, but
only 1\% of data (Orr et al., 2020). Notably, problematic knowledge gaps
still remain even in some of the best-known areas like Europe (Leandro
et al. 2017). Ultimately, any research attempting global-scale analysis
based on point data would strongly over-represent patterns in
high-income economies and better-known regions within them (Orr et al.,
2021), though different data-sharing policies from different countries
or institutes may alter completeness of available data. Countries and
regions may limit both data availability, and permitting for research,
both of which can alter the availability and therefore
representativeness of data, particularly in developing economies. These
policies may also limit collaborative efforts, particularly between
neighbouring countries, and make transnational analysis particularly
challenging due to a lack of standardisation between regional
data-collection efforts. Thus, equitable international partnerships are
a paramount consideration for research going forward, and understanding
how to reconcile differences is crucial to best use data that has
already been collected.

Taking a recent example, Chowdhury et al., 2023b) aimed to assess the
adequacy protection coverage of protected areas for all insects. Using
their occurrence data DOI, we can see that 69\% of their data came from
Europe, and 21\% from North America, whereas conversely each of Africa,
Asia, Oceania, and South America only contributed about 2\% of records
each, despite having much greater diversity (Orr et al., 2020). This is
consistent with other analyses across taxa, highlighting why
interpolation requires extreme caution when interpreting global trends
(i.e. Orr et al., 2021; Hughes et al., 2021b); a recent attempt at
global bee decline analysis faced similar geographic biases and did not
account for them (Zattara \& Aizen 2021), and even many regional
analyses (e.g Kerr et al., 2015) do not adequately account for potential
changes in collector aims and behaviours over time. There are similar
biases in taxonomic representation (Chowdhury et al., 2023b); of the
eight genera with over one million points in GBIF, seven are
butterflies, and in fact 51\% of all GBIF invertebrate data are for
Lepidoptera. In another example, Bolam et al. (2023) analyzed most
IUCN-assessed threatened species and claimed that over half of
threatened species require recovery actions, but which species have been
assessed is itself biased by spatial effort for most groups, as well as
only representing a subset of species (Hughes et al., 2021a), so these
results may not be generalizable. This inability to generalise given the
lack of spatial or taxonomic representation may be the case for many
studies using subsets of total diversity for which there are sufficient
data (Visconti et al. 2016; Pacifici et al. 2020). Furthermore,
taxonomic expertise may be limited, and in certain taxa, reliance even
on single experts may impact the reliability of results across space and
time. Given acknowledged spatial biases (Hughes et al. 2021b;
Rocha-Ortega et al. 2021), many of these species likely already occupy
human-modified areas and would benefit little from protected areas, or
might even do worse if they show anthropophilic tendencies. Furthermore,
overall 80\% of insect families have under 10\% of species covered, and
the remaining 20\% have 11-13\%, meaning that no generalizable
conclusions can be drawn when the majority of species are not covered
(because those with sufficient data do not largely represent rarer
species). In terms of how records were generated, 62\% were human
observation (majority citizen science data) and 31\% were specimens
(from possibly centuries ago, as a time filter was not applied). Some
specimens might even be fossils of species which are still extant (as
fossil records were downloaded and only extinct fossil species were
removed).

Citizen science data can also complicate matters when used uncritically,
yet such data makes up most contemporary data for many groups. Taxonomic
biases are amplified in these data, with birds clearly dominating
(Dobson et al., 2020; Di Cecco et al., 2021), but other groups may also
exhibit biases. For example, 11\% of all insect data from Chowdhury et
al. (2023b) was just from UK butterfly and moth citizen science
monitoring programs, and a further 10\% were from global citizen science
programs. This would exacerbate the aforementioned biases of collections
in developed areas (Hughes et al. 2021b), as seen for bees but was
unaccounted for in Zattara \& Aizen (2021). Citizen science data are
useful, especially for phenological monitoring, but occur
disproportionately in developed areas and for common and easy to
recognise species, especially of ``charismatic'' or ``beautiful''
species, meaning that, without careful steps to correct for or
counteract biases, these data can compound biases and not necessarily
improve our knowledge (Dickinson et al. 2012; Bird et al. 2014; Ward
2014). This can also impact the ability of such data to detect trends
(Kamp et al., 2016). Generally, regional differences in organisations
and their taxonomic foci means that these biases may vary by region; the
UK in particular has a huge aggregation of Lepidoptera data, despite not
having particularly high diversity relative to other regions. This is
further complicated by differences in data sharing policies. For
example, iNaturalist is free to use, but the Bees, Wasps \& Ants
Recording Society of the UK shares data publicly only on 10 km grids
and, as such, cannot be included in GBIF downloads
(https://www.bwars.com/content/bwars-data-download).

These issues mean that the public data for insects are not spatially or
taxonomically representative, and cannot be regarded as such, so
approaches to circumvent these issues require either resampling or
reprojection methods if we wish to reconstruct meaningful global, or
even regional patterns (i.e. Orr et al., 2021). Given the spatial and
taxonomic gaps, clear assessments of representativeness of data are
needed, especially in tropical regions (Giam et al., 2012; de Araujo et
al., 2022), and supplemental data may be needed for the most basic view
of many taxa. Many regions have disproportionate volumes of data with
different regional biases, for example Chowdhury et al., (2023b) have
largely provided a metric with a disproportionate emphasis on European
Lepidoptera. However, even within Europe, further assessment of the
percentage of the area would be needed to judge the true degree of
protection, unless additional modelling and calibration were used to
reconcile the biases within these regions. Stating any result beyond
these regions and taxa risks being misleading, while giving the
impression that we already have sufficient data for such types of global
analyses, which we simply do not for many taxa (Wyborn \& Evans, 2021).

Measuring diversity in the face of biases

Following the case study outlined above, it is clear that there are
right and wrong ways to carry out and interpret analyses. Whilst some
shortcuts may have relatively little impact, others may invalidate
interpretations when assumptions are not met. It is also worth
considering the cumulative contribution of smaller biases in undermining
accurate interpretation. Now we come to the methods: how can we use
spatially and taxonomically biased data to recover biodiversity
patterns? Avoiding biases entirely is virtually impossible in large
datasets, so finding methods to minimise their impacts is critical.

Different researchers and organizations employ different methods, but
consistency is important to enable comparison. One of the major methods
used by the IUCN in their generation of species ranges (as part of IUCN
assessments) is that of the extent of occurrence and area of occupancy
(AOO, which is the occupied subset of the EOO-which is an MCP minimum
convex polygon) to quantify species distributions. Such methods have
also been employed in some research articles (Bradshaw et al., 2014;
Chowdhury et al., 2023), sometimes with useful improvements that help
alleviate biases (Kass et al., 2021). However, mapping these ranges
requires a certain level of data confidence and completeness in existing
distributional data, and the uses of either technique are limited if
systematically collated data were not used for mapping ranges. In such
cases, the AOO approach cannot be usefully applied alone because most
points are from developed and disturbed areas (for many species, we
would not expect the areas inhabited to be protected as they are less
likely to represent high-quality habitat targeted for such protections).
Thus, if an AOO based on point data is used, a null-dataset (bias
weighting and assessment of representativeness) may be needed to ensure
that surveyed cells are adequate, as biased sampling again means that
more developed and less protected areas will have more species data.
Areas of occurrence would therefore require further stratified sampling,
such as grid- or transect-based approaches, or would need to rely on
percentage presence approaches (the percentage of surveyed cells that a
species were present in) to try to assess patterns; and may be entirely
unsuitable if the degree of data coverage is too low. Furthermore, the
AOO system of analysis has basic requirements to be performed well;
based on inventory of species presence within the estimated range; and
such a requirement is unlikely to be met in global analysis where degree
of sample coverage will underestimate occurrence wherever data gaps
occur.

Methods aiming to understand entire species ranges (and with inadequate
data for occupancy-like approaches) may attempt to use point-based data
to assess entire species ranges, but this requires extreme caution. For
interpolation-based approaches, points must be an accurate reflection of
species ranges, and multiple measures are needed to curate the data and
ensure that they are accurate. Because of the source of data and
possible encoding errors, spatial filters are needed to ensure that data
are accurate. The development and cleaning of databases is an important,
but challenging endeavour, but is needed to enable higher-resolution
analysis. Filters could include admin-area checklists (following
correction of synonyms using a curated list) or hemisphere filters (Orr
et al., 2021) to remove points where coordinate errors may exist or
points may be in private collections or even zoos. Chowdhury et al.
(2023) used CoordinateCleaner (Zizka et al. 2019) to filter for
occurrence quality (Table S1). Without clearer filters for realms or
continents this still leaves considerable room for errors. Recently, two
more complimentary packages, bdc (Ribeiro et al. 2022) and BeeBDC (Dorey
et al. \emph{In review} ; Table S1), have been released that address
additional and complementary quality checks. A failure to adequately
clean and filter data can substantially inflate species ranges.
Additionally, even after filtering, data must be examined and analysed
with a critical eye and with hypotheses kept in mind to ensure that
results fall within the limits of what the data permit.

Conversely, there can be issues if a range is calculated based on a
minimum number of points without spatial thinning or removal of
duplicates when calculating the number. For example, Chowdhury et al.
(2023) aimed to assess the percentage of ranges protected for all
insects but analysed 217 species with a polygon area of ``3'' and 1105
with under ``10'' (presumedly kilometres); these low values suggest that
many species ranges are underestimated, possibly reported only from
small-scale inventories. If they are not from a protected area, such
species would automatically be 100\% unprotected. Conversely, a species
with three geographically disparate records might have its range greatly
overestimated, potentially also exaggerating the percentage of its range
that is unprotected. Points included in studies such as Chowdhury et al.
(2023) lack such filters, and whilst attempts were made (using the
rangeBuilder package) such errors will prevail when data quantity is low
and filters have not been implemented to clean distributional errors.
However in a better scenario, where filters had been applied would a
convex hull be appropriate to represent current species ranges?

Given the paucity of data for most insects (and many other taxa),
different approaches have been applied to circumvent issues in existing
studies aiming to map richness across continents. These include
interpolating richness by modelling it directly as a function of
ecological drivers (termites, bees, Collembola: Liu et al., 2022; Orr et
al., 2021; Potapov et al., 2023, respectively), species-level modelling
within MCPs to delimit ranges, or modelling following the building of a
comprehensive point based database (ants: Kass et al., 2022). Even for
smaller regions, where there is high diversity, filtering suitable
habitat within a polygon provides a much more targeted and realistic
understanding of species ranges (Chesshire et al., 2023), and in all of
these studies the impact of data biases were limited either through
interpolation of richness for data poor regions, or via aggregation of
further data followed by modelling, and in all of them habitat filters
were applied to ensure that analyses were accurate. If these steps are
not taken, results could be misleading and, at times, counterproductive,
given the limited funds and support available for conservation and area
protection.

Using data to enable conservation planning

Most forms of biodiversity data can be used for environmental management
and conservation planning, and tailoring analysis to the available data
is critical. Target development can use two major approaches, either
focusing on species distribution mapping, or, if data of sufficient
resolution, quality and quantity are not available, attempting to map
diversity patterns and reconcile richness patterns in the face of bias.
If sufficient data are available either models or filtered convex
polygons can be used to map species ranges. Whereas when insufficient
data are available and richness is mapped (Liu et al., 2022; Orr et al.,
2021; Potapov et al., 2023), inventories of species richness are used
and richness itself is reprojected using models. These approaches can
reproject richness patterns to a reasonable degree, if sufficient
inventories have been carried out across all major environmental
conditions, and assuming that biogeographic differences will not
influence overall richness patterns. These approaches are useful in
groups where insufficient data are available for higher resolution
analysis, and can also be used to identify areas for further research if
there is a potential for high hidden diversity (Orr et al. 2021; Kass et
al. 2022).

For large spatial or taxonomic scopes, if species-level analysis is
impossible (the majority of global analyses) then interpolation-based
methods are likely to be the most appropriate. In these types of
studies, one should employ either a subsampling approach (Qiao et al.,
2023) or interpolation based on community level inventories (to model
richness overall rather than individual species ranges: Orr et al., 2021
Liu et al., 2022; Potapov et al., 2023). For subsampling, there is still
a minimum data requirement, as most areas lack data. Thus, for well
sampled taxa such as birds, it can be fairly widely applied (e.g. almost
all urban areas), but there is so little sampling for most taxa that an
index-based approach may not be possible without then interpolating.
Approaches based on biodiversity indices (Hill numbers, Shannon,
Simpson, etc.) all require both a minimum number of samples and a
minimum coverage (Qiao et al., 2023). Species area curves are a common
way to estimate completeness for any given region, yet these assume
representative coverage throughout that region and a localised inventory
of a small proportion may asymptote even when it is not representative
of the whole area to which the assessment is applied. Thus, for such
curves to be useful first assessments of the percentage of the area with
data is needed.

For many taxa, including most invertebrates, interpolation based on
modelling is needed. Such methods rely on interpolating richness based
on community-level samples and using species modelling techniques to
relate richness data to conditions present. Inevitably, this method also
involves assumptions about the representativeness of the data. For a
community projection approach, a minimum sample-size and species number
should be used (to remove the possibility of selective sampling or
overrepresentation of generalists to the neglect of specialists), and
all biome types should be represented so that the richness (or richness
index) of these varying biomes and conditions can be assayed. However,
it should be noted that such an approach will assume that there are no
biogeographic variations in drivers between regions, and consequently
cannot be applied to oceanic islands, as such models cannot inherently
incorporate biogeographic processes or dispersal. Thus, for
interpolation approaches to be applied, the number of records per
species, and even to a degree the accuracy of identification within
sites is less important (provided it is consistent within a site), and
provided there is coverage across environmental conditions these
approaches provide a powerful mechanism for global analysis, enabling
analysis even in poorly-known regions.

For species-specific approaches, both the volume and accuracy of the
data must be substantially higher, as they are much more vulnerable to
spatial bias and sensitive to data errors, with even greater
consequences for poorly known species. Firstly, data must be clean and
accurate for any species-level assessment, so cleaning checks and
filtering of bad records is a critical first step (see Box 1). The first
question is whether the data are sufficiently representative for species
level analysis both in terms of taxa, and the region under analysis,
furthermore any form of species level assessment requires sufficient
data to assess range. When examined critically, public data sources
alone are insufficient for modelling most species (Garcia-Rosello et
al., 2023), even across vertebrates, so some of the most diverse regions
might be underestimated.

Sophisticated models can be developed for well-sampled individual
species using approaches such as Maxent, or other species niche
modelling methods can be applied (though many of these will map all
relevant habitats, lacking any geospatial reference point to
differentiate functional and realised niches). However, such models have
very high data requirements, as sufficient and even data must exist from
across a species' range to effectively model its distribution and pair
it with environmental characteristics. This means that, unless
considerable effort is devoted to collating representative global data
with many partners, or taxa are already well sampled, sophisticated
models may not be representative or appropriate. Assessing these models,
not only using statistical approaches (AUC, Boyce index, AIC) alone is
also not sufficient, and work with experts to assess if ranges capture
species ranges is also likely needed to assess whether they are
reliable, and also recognise biogeographic boundaries (which may be
missed in models, especially in complex areas or where there are major
differences between fundamental and realised niche). MCPs may also be
used when data are scarce or analysis is regional, but understanding how
to curate data is a first essential step before mapping species ranges
(Zizka et al. 2019; Ribeiro et al. 2022; Dorey et al. \emph{In review}
).

Filtering for success

For basic analysis of large numbers of species, automated and repeatable
pipelines are critical. Creating an MCP is one method to delimit the
majority of a species' known range. However, for vertebrates it has been
known for centuries that species have finer-grain habitat requirements,
and even in IUCN maps the need to refine habitat within the range
polygon is becoming a basic standard (Lack, 1953; Brooks et al., 2019);
points may completely surround cities or other unsuitable regions, yet
the species may no longer be present there. Failure to remove clearly
unsuitable habitat would both dramatically increase range size and could
reduce the proportion of range protected (as much of a city is
developed). Coastal filters are also needed, as a failure to
realistically trim MCPs may render oceans suitable for land animals.

Sensible filters can transform species ranges and entirely rearrange
diversity patterns. To demonstrate how decisions on data-refinement and
cleaning impact on range sizes and degree of protection, we selected a
range of species and imposed different levels of filtering on the data,
all of which can be conducted with small datasets, or when some species
may have small volumes of data available. This includes adding spatial
filters, adding a habitat filter, trimming by coastline, and comparing
it to known IUCN ranges for species. It should be noted that most IUCN
ranges are also inaccurate and overinflate species ranges (Li et al.,
2019; Hughes et al, 2021c), yet uncritical MCPs are exponentially larger
(whilst still missing parts of the range as they will not capture
species range limits, where abundance is typically lower). For example,
an IUCN range is only 7-8\% the size of those recovered using basic MCPs
for the species shown here (as in Chowdhary et al., 2023a). If these
ranges are being mapped to assess hotspots for protection, or the degree
of protection, then the area covered and the location will entirely
determine the outcomes of assessment, and if care to filter data
appropriately is not applied, then analysis on such data may have little
relationship with the real patterns of distribution or degree of
protection of species.

Even when more carefully delineated ranges (IUCN, birdlife, GARD:
http://www.gardinitiative.org/) are likely to overestimate the degree of
protection, their area is still smaller than an MCP, especially if a
habitat filter is not applied (Table 1). We used a general habitat
filter, so more specialist filters and other steps outlined throughout
could greatly improve range estimates and make them more similar to
those in expert range maps (de Barros et al., 2021; Huang et al., 2020;
Xu et al., 2022). In all cases, the lack of filtering means ranges are
projected as many times larger than they are likely to be. Thus, as we
show here, the cleaning of data can transform where species are mapped,
richness patterns, and the efficacy of protection. We selected a range
of species for which sufficient data exist to map ranges, and where the
IUCN has mapped ranges for comparison (thus most of our examples are
mammals, though one bee, \emph{B. dahlbomii} , is also present), our
previous work has also examined the prevalence of biases in these types
of data, and how they persist across taxa (Hughes et al2021b, 2021c; Li
et al., 2019).

Table 1. Percentage of species range protected with different filters
applied for species minimum convex polygons (MCP), as well as for
International Union for the Conservation of Nature (IUCN) ranges. The
filters that were applied are noted in column headers: Hem-hemisphere
filter, Coast-removal of ocean areas within the polygon, habitat-a
simple habitat filter based on basic classifications of land-use types.
We used the species \emph{Ailuropoda melanoleuca} (Carnivora:
Ursidae),\emph{Bombus dahlbomii} (Hymenoptera: Apidae), \emph{Panthera
onca} and\emph{Panthera tigris} (Carnivora: Felidae), \emph{Priodontes
maximus}(Cingulata: Chlamyphoridae), \emph{Tapirus pinchaque} and
\emph{Tapirus terrestris} (Perissodactyla: Tapiridae).\selectlanguage{english}
\begin{longtable}[]{@{}lllllll@{}}
\toprule
& MCP & MCP & MCP & MCP & MCP & IUCN\tabularnewline
& Area & Area\_basic & Area\_hem & Area\_hem\_coast & Habitat &
IUCN\tabularnewline
\emph{Ailuropoda melanoleuca} & 2.06 & 7.49 & 7.49 & 7.49 & 7.82 &
23.05\tabularnewline
\emph{Bombus dahlbomii} & 1.91 & 1.91 & 9.37 & 11.92 & 7.34 &
25.03\tabularnewline
\emph{Panthera onca} & 12.21 & 12.21 & 16.35 & 24.54 & 27.21 &
38.87\tabularnewline
\emph{Panthera tigris} & 1.08 & 1.18 & 4.92 & 6.84 & 7.96 &
10.62\tabularnewline
\emph{Priodontes maximus} & 26.01 & 26.01 & 26.01 & 33.44 & 35.53 &
33.92\tabularnewline
\emph{Tapirus pinchaque} & 23.65 & 23.65 & 23.65 & 23.65 & 23.64 &
47.08\tabularnewline
\emph{Tapirus terrestris} & 29.38 & 29.38 & 29.38 & 29.60 & 32.28 &
30.61\tabularnewline
\bottomrule
\end{longtable}

\par\null

If we then compare the IUCN range maps to the MCPs (using the
habitat-filtered maps, as these can be as small as 8\% (\emph{B.
dahlbomii, P. onca)} of the largest MCPs), we find large areas of
commission error (mapped areas that fall outside species actual ranges
due to either wrong points, or unsuitable habitat within the MCP),
highlighting that much of these areas are still unsuitable (Table 2).
Whilst modelling could be used, and would further refine distributions
(i.e. Hughes et al., 2021c), these steps highlight that even if data
were adequately cleaned, the final outcomes would still not be
indicative of species ranges; at a minimum, a habitat filter must be
imposed, and coarser filters based on intactness can be easy to apply
(e.g. Lu et al., 2021). Thus, whilst global prioritisation and ambitious
analyses are needed, species-specific models or maps for many groups,
such as insects, are simply not possible globally without additional
efforts to collate data (Garcia-Rosello et al., 2023); such attempts
would simply not be representative of data-poor regions, or indeed, any
biodiversity hotspot (especially if no habitat filter was implemented).
Until data-gaps are filled (as in the case of concerted work for ants:
Kass et al., 2022), meaningful analysis will remain challenging or even
impossible at global scales, and focusing on taxa or regions where such
data are available is necessary.

Table 2. Overlap between IUCN ranges and habitat-filtered MCPs.\selectlanguage{english}
\begin{longtable}[]{@{}llllllll@{}}
\toprule
& \emph{Ailuropoda melanoleuca} & \emph{Bombus dahlbomii} &
\emph{Panthera onca} & \emph{Panthera tigris} & \emph{Priodontes
maximus} & \emph{Tapirus pinchaque} & \emph{Tapirus
terrestris}\tabularnewline
MCP- Commission & 80.68 & 72.05 & 43.80 & 91.52 & 17.96 & 64.55 &
11.56\tabularnewline
IUCN-Omission & 8.27 & 6.28 & 4.23 & 2.13 & 14.71 & 15.30 &
13.91\tabularnewline
Overlap & 11.05 & 21.66 & 51.97 & 6.35 & 67.33 & 20.15 &
74.53\tabularnewline
\bottomrule
\end{longtable}



Defining your research question, selecting and processing the data

Mapping species ranges is important, but any study seeking to understand
species ranges must follow sensible steps to ensure that analyses are
appropriate, and that the caveats and assumptions at each step are
clearly understood. Any researcher should keep several questions
explicitly in mind from the outset of their studies, and need to assess
if the data exists, or can be collated to answer those questions (Figure
1).

\begin{enumerate}
\tightlist
\item
  Defining the questions: What is being attempted, what data are needed,
  and do those data exist?
\item
  Selecting appropriate data to use: What assumptions are you making,
  what limits exist in potential data use? For instance, can citizen
  science data be used for your specific aim, and if so how could such
  data be sufficiently cleaned to be reliable?
\item
  Understanding the scale and representativeness: Are the data
  representative of the species and region under study?
\end{enumerate}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

Figure 1. Decision tree of how to accurately use data to analyse
patterns of richness. Questions are given in the light grey boxes and
solutions are provided in blue

Understanding species ranges and how well-protected they are is crucial
for filling gaps in protection coverage and effectively protecting
species into the future, but poor analysis can mislead and misdirect
attention and waste limited conservation resources. Here, we demonstrate
the potential pitfalls of uncritical species mapping, highlighting the
importance of thoroughly vetting data sources and engaging them with the
same ecological principles that are expected for the best-known species.
Ultimately, best-available-data arguments seeking to overreach beyond
what is possible (i.e. globally, Wyborn \& Evans 2022) are bad-data
arguments that can also have bad outcomes for conservation. Furthermore,
``effective protection'' needs may also differ across species (Butchart
et al., 2015), such that generalists and specialists would respond
differently, and this must also be considered, rather than na\selectlanguage{ngerman}ïve
aggregations that treat all species as generalists or only include
generalist species and then claim representation for entire groups.

Various analyses have been conducted which use these approaches (Figure
1) to sensibly and sensitively map species ranges and biodiversity
patterns. For example, in terms of limiting analysis to areas where data
are representative, there are many regional analyses which use such
approaches (Figure 1; solution 1), as well as studies which increase the
resolution of studies by incorporating other types of data not available
at wider scales (Fukaya et a., 2020; Cosentino et al., 2023). For
modelling richness patterns in the absence of sufficient data for
species-level analysis, examples exist for data-poor invertebrates,
ferns, and lycophytes (Orr et al., 2020; Weigand et al., 2020; Liu et
al., 2022; Potapov et al., 2023) (Figure 1; solution option 2), and such
work may also include collating more data to ensure that it is spatially
and taxonomically representative (Figure 1; solution 4) (Qiao et al.
2023). A good recent example is for ants, where a large database was
collated to ensure that it was representative before conducting
individual species models to map richness (Figure 1; solutions 5 and 7)
(Janicki et al., 2016; Guénard et al., 2017; AntWeb; https://antweb.org;
Kass et al., 2022). Alternatively, indices can also be applied to fill
gaps, either for limited regions such as urban areas (Hughes et al.,
2022) (Figure 1; solution 3), or indices combined and interpolated more
widely (Qiao et al., 2023). Only where representative data exist across
taxa can species-specific analyses be conducted. If limited
species-specific data are available, this may include simple approaches,
such as selecting suitable habitat from within MCPs (Figure 1; solution
6) (Chesshire et al., 2022), or following approaches similar to those
advocated for refining IUCN maps to better represent species true ranges
(landcover and elevation filters within similar ecoregions or biomes,
Brooks et al., 2019). In all cases, care and calibration at each step is
needed to ensure that data are used within reasonable limits, to not
over-extend their utility, or overreach on interpretation.

Figure 2. Projecting richness with different approaches. A. Maxent
species models (Figure 1 solution5). B. Interpolating richness from
inventories (solution 2). C. Filtering habitat within convex polygons
(solution 6). D. IUCN richness. E. IUCN richness filtered by suitable
habitat, F. MCP unfiltered richness.

Each approach has its own inherent assumptions and level of detail (see
Figure 2), so some caveats are necessary. Patterns from each should be
comparable (provided that there are sufficient data for analysis), but
they will not be identical, and richness values will vary between
approaches (thus, relative patterns rather than absolute values should
be explored). Selection of input variables, particularly land-cover
variables, can strongly influence the projected distribution of species,
and if some regions are under-sampled or under-represented then they may
not be accurately reflected in analysis; this problem increases with
scale. Categorical variables can artificially delimit ranges, and thus
whilst we used the IUCN ecosystem typology to increase the sensitivity
of analysis (as more accurate National maps are not available for
continental regions such as Africa), this may still cause more extreme
transitions in apparent diversity (especially in small, inaccessible and
under-sampled regions) but this is still necessary to refine
overgeneralised maps (Figure 2E vs D for IUCN and F vs C for MCPs); yet
more sensitive models using interpolation or modelling based on
continuous variables (such as vegetation height and density) can be
better where they can be applied (Figure 2 a, b). Furthermore, selection
of models requires care, as joint-species distribution models are
becoming increasingly popular (Zurell et al., 2018; 2019), however
whilst popular these should be applied only where a functional
relationship between species (competition, mutualism etc) exists, as
correlations based on bias sampling could emerge from applying such
models without verified ecological relationships (Poggiato et al.,
2021). Similarly tied to scale, interpolations over very large and
heterogeneous regions cannot incorporate biogeographic differences if
relying solely on interpolation of richness rather than species-specific
approaches, and may better represent drivers of patterns in better
sampled regions. Furthermore, interpolations between mainland areas and
islands cannot encompass island biogeography, as dispersal would be
assumed equal through the study area. If using MCPs, the edges of
species distributions will be excluded (as the approach can only assess
range within the recorded maximum distribution of the species based on
known locations; see Figure 2C, F). Analyses using unfiltered MCPs
(Figure 2F) are vulnerable to ``mid-domain'' type effects, where central
areas will appear richer regardless of land-cover type due to the
probability of overlap between MCPs in central areas of maps (Figure 2).

The importance of reflecting habitat in maps is clear by comparing
patterns when habitat variables are incorporated versus richness maps
based on simple clipped maps of maximum range extent (Figure 2C vs 2E).
Inventory-based approaches also rely on data being representative
throughout the study area. Assessing the performance of inventory-based
approaches can be challenging without having complete inventory data for
cross-referencing (Figure 2B). Hence, expert knowledge may be the only
way to assess if patterns ``make sense''. Furthermore, as
interpolation-based approaches may work better over large regions where
the full environmental gradient is adequately sampled they might be more
appropriate over very large scales (i.e. global) given that some regions
(such as Europe and the US) have very good sampling. However, at a
continental scale this can be challenging without additional fieldwork
or data-collation to add additional inventory data (which was not
possible in the case of this study, 2B). In addition, less species-rich
areas may be more poorly sampled, so inventory-based interpolations need
at least some site-based inventories in addition to calculations of
richness by larger unit areas. Assessment and ground-truthing at each
step is a fundamental necessity of analysis, and whilst all models are
wrong, some models are useful. That usefulness ultimately depends on
whether a model provides sufficient accuracy to guide interventions and
further work effectively. Here we show that many methods can create
broadly similar patterns (Figure 2A-C), species specific models will
provide the best approach if sufficient species level data are
available, but on extended (i.e. global) scales this is rarely done. In
most of these examples (Figure 2) the relative patterns are similar, but
unfiltered approaches overgeneralise, inflating richness of regions
predicted to host lower diversity in Central Africa, whilst missing
areas to the Northeast and Southeast, whilst other approaches do
highlight the same areas as potentially hosting higher diversity.

Moving forward, a set of best practices are needed to ensure that, as
accessible data grow, they are used in a way that builds on
understanding rather than bias (e.g., see Box 1). For example,
understanding if data are representative across regions and taxa for
species level analysis (as noted in Figure 1), as global insect data do
not meet these criteria, global analysis is not yet possible.This is
because most data are concentrated in North America and Europe and
taxonomic representation is too incomplete. For insects, such analysis
may be possible within extremely well-studied taxa, such as ants,
because work to collate representative data for species level analysis
has been conducted (Kass et al., 2022). However, for most insects (and
many other organisms) species-specific analysis of protected area
coverage or stacked diversity patterns cannot be conducted meaningfully
on a global basis, and only coarser metrics of diversity are possible.
We also need to prepare for new types of data (such as
social-media-generated data), and constantly update guidelines to ensure
their effective use. These different types of data can amplify different
forms of bias or introduce new biases, and thus knowing how they can be
used will likely need continued revisiting, not to mention safeguards to
ensure their use is also ethical. As we have seen, the greatest growth
in data availability has been in developed areas of high-income
economies; if we uncritically apply methods fit only for data-rich areas
to data-lacking areas, we risk misleading rather than facilitating
effective management.

Whilst it is true that we will continue to generate data, and the issues
highlighted here are not new, developing best practice guidelines and
facilitating sensible and sensitive data-use remains crucial if we are
to lead rather than mislead management and conservation prioritisation.
Inappropriate data-use can misrepresent hotspots, and incorrectly gauge
levels of protection species and hotspots have. Therefore, ensuring how
to use data effectively, however limited or biased those data may be, is
key to enabling effective use and solution generation as we continue to
grow biodiversity databases.

Box 1; Steps for cleaning data.

Table S1. The possible occurrence data filtering steps and the
functions, packages, descriptions, and citations required to undertake
each sub-step. Sub-steps include data flagging (adding a column with the
test results), data carpentry (changing the data itself), and data
filtering (removing occurrences based on data flags). The packages bdc
and BeeBDC also have a selection of functions that are useful for data
visualisation and critical for checking the ``common-sense'' of results.\selectlanguage{english}
\begin{longtable}[]{@{}lllll@{}}
\toprule
\textbf{Step} & \textbf{Function} & \textbf{Package} & \textbf{Brief
description} & \textbf{Citation}\tabularnewline
\textbf{Pre-filtering} & \emph{bdc\_scientificName\_empty} & \emph{bdc}
& Flag occurrences without a scientific name & (Ribeiro et al.
2022)\tabularnewline
& \emph{bdc\_coordinates\_empty} & \emph{bdc} & Flag occurrences without
latitude or longitude & (Ribeiro et al. 2022)\tabularnewline
& \emph{bdc\_coordinates\_outOfRange} & \emph{bdc} & Flag occurrences
that don't map on Earth & (Ribeiro et al. 2022)\tabularnewline
& \emph{bdc\_basisOfRecords\_notStandard} & \emph{bdc} & Flag
occurrences based on ''basisOfRecord'' & (Ribeiro et al.
2022)\tabularnewline
& \emph{bdc\_country\_from\_coordinates} & \emph{bdc} & Get country name
using coordinates (where no country name is provided) & (Ribeiro et al.
2022)\tabularnewline
& \emph{jbd\_CfC\_chunker} & \emph{BeeBDC} & A chunked and
multi-threaded function similar to bdc\_country\_from\_coordinates where
datasets are otherwise too large & (Dorey et al. \emph{In
review})\tabularnewline
& \emph{bdc\_country\_standardized} & \emph{bdc} & Standardises country
names and adds ISO2 codes, if needed & (Ribeiro et al.
2022)\tabularnewline
& \emph{bdc\_coordinates\_transposed} & \emph{bdc} & Flags occurrences
where latitude and longitude appear to be transposed & (Ribeiro et al.
2022)\tabularnewline
& \emph{jbd\_Ctrans\_chunker} & \emph{BeeBDC} & A chunked and
multi-threaded function similar to bdc\_coordinates\_transposed where
datasets are otherwise too large & (Dorey et al. \emph{In
review})\tabularnewline
& \emph{bdc\_coordinates\_country\_inconsistent \&
jbd\_coordCountryInconsistent} & \emph{bdc and BeeBDC} & Flag
occurrences where the country name does not match the coordinates. Both
functions are very similar with slightly different implementations &
(Ribeiro et al. 2022; Dorey et al. \emph{In review})\tabularnewline
& \emph{bdc\_coordinates\_from\_locality} & \emph{bdc} & Extracts
occurrences with locality information that might allow geolocation (to
fill gaps) & (Ribeiro et al. 2022)\tabularnewline
& \emph{flagAbsent} & \emph{BeeBDC} & Flags occurrences that are marked
as ''absent'' under occurrenceStatus & (Dorey et al. \emph{In
review})\tabularnewline
& \emph{flagLicense} & \emph{BeeBDC} & Flags occurrences that are
restricted by usage licences & (Dorey et al. \emph{In
review})\tabularnewline
& \emph{GBIFissues} & \emph{BeeBDC} & Flags occurrences based on
user-defined GBIF flags & (Dorey et al. \emph{In review})\tabularnewline
& \emph{clean\_fossils} & \emph{CoordinateCleaner} & Flags species that
occur in common fossil databases & (Zizka et al. 2019)\tabularnewline
\textbf{Taxonomy} & \emph{bdc\_clean\_names} & \emph{bdc} & Cleans
scientific names (e.g., removes prepended family names, removes
qualifiers, etc.) and flags occurrences with name qualifiers & (Ribeiro
et al. 2022)\tabularnewline
& \emph{bdc\_query\_names\_taxadb} & \emph{bdc} & Harmonizes taxonomy
based on the taxadb package & (Ribeiro et al. 2022)\tabularnewline
& \emph{harmoniseR} & \emph{BeeBDC} & Harmonizes taxonomy based on
custom synonym lists (updated bee taxonomy provided) & (Dorey et al.
\emph{In review})\tabularnewline
\textbf{Space} & \emph{bdc\_coordinates\_precision \&
jbd\_coordinates\_precision} & \emph{bdc and BeeBDC} & Flags occurrences
based on coordinate precision (number of decimal places). The bdc
function flags occurrences if either latitude or longitude are
imprecise. The BeeBDC function only flags occurrences where both
latitude and longitude are imprecise & (Ribeiro et al. 2022; Dorey et
al. \emph{In review})\tabularnewline
& \emph{coordUncerFlagR} & \emph{BeeBDC} & Flags occurrences that do not
pass a threshold of ''coordinateUncertaintyInMeters'' & (Dorey et al.
\emph{In review})\tabularnewline
& \emph{clean\_coordinates} & \emph{CoordinateCleaner} & Flags
occurrences based on proximity to the centroids of capitals, countries,
provinces, GBIF, and institutions; zeros for coordinates; points in
seas; statistical outliers; urban areas; and coordinate validity &
(Zizka et al. 2019)\tabularnewline
& \emph{cd\_ddmm} & \emph{CoordinateCleaner} & Flags occurrences where a
significant fraction of records have been subjected to a common `degree
minute' to `decimal degree' conversion error, where the degree sign is
recognized as decimal delimiter & (Zizka et al. 2019)\tabularnewline
& \emph{diagonAlley} & \emph{BeeBDC} & Flags records for fill-down
errors in coordinates within datasets using a sliding window & (Dorey et
al. \emph{In review})\tabularnewline
& \emph{cd\_round} & \emph{CoordinateCleaner} & Flags occurrences that
might be from gridded datasets & (Zizka et al. 2019)\tabularnewline
& \emph{countryOutlieRs} & \emph{BeeBDC} & Flags occurrences that do not
match exact or adjacent country based on a custom checklist (bee country
checklist is provided) & BeeBDC\tabularnewline
& \emph{manualOutlierFindeR} & \emph{BeeBDC} & Flags occurrences (and
their duplicates) based on expert identification & BeeBDC\tabularnewline
\textbf{Time} & \emph{dateFindR} & \emph{BeeBDC} & Attempts to find and
rescue dates hidden in other columns & (Dorey et al. \emph{In
review})\tabularnewline
& \emph{bdc\_eventDate\_empty} & \emph{bdc} & Flags occurrences without
a date & (Ribeiro et al. 2022)\tabularnewline
& \emph{bdc\_year\_outOfRange} & \emph{bdc} & Flags occurrences created
prior to a user-specified year cut-off & (Ribeiro et al.
2022)\tabularnewline
\textbf{Duplicates} & \emph{dupeSummary} & \emph{BeeBDC} & Iteratively
searches out duplicates based on user-specified inputs. Duplicate
clusters are merged and one occurrence is kept (based on user input)
while the rest are flagged & (Dorey et al. \emph{In
review})\tabularnewline
& \emph{cc\_dupl} & \emph{CoordinateCleaner} & Flags duplicates based on
matching coordinates and species names & (Zizka et al.
2019)\tabularnewline
\textbf{Filtering} & \emph{bdc\_filter\_out\_flags} & \emph{bdc} &
Filters occurrences using all present flag columns (starting with ''.'')
& (Ribeiro et al. 2022)\tabularnewline
& \emph{bdc\_filter\_out\_names} & \emph{bdc} & Filters occurrences
based only on the accepted status of taxonomy & (Ribeiro et al.
2022)\tabularnewline
& \emph{summaryFun} & \emph{BeeBDC} & Filters occurrences based on flag
columns and has additional inputs including: choosing filters to
examine, only updating the ''.summary'' flag column, and removing the
flag columns & (Dorey et al. \emph{In review})\tabularnewline
\bottomrule
\end{longtable}

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\textbf{Supplemental Methods}

\emph{IUCN richness maps}

We developed a range of maps of richness using different techniques
outlined here. Firstly, as a comparator we mapped richness using IUCN
polygons as well as trimming the IUCN polygons as advocated by Brooks et
al 2019. To map richness of African bats based on the IUCN (though this
does not include all known species) we downloaded as all Chiroptera from
the African continent from the IUCN website (on June 3rd). Firstly we
clipped the polygons for a rectangular area over the African continent,
then dissolved each species polygon to give a single polygon for each
species. We used the ``count overlapping polygons'' toolbox, split the
polygon. To do this we split the polygon into groups of 30 species (the
tool works better on lower numbers), numbered outputs sequentially, then
converted to rasters (with a resolution of 0.008 degrees, approximately
1km at the equator) then used the raster calculator to add all results
to map richness according to the IUCN.

\emph{Simple polygon richness maps}

To map richness based on point-based data we downloaded data from a
number of sources (see supplemental data), this included the ACR bat
database (using the 2023 dataset), as well as additional datasets
(Hughes et al., 2017; Tanalgo et al., 2022). In addition, we downloaded
updates to the bat interactions database (May 2023) and data collected
since 2000 stored in the Arctos dataset, plus the updated bat records in
GBIF for 2021-2023 (shown in supplemental data). This data was then
cleaned to remove synonyms and correct spellings throughout. This
yielded a dataset with 38208 points for 343 species.

Minimum convex polygons were created for each species using the
``minimum bounding geometry'' tool in ArcMap 10.8. We then trimmed this
by the outline of the African continent, then split these into a
geodatabase using the split by attributes tool using ``species'' as the
attribute. MCPs were then treated in two ways, the overall map of
unfiltered MCP richness was calculated in the same way as the IUCN
simple richness using the count overlapping polygons toolbox in ArcMap
to show how unfiltered maps of richness misrepresent range.

\emph{Habitat trimmed maps}

We needed to filter out habitat. This step is key, and likely will
introduce errors, as whilst widely advocated, most landuse maps
misrepresent actual landcover by delineating landcover types in a fairly
arbitrary way. This will over-emphasize the importance of more sampled
habitats, and may miss undersampled and smaller habitats; thus the
choice of this layer is key. Here we used the IUCN ecosystem typology
(published in 2022) because it provides overlapping classes of
ecologically relevant landcover and thus provides the most nuanced
mechanism to map landcover for regions where accurate national maps of
landuse may not be available. National maps are generally better
calibrated for a given region than global maps, which must necessarily
simplify land-cover categories, and may not be sufficiently tested at a
local level. Furthermore, the IUCN ecosystem typology is recognised by
the UN System of Environmental Economic Accounting (SEEA) and is
therefore already a global standard. An alternative here if the region
of interest is predominantly forested is to use tree density or height
as the delimiter for habitats, however, in a region with diverse
habitats, more nuance may be needed given that trimming in this way
cannot account for climate differences across the recorded maximum
bounds of species distribution. Notably urban areas were not included as
biases in sample collection to more populated areas can skew results
(Hughes et al., 2021a).

After removing duplicate species points we then extracted the landcover
types based on the typology under all locality points. We then used the
summary statistics tool to calculate the total number of points per
species in each landcover category as well as overall, then used this to
calculate the percentage of locality points for each species within each
category. We then filtered out habitat with at least 10\% of locality
points within each landcover category which fell within the polygon for
the species. To do this we masked all habitat types suitable for the
species with their individual MCP then mosaiced all parts of the species
range together using the mosaic to new raster tool, all suitable
habitats were given a value of one. Because of errors in the mosaic to
new raster tool if applied to stack richness for large numbers of
species we then mosaiced all species range maps onto a mask of the
African region, then used the raster calculator to sum richness for
groups of 40 species, numbered these sequentially, then added the
numbered MCP filtered outputs to sum overall MCP filtered richness.

In addition we used the same filtered habitat requirements and repeated
the process using the IUCN polygons rather than the MCPs. This has the
advantage of better representing overall species range (MCPs will
necessarily not include the edges of species ranges as they can only
delimit the maximum bounds of recorded range), but we know from former
work IUCN maps include pervasive biases and also artificially
under-estimate many parts of species ranges (Hughes et al., 2021, Li et
al., 2020). Furthermore, IUCN maps are not available for most plants and
invertebrates, as well as being notably incomplete in some small bodied
vertebrate taxa. Filtered IUCN maps were then stacked and richness
calculated in the same way as MCP filtered richness using the raster
calculator.

\emph{Projecting richness from inventories}

Where insufficient data exists for species specific modelling,
projecting richness from inventories may be the only possibility. This
requires a lower resolution than many other approaches (as inventories
must be drawn from an area) and can also not reflect biogeographic
differences, meaning that where island biogeography is important it
cannot account for that. Projecting richness using this approach relies
on site-based inventories of species present. To do this we first used
the same dataset as above, we created a 10km\textsuperscript{2} fishnet
as a polygon grid. The fishnet was trimmed to continental boundaries
using the same clip as previously used. This was then imported along
with point data into QGIS 3.26.3 and the sampling point tool used to
intersect grids with point data, with the FID of each grid used in an
additional column as a unique ID for the grid. We then reimported this
into ArcMap 10.8 and used the summary statistics tool to calculate the
number of points and species per grid cell. This was then reconnected to
the original grid using joins and relates, and cells with at least 30
unique records were selected as inventories, the latitudes and
longitudes of the centroids added, and this data exported to a CSV. To
better reflect appropriate data from adequately inventoried but
potentially less diverse sites we then added the publically available
data from the Darkcide database (Tanalgo et al 2022) once we removed any
listings with only a single species present (which may reflect species
specific inventories, for example a number of sites only
listed\emph{Otomops} species). The final dataset had 417 inventories for
the region which were then used for modelling.

This data could then be modelled for richness. This has the advantage
that we can use the same variables as used for species specific
modelling, including not requiring the use of a landcover map as a
filter and thus enabling more nuance. Variables selected are noted in
supplemental data, these were chosen to reflect climate parameters, and
continuous metrics of habitat structure. Variables included actual
evapotranspiration, annual mean potential evapotranspiration, aridity,
two metrics of distance to bedrock (bdticm, bedroom from ISRIC world
soil grids, as well as Estimated soil organic carbon stock as a measure
of fertility), continental moisture index, continentality, embergers
pluviothermic quotient, growing degree days 5, potential
evapotranspiration of the driest quarter (resources during the most
limiting time of year) potential evapotranspiration seasonality,
thermicity (from Envirem) and bio 3,4,5,6,12,13,14 and 15 from Worldclim
as well as vegetation canopy height.

Therefore for both forms of modelling (richness modelling based on
inventories and species modelling) we used canopy height of all
vegetation, a range of soil variables, and variables representative of
climate, moisture and seasonality. Richness inventories were then
grouped into classes (i.e. under 5 species, 5-10 species, etc) and
modelled as individual classes using Maxent, all outcomes had an AUC of
over 0.9. Within Maxent we modelled each richness level, ran three
replicates (and used an average) and used default parameters. The
average was then reclassified in ArcMap 10.8 using the 10 percentile
cloglog threshold as a minimum bound of suitability, and then using an
equal division between the threshold and the maximum value of 1 to
reflect the maximum and minimum values of the richness level, with areas
``unsuitable'' given a value of 0. The mosaic to new raster tool was
then used with the selection set to ``maximum'' to give the maximum
number of species any given area was suitable for based on model
outcomes.

\emph{Species specific modelling}

When representative data (spatial and taxonomic) is available
species-specific models are always the best approach. Using the same
point data outlined above with the same environmental variables (but at
resolution of 1km (0.008 degrees) rather than 10km (0.08 degrees)).
Species with at least 3 points were modelled (this is too low for
individual species models, but can still be useful in an approach where
only patterns are being examined, though output accuracy can then be
assessed by comparing to the IUCN ranges as whilst ranges within IUCN
are artificial the overall patterns in terms of geographic regions
should be broadly comparable). As noted variables should represent the
conditions likely to delimit species ranges, reflect ecophysiological
thresholds, and other habitat constraints, including habitat structure.
Variable correlation is accounted for within Maxent, and
species-specific variable selection can also be made using various R
packages (i.e. ENMEval).

Once again, we ran models using the default parameters and used 3
replicates, and the average reclassified to a binary map (0:1) using the
10 percentile cloglog threshold. As models will note all environmentally
suitable habitat regardless of biogeographic constraints, to reflect
biogeography we then used the IUCN redlist to note species endemic to
Madagascar, in Madagascar and continental Africa, and those limited to
the African continent. It should be noted that a number of Madagascan
endemic species lacked sufficient data to model, thus richness in
Madagascar may be under-estimated, especially given the small ranges of
some bats, but all models had an AUC exceeding 0.9, additional indicies
such as Boyce index or AIC can be used to give independent measures of
model performance and accuracy. These were then masked in batches using
masks of each of those three regions. The raster calculator was then
used to batch sum groups of 30 species within each of these three groups
and numbered sequentially. We then combined all those species restricted
to continental Africa using the raster calculator, before using the
mosaic to new raster tool to combine the three types of biogeographic
map to refine ranges and map modelled richness.

All maps are provided to show how they note richness patterns across the
African continent.

\emph{Supplemental data}

\emph{1-Species data}

\emph{ACR data downloaded June 1st 2023
https://africanbats.org/publication/african-chiroptera-report-2022/}

\emph{Arctos Data Downloaded June 1st 2023
https://arctos.database.museum/download.cfm?file=ArctosData0Iq7LVJgPN.csv}

\emph{Alroy, J. (2019). Latitudinal gradients in the ecology of New
World bats. Global Ecology and Biogeography, 28(6), 784-792.}

\emph{Bat-Eco Interactions database Downloaded June 1st
blob:https://batbase.org/05e4c768-ec29-43a2-b9d1-7fc7855484e8}

\emph{Becker, D. J., Crowley, D. E., Washburne, A. D., \& Plowright, R.
K. (2019). Temporal and spatial limitations in global surveillance for
bat filoviruses and henipaviruses. Biology Letters, 15(12), 20190423.}

\emph{Di Gregorio, C., Iannella, M., \& Biondi, M. (2021). Revealing the
role of past and current climate in shaping the distribution of two
parapatric European bats, Myotis daubentonii and M. capaccinii. The
European Zoological Journal, 88(1), 669-683.}

\emph{GBIF.org (06 May 2023) GBIF Occurrence
Downloadhttps://doi.org/10.15468/dl.xc2jvq}

\emph{GBIF.org (08 May 2022) GBIF Occurrence Download
https://doi.org/10.15468/dl.82sgat}

\emph{Jargalsaikhan, A., Ariunbold, B., Dalannast, M., Purevee, E.,
Paek, W. K., Tsagaan, K., \& Ganbold, O. (2022). Molecular phylogenetic
analysis of bats in the family Vespertilionidae in Mongolia. Journal of
Asia-Pacific Biodiversity, 15(3), 329-335.}

\emph{Kruskop, S. V., Artyushin, I. V., Yuzefovich, A. P., Undrakhbayar,
E., Speranskya, A. S., Lisenkova, A. A., \ldots{} \& Lebedev, V. S.
(2020). Genetic diversity of Mongolian long-eared bats (Plecotus;
Vespertilionidae; Chiroptera). Acta Chiropterologica, 22(2), 243-255.}

\emph{Kuo, H. C., Chen, S. F., Fang, Y. P., Flanders, J., \& Rossiter,
S. J. (2014). Comparative rangewide phylogeography of four endemic T
aiwanese bat species. Molecular Ecology, 23(14), 3566-3586.}

\emph{Mokrani, Y., Mimeche, F., Nouidjem, Y., \& Saheb, M. (2018). Rapid
assessment of cave-dwelling bat diversity in the Chebket ES-Sellaoua
Mountains (Eastern Algeria). Arxius de Miscel* lania Zoologica, 16,
112-120.}

\emph{Raman, S., Shameer, T. T., Pooja, U., \& Hughes, A. C. (2023).
Identifying priority areas for bat conservation in the Western Ghats
mountain range, peninsular India. Journal of Mammalogy, 104(1), 49-61.}

\emph{Scherrer, D., Christe, P., \& Guisan, A. (2019). Modelling bat
distributions and diversity in a mountain landscape using focal
predictors in ensemble of small models. Diversity and Distributions,
25(5), 770-782.}

\emph{Tanalgo, K. C., Tabora, J. A. G., de Oliveira, H. F. M.,
Haelewaters, D., Beranek, C. T., Otalora-Ardila, A., \ldots{} \& Hughes,
A. C. (2022). DarkCideS 1.0, a global database for bats in karsts and
caves. Scientific Data, 9(1), 155.}

\emph{Zhou, H., Ji, J., Chen, X., Bi, Y., Li, J., Wang, Q., \ldots{} \&
Shi, W. (2021). Identification of novel bat coronaviruses sheds light on
the evolutionary origins of SARS-CoV-2 and related viruses. Cell,
184(17), 4380-4391.}\selectlanguage{english}
\begin{longtable}[]{@{}ll@{}}
\toprule
\textbf{Variable} & \textbf{Reference}\tabularnewline
Climate layers & \emph{Worldclim
https://data.biogeo.ucdavis.edu/data/worldclim/v2.1/base/wc2.1\_30s\_bio.zip}\tabularnewline
Tree density & \emph{Crowther, T. W., Glick, H.B., Covey, K. R., et al.
(2015). Mapping tree density at a global scale.Nature, 525(7568),
201-205. Available online: 10.1038/nature14967}\tabularnewline
Soil layers & \emph{ISRIC Soil grids downloaded May 8th 2023.
https://data.isric.org/geonetwork/srv/eng/catalog.search\#/metadata/f36117ea-9be5-4afd-bb7d-7a3e77bf392a}\tabularnewline
Vegetation height & \emph{Lang,N., Jetz, W., Schindler, K, Wegner, J.D.(
2019) A high-resolution canopy height model ofthe Earth
https://langnico.github.io/globalcanopyheight/}\tabularnewline
IUCN ecosystem typology &
\emph{https://global-ecosystems.org/analyse?area=-18.984375+-38.272689\%2C-18.984375+39.909736\%2C56.601563+39.909736\%2C56.601563+-38.272689\%2C+-18.984375+-38.272689}\tabularnewline
Soil water balance & \emph{Trabucco, A.,Zomer, R. J. (2019): Global
High-Resolution Soil-Water Balance. figshare. Dataset. H
ttps://doi.org/10.6084/m9.figshare.7707605.v3 Copy
citation}\tabularnewline
\bottomrule
\end{longtable}

\selectlanguage{english}
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