DOI: 10.5061/dryad.s1rn8pkcm
title: Data from: Multiple facets of biodiversity are threatened by mining-induced land-use change in the Brazilian Amazon

description: Aim  Mining is increasingly pressuring areas of critical
 importance for biodiversity conservation, such as the Brazilian Amazon.
 Biodiversity data are limited in the tropics, restricting the scope for
 risks to be appropriately estimated before mineral licencing decisions are
 made. As the distributions and range sizes of other taxa differ markedly
 from those of vertebrates – the common proxy for analysis of risk to
 biodiversity from mining – whether mining threatens lesser-studied
 taxonomic groups differentially at a regional scale is unclear. Location
 Brazilian Amazon Methods We assess risks to several facets of biodiversity
 from industrial mining by comparing mining areas (within 70km of an active
 mining lease) and areas unaffected by mining, employing species richness,
 species endemism, phylogenetic diversity, and phylogenetic endemism
 metrics calculated for angiosperms, arthropods, and vertebrates. Results
 Mining areas contained higher densities of species occurrence records than
 the unaffected landscape, and we accounted for this sampling bias in our
 analyses. None of the four biodiversity metrics differed between mining
 and non-mining areas for vertebrates. For arthropods, species endemism was
 greater in mined areas. Mined areas also had greater angiosperm species
 richness, phylogenetic diversity, and phylogenetic endemism, although
 lower species endemism than unmined areas. Main Conclusions Unlike for
 vertebrates, facets of angiosperm and arthropod diversity are relatively
 higher in areas of mining activity, underscoring the need to consider
 multiple taxonomic groups and biodiversity facets when assessing risk and
 evaluating management options for mining threats. Particularly concerning
 is the proximity of mining to areas supporting deep evolutionary history,
 which may be impossible to recover or replace. As pressures to expand
 mining in the Amazon grow, impact assessments with broader taxonomic reach
 and metric focus will be vital to conserving biodiversity in mining
 regions.
description: Database Assembly Mapping
 Mining Areas We obtained spatial information on
 mineral prospecting and mineral mining leases within the Brazilian Amazon
 from SIGMINE (Sistema de Informações Geográficas da Mineração; DNPM,
 2012). This database catalogues all registered legal mining activities
 within Brazil, detailing the extent of each activity, dates of operation,
 and mined commodities. To map ‘mining leases’ of industrial-scale mineral
 mines, we selected records greater than 100 hectares in area and
 classified as mining concessions (<em>Concessão de Lavra</em>)
 and omitted leases extracting water or those classified as small-scale
 artisanal operations (<em>Lavra Garimpeira</em>). This
 resulted in 411 polygons (including active leases and adjacent extensions
 of such leases) of 15,750 km<sup>2</sup> in total area, with
 mining start dates ranging from 1944 to 2017 (mean = 1978, sd = 11.9; Fig.
 1). To map ‘mining areas,’ which include the direct (i.e., immediate
 land-use change resulting from mineral extraction) and indirect (i.e.,
 extensive land-use change associated with mineral extraction, processing,
 and transportation) impacts of mining on forests (Sonter<em> et
 al.</em>, 2017), we created a 70 km buffer surrounding each mining
 lease. ‘Non-mining areas’ (i.e., areas unaffected by industrial mining)
 were mapped by extracting our mapped ‘mining areas’ and an additional
 layer representing all other legal mining leases excluded from our
 analyses (i.e., inactive leases, those targeting water, or operations
 smaller than 100 hectares in area; shown in white in Fig. 1) from the
 Brazilian Amazon (Fig. 1). For interpolation analyses, hexagons
 are the most logical sampling unit shape as their centroids are
 equidistant, the distance of points from the edges to the centroid is the
 closest, and sampling biases are reduced due to their lower perimeter-area
 ratio compared to squares or triangles (Birch<em> et al.</em>,
 2007). Hexagons of approximately 0.5° with equal area were assigned to one
 of two study areas – mining areas or non-mining areas – based on where
 their centroid was located (Fig. 1). Hexagons were omitted from our
 analyses if they contained fewer than 20 occurrence records per taxonomic
 group or their centroid was located outside the Brazilian Amazon. We used
 0.5° hexagon sampling units as sensitivity analyses conducted in previous
 studies utilising the same dataset indicated reduced variation in results
 for hexagon areas of 0.5° and above (Oliveira<em> et al.</em>,
 2017a; Strand<em> et al.</em>, 2018) and so any fine-scale
 georeferencing inaccuracies remaining in the dataset after filtering are
 minimised (Oliveira<em> et al.</em>, 2017b). This sampling
 unit area also ensured sufficient sample sizes would be assigned within
 and among mining-induced deforestation-affected areas to enable robust
 comparisons across the study area for all taxonomic groups, particularly
 arthropods, while reducing the amount of area hexagon interpolations may
 sample from outside their respective study area polygons. Assembling Biodiversity
 Data Data on species occurrences were
 obtained from (Oliveira<em> et al.</em>, 2017a) and
 (Oliveira<em> et al.</em>, 2019a) and represent the most
 comprehensive dataset of species occurrences in Brazil to date. These data
 were assembled from online databases spanning GBIF (gbif.org); CRIA
 (specieslink.net); Birdlife International (birdlife.org), Herpnet
 (herpnet.org), Nature Serve (natureserve.org); and Orthoptera Species File
 (orthoptera.speciesfile.org). These data were also supplemented with
 occurrence records obtained from taxonomic literature and biodiversity
 inventories (Oliveira<em> et al.</em>, 2017a;
 Oliveira<em> et al.</em>, 2019a). All species occurrence
 records were filtered to determine if they lacked geographic coordinates
 or exhibited location errors using a map of Brazilian municipalities
 (mapas.ibge.gov.br; Oliveira<em> et al.</em>, 2017a;
 Oliveira<em> et al.</em>, 2019a). Taxonomic validity for all
 occurrence records was confirmed using taxon-specific catalogues and
 expert reviews for each taxonomic group (Oliveira<em> et
 al.</em>, 2017a; Oliveira<em> et al.</em>, 2019a). After
 filtering for geographic and taxonomic accuracy, the final dataset
 comprised 113,790 occurrence records for all the Brazilian Amazon. The
 dataset contained 44,660 records of angiosperms (6899 species of families
 Asteraceae, Bromeliaceae, Fabaceae, Melastomataceae, Myrtaceae,
 Orchidaceae, Poaceae, and Rubiaceae), 24,374 records of arthropods (4630
 species of bees, spiders, millipedes, Orthoptera, dragonflies, moths and
 Diptera), and 44,756 records of vertebrates (1584 species of birds,
 mammals, and anurans). Spatial distributions of occurrence record
 densities for each taxonomic group are provided in the supporting
 information (Fig. S1). Phylogenetic trees were constructed
 from published figures into Newick code with TreeSnatcherPlus (Laubach
 & Von Haeseler, 2007) and supplemented with data from empirical
 phylogenetic studies synthesised by The Open Tree of Life project
 (Hinchliff<em> et al.</em>, 2015). As branch lengths, when
 available, are not directly comparable between trees, all branch lengths
 were considered equal to one (Oliveira<em> et al.</em>, 2017a;
 Oliveira<em> et al.</em>, 2019a). Phylogenetic trees were
 compiled into a supertree using matrix representation with parsimony
 (Baum, 1992) and pruned to represent species restricted to Brazil. Our
 dataset represents the most extensive collection of species occurrence
 records and phylogenetic trees compiled in Brazil for this purpose to date
 (Oliveira<em> et al.</em>, 2017a). However, data collected for
 environmental impact assessments that are not published online will
 inevitably be missing from our database, and rare, threatened, or
 range-restricted organisms may also not be included due to limited
 sampling. Calculation of Biodiversity
 Facets Sampling Effort We first
 intersected mining lease and mining area polygons with species occurrence
 records to provide a coarse estimate of the proportion of occurrence
 records within mining leases and their more expansive impact areas from
 the total contained in our database. An equal area measure was calculated
 through the ‘Sampling Effort’ functor of the BioDinamica plug-in
 (Oliveira<em> et al.</em>, 2019b) of Dinamica EGO
 (Ferreira<em> et al.</em>, 2019), which was set with a 10 km
 search radius due to limited and sporadic biodiversity sampling in the
 Brazilian Amazon (Oliveira<em> et al.</em>, 2016;
 Oliveira<em> et al.</em>, 2017a). We then converted the output
 raster to points and summed the mean sample effort index values across
 0.5° radius hexagon sampling units (Fig. 1). The ‘Sampling Effort’ functor
 in BioDinamica employs a Gaussian kernel density index function. For all
 analyses using BioDinamica, 0.5° hexagon sampling units were only created
 where ≥ 20 species occurrence records existed. Biodiversity Metrics
 We
 calculated four sampling-effort-corrected biodiversity metrics for each of
 the three taxonomic groups: species richness, species endemism,
 phylogenetic diversity, and phylogenetic endemism, since measuring
 biodiversity with species richness alone does not capture values pertinent
 to conservation at the landscape scale, such as endemism or evolutionary
 history (Faith, 1992; Faith<em> et al.</em>, 2004). Indeed,
 the loss of species is not equivalent to the loss of evolutionary history
 (Vane-Wright<em> et al.</em>, 1991), and conservation priority
 areas can differ when using species richness and phylogenetic diversity
 (Rodrigues<em> et al.</em>, 2005; Forest<em> et
 al.</em>, 2007). Furthermore, phylogenetic measures may capture the
 quantity and distribution of diversity better than species-based measures,
 especially when data are limited, but both are representative of different
 diversity components (Rosauer & Mooers, 2013; Tucker<em> et
 al.</em>, 2017). Thus, here we employ a variety of biodiversity
 metrics for comparison between mining and non-mining areas in the
 Brazilian Amazon. Species-based Metrics
 Species richness
 (per unit area) is the most sensitive biodiversity measure to variation in
 sampling effort (Oliveira<em> et al.</em>, 2016). To quantify
 species richness, we used a resampled species richness index to account
 for variation in sampling effort. The ‘Resample Species Richness’ functor
 (Oliveira<em> et al.</em>, 2019b) operates by spatially
 resampling species occurrences. We set this functor to a minimum of 20
 species occurrences per hexagon sampling unit, taking a random 25%
 subsample with 1000 iterations. This tolerance level retained the most
 variation in species richness while maintaining an adequate sample size to
 compare mining and non-mining areas. The output represents the mean
 resampled species richness per hexagon (Oliveira<em> et
 al.</em>, 2017a; Oliveira<em> et al.</em>, 2019a). This
 method provides a relative measure of species richness, simulating uniform
 sampling throughout the study area, thus addressing variation in sampling
 effort (Oliveira<em> et al.</em>, 2017a; Oliveira<em> et
 al.</em>, 2019a)<strong>.</strong> For
 comparisons of endemism, the level of geographic restriction among
 species, we used the weighted endemism index, a relative measure of
 endemism as opposed to an absolute measure (Williams & Humphries,
 1994). We computed this using the ‘Weighted Endemism’ functor
 (Ferreira<em> et al.</em>, 2019), which calculates the inverse
 of a species distribution area and sums it across hexagon sampling units
 (Oliveira<em> et al.</em>, 2017a; Oliveira<em> et
 al.</em>, 2019a). To control for variation in sampling effort and
 uncertainty in species distribution estimations, the functor generates a
 sampling effort-corrected and area-weighted endemism index using the
 equation: A * B / ((A * B) + ((1 - A) * (1 - C)))  where ‘A’ is the weighted endemism as determined by the inverse of a species distribution area, ‘B’ is the product of weighted endemism and sampling effort (expressed as the mean kernel density index of species occurrence records within a 50 km radius of each occurrence point for each species analysed), and C is the total number of species sampled, with 150 species occurrences set as the maximum (0.999) as the frequency distribution of species records reaches an asymptote at this value (Oliveira<em> et al.</em>, 2017a). Species with fewer records are assigned values beginning at 0.00001 for a single record upwards linearly (Oliveira<em> et al.</em>, 2017a). Phylogenetic Metrics We considered two measures of spatial phylogenetic variation important for the maintenance and persistence of biodiversity, phylogenetic diversity (PD), a measure of divergence in phylogenetic relationships between species in an area (Faith, 1994), and phylogenetic endemism (PE), a measure of the restriction of phylogenetic lineages between given areas (Rosauer<em> et al.</em>, 2009). Phylogenetic diversity is estimated by comparing summed distances between phylogenetic branches among species in a given area (Faith, 1992). Phylogenetic endemism is estimated by comparing the relative rarity of evolutionary lines among taxa between areas, with fewer branches at higher taxonomic classifications being afforded greater weighting in contributing to the endemism of a species’ evolutionary lineage (Rosauer<em> et al.</em>, 2009). We compared phylogenetic diversity using branch lengths as a surrogate for the uniqueness or similarity in features of species within a phylogenetic tree (Faith, 1992). The ‘Phylogenetic Diversity’ functor sums branch lengths through the root of phylogenetic trees using the shortest path between species connected within a sampling unit (Faith, 1992, 1994; Oliveira<em> et al.</em>, 2017a). Due to the paucity of phylogenetic information for Brazilian species and hence within our database, despite it being the most comprehensive dataset compiled in Brazil to date (Oliveira<em> et al.</em>, 2017a), branches were assigned equal lengths under an assumption of equivalent rates of feature descent across all phylogenetic pathways (Faith, 1992). In comparing phylogenetic measures of biodiversity, we used phylogenetic trees for taxa geographically restricted to Brazil (Oliveira<em> et al.</em>, 2017a). To compare phylogenetic endemism, a measure of geographic restriction of phylogenetic diversity and hence evolutionary history, we used the phylogenetic weighted endemism index (Rosauer<em> et al.</em>, 2009). This index employs a relative measure of endemism rather than an absolute measure to compare geographic concentrations of evolutionary history, to address the sensitivity to spatial scale apparent in absolute endemism measures (Rosauer<em> et al.</em>, 2009). The ‘Phylogenetic Endemism’ functor (Ferreira<em> et al.</em>, 2019) interpolates phylogenetic weighted endemism indices across sampling hexagons by summing the phylogenetic branch lengths between species (Oliveira<em> et al.</em>, 2017a; Oliveira<em> et al.</em>, 2019b). Data Processing and Analysis Sampling effort and biodiversity indices were generated in the BioDinamica plug-in extension (Oliveira<em> et al.</em>, 2019b) for the freely available Dinamica EGO software (Ferreira<em> et al.</em>, 2019). The outputs from Dinamica were processed and assigned to study areas in ArcMap 10.7 (ESRI, 2018). Due to the amount of species occurrence records present in a sampling unit for each taxonomic group and the availability of phylogenetic tree data for each occurrence, the number of groups compared between mining and non-mining areas varied, as hexagon sampling units were removed from analyses if they contained &lt; 20 occurrence records per taxonomic group (see N column in Table S1). Statistical comparisons between mining and non-mining areas for all metrics and all taxonomic groups were made using two-tailed Wilcoxon rank-sum tests, with all graphical representations created in R (R Core Team, 2018). For improved visualisation, the four biodiversity metrics were rescaled between 0 and 1 to facilitate interpretation using the scales package in R (Wickham & Seidel, 2020), which maintains identical data spread. Sensitivity analyses showing the small and largely inconsequential variation in results when using alternative potential impact buffers of 20 km and 50 km are included in the supporting information, noting that the largest difference in findings is observed for arthropod phylogenetic endemism where the sample size is substantially reduced (mining area hexagon n = 17) when using a 20 km buffer versus a 70 km buffer (mining area hexagon n = 53; Fig. S2, Fig. S3). Effect sizes are reported (Table S1) and compared in sensitivity analyses of mining risk buffers (Fig. S3) to show the magnitude of difference in metrics between study areas and to support comparisons with future analyses. However, they are not to be interpreted as measures of mining impact on biodiversity due to the abstract complexity of their biological interpretation as they relate to differences in interpolated biodiversity metrics.
description: GIS Software, e.g. ArcGIS, R, GRASS, QGIS.
 Dinamica Environmental Modelling Software, with BioDinamica
 plug-in. Available at &lt;https://csr.ufmg.br/dinamica/&gt;