DOI: 10.5061/dryad.2fqz6131p
title: A predictive approach to assess urban biodiversity and plan for future development scenarios

description: Protecting and enhancing biodiversity in urbanized areas is recognized as
 an important priority. To achieve this through urban planning, there must
 be empirically derived tools to predict biodiversity at the appropriate
 spatial scales and resolutions given various options in urban designs to
 compare the expected biodiversity outcomes and make optimal decisions. We
 demonstrate how this can be done by developing models that predict the
 expected species densities or ‘alpha diversity’ in urban landscapes for
 four animal groups: birds, butterflies, odonates and amphibians, based on
 assemblage data from spatiotemporally replicated surveys conducted in the
 tropical city of Singapore. We demonstrate two use cases for these
 predictive models: citywide assessment and future scenario planning. For
 citywide assessment, sub-city ‘towns’ (equivalent to districts or suburbs
 elsewhere) were compared and benchmarked relative to all other towns,
 based on the average species densities as indicators of habitat value for
 each of the four animal groups. For future scenario planning, four
 development scenarios were compared, and the compatibility of vector-type
 planning layers with the models was tested. An open-source R package,
 biodivercity, was developed that would facilitate the use of the same
 workflow elsewhere: to build, apply and validate predictive models
 elsewhere given similar available empirical data. Synthesis and
 applications: The models developed can also be examined to generate
 recommendations for further actions that can improve biodiversity across
 different spatial scales. These techniques can be incorporated into
 current planning practices to achieve a more quantitative and
 performance-based approach to enhancing biodiversity at fine spatial
 scales in human-dominated landscapes.
description: # A predictive approach to assess urban biodiversity and plan for future
 development scenarios Dataset DOI:
 [10.5061/dryad.2fqz6131p](10.5061/dryad.2fqz6131p) ## Description of the
 data and file structure ## Sampling locations Four animal groups were
 surveyed at six towns (as officially delimited) in the equatorial
 city-state of Singapore located in Southeast Asia (1.3° N, 103.8° E). For
 each town, surveys were conducted every two months across a one-year
 duration. Point locations for surveys were randomly sampled across two
 land-cover strata—natural vegetation and urban cover. Before each survey
 year, ‘natural vegetation cover’ (forest patches) within the respective
 study towns was preliminarily delineated in Google Earth; large water
 bodies were then excluded from the remaining area to form ‘urban cover’
 (which includes cultivated vegetation). The two land-cover strata were
 used to randomly generate sampling points at a density higher than the
 target of 1 point per 50 ha, to provide backup points upon inspection and
 on-site reconnaissance. Each sampling point was checked for accessibility
 and the presence of water bodies nearby. Proximity to permanent or
 ephemeral water bodies was required for surveys of two out of the four
 animal groups—odonates and amphibians. Sampling points were adjusted up to
 50 m to avoid safety hazards, or to increase their proximity to water
 bodies to be within 20 m, owing to the relatively low chance of points
 being randomly generated close to water bodies. For each survey round, the
 number of points close to water bodies was fixed at 10 per town. ## Animal
 surveys Four animal groups were assessed in this study: Birds (Aves);
 butterflies (Insecta: Lepidoptera excluding moths); dragonflies and
 damselflies (Insecta: Odonata; hereafter ‘odonates’); and frogs and toads
 (Amphibia: Anura; hereafter ‘amphibians’). Thirty-minute surveys were
 conducted by two observers for each animal group. The bird and butterfly
 surveys were conducted at all sampling points, while the odonate and
 amphibian surveys were only conducted at points that were close to water
 bodies. Counts of each species were recorded within a fixed radius of the
 sampling point; the radius of observations was set at 50 m for birds, and
 20 m for the other three animal groups. No animals were captured; visual
 and aural surveys were conducted for birds (0700–0930 hrs) and amphibians
 (2000–2200 hrs); and visual surveys were conducted for butterflies
 (0930–1200 hrs) and odonates (1400–1600 hrs). Surveys were conducted
 during fair weather conditions. For points close to ephemeral water
 bodies, the odonate and amphibian surveys were conducted within 24 hours
 after a rain event (recorded at the weather station nearest to the
 sampling point). To account for seasonal variation, surveys were repeated
 every two months at each sampling point, across a one-year duration (six
 surveys). For some survey points, there were disruptions such as
 commencement of construction work that meant that the full set of six
 surveys could not be completed; only the points with six completed surveys
 were used in the analysis (sample size *n* = 134 for birds and
 butterflies; *n* = 50 for amphibians and odonates). For each animal group,
 the total number of species observed across the six repeated surveys was
 tallied at each sampling point. ## Remote sensing of landscapes Features
 of the built environment (i.e., buildings, roads) were derived based on
 OpenStreetMap (OSM) data, downloaded using the R package *osmextract*, and
 then ground-truthed on-site during the year of the animal surveys and
 amended if necessary for accuracy. Sentinel-2 satellite images were
 downloaded using the R package *sen2r* , to derive classes of land cover
 such as vegetation and water cover at a 10-m pixel resolution. Vegetation
 cover was then sub-categorized into canopy (&gt; 2 m height) and short (≤
 2 m height) vegetation cover, based on height from ground level. The
 height from ground level was represented by the normalized digital surface
 model (nDSM) at a 0.5-m pixel resolution, derived from the LiDAR-based
 digital surface model (DSM) and digital terrain model (DTM) obtained from
 the Singapore Land Authority from airborne scans conducted in 2019.
 Landscape patterns were then quantified for each land cover class,
 summarized at each sampling point. The R package *landscapemetrics* was
 used to calculate each metric at the ‘class’ level. ### Files and
 variables #### File: data.xlsx Description: Excel file with four sheets,
 one for each animal group ##### Variables * town: The residential town in
 Singapore where the point counts were conducted. BS: Bishan; JW: Jurong
 West; PG: Punggol; QT: Queenstown; TP: Tampines; WL: Woodlands. * sprich:
 The total number of unique species encountered across the six bimonthly
 surveys at that point. * r[x]m_*: Quantified within a buffer radius of x
 m. Followed by either osm or lsm. * *osm*: From a vector layer generated
 from Open Street Maps. Followed by one of the following: *
 *_buildingAvgLvl: The weighted average height of buildings in the area,
 (∑(A × L))/(∑A), where A and L are the respective areas and number of
 levels for each building. * *_buildingFA_ratio: The gross floor area of
 buildings divided by the area of interest, (∑(A × L))/(πr^2), where A and
 L are the respective areas and number of levels for each building, and r
 is the radius of the sampling point. * *_buildingVol_m3: The total volume
 of buildings within the area of interest, ∑(A × H) where A and H are the
 respective areas and height for each building. * *_laneDensity: Represents
 the density of traffic within the area of interest, (∑(s × l))/(πr^2 ),
 where s and l denote the respective length and number of lanes of each
 road segment, and r is the radius of the sampling point. - *lsm*: A
 landscape metric extracted from the raster layer generated from remote
 sensing. Followed by _: * Land
 covers * veg: all vegetation * vegshort: short vegetation * vegcanopy:
 vegetation canopy * water: water cover * Landscape metrics | **Variable
 Code** | **Name** | | :------------------------------ |
 :----------------------------------------- | | ***\\_ai*** | Aggregation index | | ***\\_area\_mn*** | Patch area (mean) | | ***\\_circle\_mn*** | Related circumscribing circle (mean) | |
 ***\\_clumpy*** | Clumpiness index | | ***\\_contig\_mn*** | Contiguity index (mean) | | ***\\_division*** | Division index | | ***\\_ed***
 | Edge density | | ***\\_enn\_mn*** | Euclidean nearest
 neighbor distance (mean) | | ***\\_frac\_mn*** | Fractal
 dimension index (mean) | | ***\\_gyrate\_mn*** | Radius
 of gyration (mean) | | ***\\_lpi*** | Largest patch
 index | | ***\\_lsi*** | Landscape shape index | |
 ***\\_mesh*** | Effective mesh size | | ***\\_nlsi*** | Normalized landscape shape index | | ***\\_np*** | Number of patches | | ***\\_para\_mn*** | Perimeter-area ratio (mean) | | ***\\_pd*** | Patch density | | ***\\_pladj*** |
 Percentage of like adjacencies | | ***\\_pland*** |
 Percentage of landscape | ## Code/software All sampling design, data
 processing, and analyses in this study were performed using the
 open-source statistical software R v. 4.1.1.