Evaluating environmental and ecological landscape characteristics relevant to urban resilience across gradients of land-sharing-sparing and urbanity

Within urban landscape planning, debate continues around the relative merits of land-sparing (compaction) and land-sharing (sprawl) scenarios. Using part of Greater Manchester (UK) as a case-study, we present a landscape approach to mapping green infrastructure and variation in social-ecological-environmental conditions as a function of land sparing and sharing. We do so for the landscape as a whole as well as for areas of high and low urbanity. Results imply potential trade-offs between land-sparing-sharing scenarios relevant to characteristics critical to urban resilience such as landscape connectivity and diversity, air quality, surface temperature, and access to green space. These trade-offs may be particularly complex due to the parallel influence of patch attributes such as land-cover and size and imply that both ecological restoration and spatial planning have a role to play in reconciling tensions between land-sparing and sharing strategies.

Social-ecological outcomes of urban land-sparing-sharing across scales  16  17  18  19  20  21  22  23  24  25  26  27  28  29  30  31  32  33  34  35  36  37  38  39  40  41  42  43  44  45  46  47  48  49  50  51  52  53  54  55  56  57  58 green space in promoting sparing and sharing scenarios respectively can also be clarified, which should 118 inform persisting debates within urban planning. 119 120 However, despite the need for holistic, integrated conceptualisations of urban landscapes, research on 121 urban land sparing and sharing has largely sought to reduce the complex characteristics of urban areas. 122 For example, studies have typically modelled hypothetical landscapes based on observed patterns of 123 species distribution (Caryl et al., 2016) as a response to broad land-use types such as building density 124 (Soga et al., 2014). In addition, meta-analyses drawing on a range of geographically diverse studies (Stott 125 et al., 2015) have been carried out in order to identify common trends. These reductionist approaches 126 however, have not considered wider social-ecological factors such as landscape connectivity, 127 heterogeneity, overall green cover quantity and quality or other socio-environmental factors such as 128 access to nature, urban cooling or air quality. We argue that a more holistic approach to evaluating urban 129 landscapes is necessary in order to inform planning frameworks that align with UN Sustainable 130 Development Goals. The creation of landscapes that promote human well-being, urban resilience to 131 climate change, and which address inequalities in addition to biodiversity loss, requires a green 132 infrastructure approach which considers a range of social-ecological outcomes (Lovell and Taylor demonstrated how a range of geo-computational techniques can be applied to high resolution remotely 150 sensed data integrating information on land-use and land-cover in order to achieve high levels of 151 integration necessary for studying complex social-ecological landscapes. Such advances present an 152 opportunity to explore associations between spatial configurations of green infrastructure and urban 153 social-ecological outcomes. 154 155 Conceptualizing land-sparing-sharing outcomes within a green infrastructure framework 156 157 The consideration of wider characteristics such as overall green cover and quality in urban localities is 158 particularly important if urban studies are to be based on the same robust logic as agriculture-based 159 studies on land-sparing-sharing. The latter are assessed primarily at the level of yield-to-species density 160 performance in order to compare the relative success of sparing-to-sharing scenarios (Phalan, 2018). In 161 urban areas however, the management goal is less clear or, at least, characterised with less consistency. 162 Although housing density provides a useful proxy for level of development in urban environments, this 163 comprises only one type of built infrastructure common in urbanizing landscapes. Sophisticated measures 164 of "yield" from urbanisation, comparable to the use of the term in agricultural land-sparing-sharing 165 models, are not forthcoming. A useful approach is to consider total surface sealing as a measure of 166 overall development and, therefore, as a proxy for services delivered by "grey infrastructure". The 167 question then, from a land-sparing-sharing perspective, is whether consolidating such grey infrastructure 168 into compact forms for the sake of sparing large undeveloped spaces is preferable to allowing developed 169 areas to spread out in low-density patterns. The latter implies smaller, albeit potentially more numerous 170 patches of green space and represents a lower level of urban land-use intensity that, in both agricultural 171 and urbanisation contexts, inevitably requires a larger spatial extent (Stott et al., 2015). However, in the 172 urban context, where measuring productivity is a more complex issue, in order to assess the relative 173 performance of land that remains undeveloped, it is logical to standardise comparisons of land-sharing 174 and land-sparing scenarios by the degree of development and scale. The former requires that, for the same degree of urban development (i.e. surface sealing) a direct comparison across a range of desirable 176 landscape attributes can be made between different spatial configurations. This is important for three 177 reasons. Firstly, without this standardised approach, it is not possible to assess whether relative gains (e.g. 178 land-cover diversity and connectivity) are due to spatial factors or simply a greater amount of green land-179 cover. Secondly, by taking a standardised approach, meaningful comparisons across scales of  180  investigation are thereby permitted. By developing assessments which model outcomes across scales and  181  are standardised by area, a more informed view can be taken on spatial planning approaches which  182 balance land-use productivity with landscape resilience. Thirdly, decision-makers are required to develop 183 urban spatial frameworks within defined spatial extents according to administrative boundaries. 184 Therefore, research which can identify optimum landscape configurations for a given degree of 185 development at a range of scales are desperately needed in order to allow planners to design urban areas 186 which can provide much needed ecosystem services to local residents. Such knowledge may assist 187 decision-makers to identify bottom lines for the amount of green infrastructure cover necessary at a range 188 of scales that, when consisting of suitable type and distribution, ensures both productivity and resilience. 189 190 Land itself can be thought of as the primary asset to be managed in urban areas with local planning 191 authorities working to tight spatial and regulatory constraints, and within administrative boundaries. The urban-to-peri-urban context 202 203 The spatial and temporal heterogeneity of landscapes subject to urbanisation stand in contrast to the 204 relatively homogenizing effect of land-use change by agriculture and reinforce the need for high 205 resolution, integrated data on urban spatial configurations. Gradients of urbanisation in particular create 206 complex social-ecological conditions. Rural to urban gradients have been shown to exhibit considerable 207 variation in ecosystem service provision (Radford and James, 2010; Haase, 2019), well-being effects of 208 green space (Dennis and James, 2017) and biodiversity outcomes (Turrini and Knop, 2015). Moreover, 209 urbanised landscapes covering city-regions may encompass a range of human-dominated land-uses 210 including highly compacted urban centres to low-density suburbs as well as agricultural landscapes in the 211 peri-urban fringe. Due to such contrasting land-use-land-cover configurations, calls have rightly been 212 made to employ whole-landscape approaches to modelling sparing-sharing outcomes in urban systems 213 (Lin and Fuller, 2013). In addition to whole-landscape assessments we also argue that analyses at sub-214 landscape scales e.g. within urban and peri-urban zones are necessary given that the subject of a land-215 sparing-sharing model (i.e. the land being "spared") will differ depending on the context. For example, 216 taking a sparing approach in high-urban areas will typically imply the promotion of urban intensification 217 towards consolidating larger patches of urban green space whereas, in peri-urban areas, the "spared" land 218 will likely take the form of agricultural or forestry land. This raises another important point related to a 219 land-sharing-sparing dichotomy within the context of urbanisation. Much of the debate and associated 220 research related to land-sparing and sharing in agricultural landscapes is predicated on the relative 221 success of modelled yield-species density curves within biodiversity supporting habitats. However, many 222 peri-urban landscapes typically comprise already degraded ecosystems in various stages of agricultural 223 land-use. Indeed, for some functional groups, urban areas, and residential gardens in particular, can 224 contain higher diversity and abundance than the agricultural hinterland (Cussans et al., 2010). Therefore, 225 it is entirely possible that assumptions applied to land-sparing conservation efforts in areas containing in-226 tact biodiversity-supporting vegetation, may not be applicable to landscapes made up of complex 227 juxtapositions of highly-modified land-uses. Given the variance in green infrastructure function, 228 heterogeneity and quality between urban and peri-urban areas, information on vegetation type and health 229 is a critical factor (along with spatial characteristics such as connectivity and patch size) when judging 230 the productivity and resilience of landscapes characterised by (semi-)natural and highly modified 231 habitats. 232 233 In order to address these research imperatives, a novel spatial dataset was created, following a method 234 developed by Dennis et al. (2018), which allowed the precise measurement of land-use-land-cover 235 configurations across a spatially contiguous urban area comprising the two cities of Manchester and 236 Salford, and the Metropolitan Borough of Trafford, all parts of Greater Manchester, in north-west 237 England, UK. Our overall aim was to evaluate associations between sharing-sparing scenarios on a range 238 of social-ecological-environmental factors relevant to urban landscape productivity and resilience. In 239 order to do this robustly we focussed on potential mediating factors and, as such, our objectives were 240 three-fold: 1: to assess the relative contribution of land-use-land-cover combinations to sparing-sharing 241 configurations; 2: to identify scale-effects in the performance of sparing-sharing scenarios, and 3: to 242 evaluate the relevance of urban and peri-urban contexts in assessing the relative merits of different 243 landscape configurations. 244 245 246 Methods 247 248 Spatial data on land-use and land-cover

273
Landscape and environmental metrics 274 275 A range of social-ecological metrics were quantified within 0.5, 1 and 2 km² zones created through a 276 hexagonal tessellation of the study area. The land-cover layer was used to compute a range of landscape 277 characteristics including effective mesh size (Meff), total core area (TCA), largest patch index (LPI) and 278 Shannon's land-cover diversity (SHDI) , calculated using the QGIS plug-in Lecos (Jung, 2015). Values 279 for Meff and TCA are returned in the spatial units of the source data and, in order to allow comparability 280 across scales, these were standardized as a percentage of the spatial units used in our analysis. In This was achieved by creating a mask based on all green land-cover pixels and setting this as the 288 environment for the NDVI calculation within ArcMap (version 10.4), again at units of 0.5, 1 and 2 km². 289 We refer to this metric as vNDVI in subsequent sections. Subsequently, the degree to which the 290 tessellated regions exhibited land-cover indicative of land-sparing or land-sharing was judged according to their largest patch index (LPI), following similar approaches taken elsewhere (e.g. Soga et al. 2015). 292 This metric represents the proportion of green space in a given locality that is comprised of a single 293 contiguous patch. High values therefore represent increasing sparing of large patches relative to overall 294 cover by green-space. Tessellated regions were divided into three quantile groups representing low (land-295 sharing), medium (neither land-sparing nor land sharing) and high (land-sparing) scores for LPI. Figure 2  296 gives examples of areas exhibiting low, medium and high LPI (land-sharing, neither sharing nor sparing, 297 and land-sparing respectively). 298 299 300 301

303
The influence of land-sharing/sparing on critical ecological and socio-environmental attributes was 304 assessed through a series of general linear models using the three LPI quantile groups as fixed factors. 305 Meff, SHDI, TCA, vNDVI, LST, nitrogen dioxide and percentage of the local population within 300 m of 306 a recreational green space were all entered as dependent variables whilst controlling for total green land-307 cover. Controlling for overall green cover, in addition to fulfilling the standardised approach argued for in 308 the introduction to this paper, was equally important from a methodological point of view. LPI and total 309 green land-cover were significantly correlated (at units of 1 kmᶟ, for example, Pearson's r = 0.82; p < 310 0.01). Therefore, entering green land-cover as a co-variate ensured that the LPI metric was not acting as a 311 surrogate for the former in our assessments. Analyses were repeated at low and high urbanity levels 312 (separated by the median values of developed land -i.e. non-green land-use -within each of the 0.5, 1 313 and 2 km² units of analysis In addition to the above, for models in which vegetation type was deemed to be of particular relevance 332 (i.e. where mean LST, nitrogen dioxide and vNDVI were the dependent variables), combinations of all 333 land-use and land-cover classes (proportion of the unit of analysis that is e.g. tree canopy in public parks 334 or ground layer vegetation in the urban fabric) were entered as independent variables. For analyses with 335 mean nitrogen dioxide as the dependent variable, density (m 1000 m⁻²) of major and minor roads 336 (downloaded from OS Open Roads, 2018), were also considered important predictors, as primary 337 emission sources. Regression models were carried out at the 1 km² level as this provided a more robust 338 number of cases than doing so at the 2 km² level whereas an unsatisfactorily high number of missing 339 values for the variables given in Table 1 were produced when calculated at the 0.5 km² level. All 340 statistical tests were carried out in SPSS.23. 341 342 343 Results 344 345 Land-cover and land-use attributes for the study area (form and function) are presented in Figures 3 and 4  346 respectively. The land-use classification achieved a high level of overall accuracy (92%; Cohen's Kappa 347 = 0.89, p < 0.001). Figure 5 gives the relative cover by major land-uses (those comprising > 1% of the 348 study area) and associated land-cover across low, medium and high income levels (for whole-landscape 349 and for low versus high-urban areas) at the 0.5 km² level. 350 351 352 The spatial extent and content of public and domestic green space exhibited contrasting mean values 373 between low and high urban areas. Values associated with domestic gardens in particular also showed 374 considerable variation as a function of income. For example, in terms of domestic green-space, low-urban 375 areas contained lower cover relative to high-urban areas and, within the context of the latter, higher 376 income was associated with both a larger spatial extent and a greater proportion of green land-cover. For 377 both levels of urbanity, lower income areas contained the greatest public green space cover with a higher 378 degree of surface sealing seen for this land-use in the high-urban context. Table 2 gives correlation co-379 efficients (Pearson's r) between land-use types and key indicators of urbanisation. 380 381 The relative cover by major land-use types for three quantile groups of the Largest Patch Index metric 387 within 1 km² zones (low LPI = land-sharing; high LPI = land-sparing), controlling for overall green land-388 cover, is given in Figure 6. 389 390 391 392  Contrasting patterns were observed between individual landscape metrics with TCA and SHDI in 427 particular exhibiting unique distributions along the sharing-sparing gradient employed. Figures 11 and 12  428 give the marginal mean values resulting from general linear models for socio-environmental variables 429 land surface temperature and ambient nitrogen dioxide concentration respectively. In terms of population 430 within 300 m of a recreational green space, statistical significance was exhibited only in high urban areas 431 ( Figure 13 449  450  451  452  453  Table 3 gives significance levels for models at each scale and level of urbanity considered. Overall, 454 analyses at units of 0.5 km² provided the greatest number statistically significant tests, though low-urban 455 areas did not follow this trend as closely as high-urban areas. 456 457 458  Table 4 gives the results of the multiple linear regression models with landscape metrics LPI, TCA, Meff,  464  SHDI and vNDVI as dependent variables and Table 4 summarizes regression results where socio-465  environmental variables mean LST, mean nitrogen dioxide concentration and percentage population  466 within 300 m of a recreational green space. 467 468 Table 4 Results of regressing land-use-land-cover attributes on landscape metrics used in this study. All tests carried 469 out at 1 km² units.

Low-urban
Beta Sig.  For the study area as a whole, and in areas of high urbanity, the distribution of public versus private 489 green-spaces, controlling for total green land-cover, exhibited patterns that fulfill expectations of land-490 sparing-sharing scenarios. Inverse trends were observed for mean cover of public relative to domestic 491 green space with increasing LPI (Figure 6a and c). However, in areas of low urbanity this pattern was not 492

High-urban
replicated where a dominance of public over domestic land-use was seen in land-sharing areas (i.e. low 493 LPI) with domestic green space cover highest in land-sparing areas. Our analysis suggests, therefore, that 494 the definition of land-sparing and sharing within an urban planning framework, in terms of primary land-495 uses which support this dichotomy, is subject to some fluidity as a function of urbanity. Moreover, the 496 regression results highlighted domestic green and built land-covers as critical factors contributing to the 497 largest patch index in both low and high urbanity areas, seemingly exerting a stronger influence on LPI 498 than public green-space (Table 4). This is an important observation as it challenges some of the 499 assumptions surrounding the relative patterns resulting from the prevalence of public and private green 500 spaces within green infrastructure planning frameworks (Lin and Fuller, 2013). That ratios of built-to-501 green land-cover in domestic green space were also shaped by socio-economic status ( Figure 5) suggests 502 that overall urbanity, land-cover and economic status may all comprise determinants of land-sparing-503 sharing configurations in city regions. 504 505 Level of Urbanity 506 507 Our analysis suggests that complex trade-offs may be implied by the ascendency of one or other of a 508 land-sparing versus land-sharing approach within different contexts of urbanisation. This appeared to be 509 most evident for socio-environmental factors considered. For example, models for mean LST and 510 nitrogen dioxide values exhibited differing trends between high and low areas of urbanity. For mean LST, 511 contrasting trends were observed along the sparing-sharing gradient between low and high-urban areas. 512 This mirrored similarly inverse trends for domestic green space cover, presenting the latter as a potential 513 causal factor. In the case of percentage of the local population in close proximity to a recreational green 514 space, analysis of high-urban areas suggested provision was greatest in land-sharing environments when 515 measured at a scale of 2 km². For low-urban areas however, a mixture of land-sharing and land-sparing 516 exhibited the greatest delivery of green space access. Vegetation quality (vNDVI) also exhibited highest 517 mean values within this scenario in statistically significant models in low-urban areas (0.5 and 1 km²) 518 whereas the highest values were associated with land-sparing in high-urban areas. 519 520 Although the two levels of urbanity presented some contrasting results, there was evidence of some 521 consistency related to specific spatial or class-level components. For example, regardless of scale or level 522 of urbanity, land-sparing appeared to consistently promote greater connectivity (Meff). That Meff was 523 highest in land-sparing scenarios in both urbanity contexts (even though this implied different land-use 524 patterns) suggests that individual land-use types are a minor consideration relative to spatial 525 characteristics when aiming at connectivity. In terms of land-cover, tree canopy consistently promoted 526 greater cooling (lower mean LST) and greater vegetation vigour, regardless of land-use or urbanity. This 527 implies that, as identified by others (e.g. Collas et al, 2017), restoration through afforestation may 528 significantly support and mediate broader landscape considerations in the promotion of urban ecosystem 529 services and their resilience. From the perspective of landscape heterogeneity, differences in SHDI were 530 significant between sparing-sharing scenarios in low-urban areas at the 0.5 and 1 km² scale. At these 531 scales, areas which comprised neither sharing nor sparing configurations exhibited greatest land-cover 532 diversity, with land-sharing areas also showing significantly greater mean SHDI values than land-sparing 533 areas (Figure 9). In addition, in low-urban areas peri-urban land-use appeared to play a detrimental role in 534 landscape heterogeneity (Table 4). Overall, therefore, our results point towards an increase in vegetation 535 diversity and quality in areas character rised by peri-urban land-use through the introduction of more 536 typically urban green space types (Figures 5, 6 and 9). In the high-urban context, all major green land-537 uses appeared to contribute to landscape heterogeneity (Table 4) suggesting that increases in green land-538 cover of any type are beneficial regardless of land-sparing-sharing considerations (which were not statistically relevant to SHDI in high urban areas, Table 3).  540  541  542  Scale  543  544 Associations between ecological and socio-environmental patterns and land-sparing-sharing scenarios 545 appeared to be moderated as a function of the scale of investigation employed. For example, for the study 546 area as a whole, when measured at units of 2 km², TCA appeared to be highest within spatial 547 configurations which represent land-sparing scenarios (Figure 7). In contrast, land-sparing appeared to 548 promote this critical landscape characteristic when measured at scales of ≤ 1 km². The influence of scale 549 differed between variables. For example, of the landscape attributes tested, SHDI exhibited generally 550 higher values when measured at larger scales, whereas (standardised) Meff values were highest at smaller 551 scales of investigation. In terms of levels of statistical relevance, our analysis exhibited scale-dependence 552 (Table 3). This is important from both an urban planning and nature conservation perspective. When 553 treating the study area landscape as a whole, higher levels of statistical significance were exhibited at 554 smaller scales of investigation for most variables considered (Table 3), though urbanity appeared to 555 mediate this trend. For example, in low-urban areas, analysis at scale of 1 km² returned the greatest 556 number of statistically significant tests, whereas in high-urban areas this was occurred at the 0.5 km² 557 scale. This implies that in more highly fragmented landscapes, higher spatial resolution is necessary to 558 discern land-sparing-sharing associations with environmental characteristics. potential for land-cover configurations to similarly achieve co-benefits such as urban cooling. Therefore, 565 using a multi-scale approach such as that developed here, considering multiple socio-environmental 566 characteristics relevant to sustainable urban development may be of considerable merit. This is largely 567 due to the possibility, as demonstrated here, of identifying optimum scales of analysis through relatively 568 rapid assessments using GIS and remote sensing techniques. 569 570 Influence of land-cover 571 572 Regression analyses of individual land-use and land-cover attributes on environmental and ecological 573 variables demonstrated a high degree of consistency between areas of contrasting urbanity though 574 exceptions, related to SHDI in particular, were observed (Table 4). Specifically, both peri-urban and 575 domestic land-use exhibited contrasting directions of association with SHDI dependent on whether they 576 were assessed at low or high-urbanity. The cover by, and level of vegetation within, domestic gardens in 577 particular were also subject to stark contrasts between areas of low and high urbanity ( Figure 5). These 578 disparities appeared to be underpinned by socio-economic processes. The latter, therefore, proved also to 579 be an important local consideration moderating the status, and therefore influence, of land-use-land-cover 580 combinations on ecological and environmental variables. 581 582 Cover by gardens and land-cover within gardens exhibited strong links with all socio-environmental 583 characteristics measured. Of all land-cover types, mean LST was most strongly (negatively) associated 584 with canopy cover in gardens in high-urban areas ( Table 5), suggesting that management of domestic 585 greening presents opportunities for climate resilience in cities. Green land-cover within informal and 586 other private (institutional) settings also exerted significant influence on both ecological and 587 environmental characteristics, particularly in high urban areas. This underlines the complex mosaic of 588 land-uses contributing to effective urban green infrastructure and the need for land management within 589 such spaces to be acknowledged as key components of planning for sustainable and resilient cities. 590 Gardens also appeared to exert an influence on both proximity to green space and air quality. For 591 example, domestic garden cover was positively associated with access to green space in high-urban areas 592 though, notably, public green-space (to which category green recreational spaces belonged), was non-593 significant. This suggests that, for the current study area at least, access (defined as proximity) to 594 recreational green spaces may be more closely related to population distribution than to provision of 595 green space per se. This is supported by the fact that domestic green space mean patch size -denoting 596 lower housing (and therefore population) density -was negatively associated with proximity to recreational green space (Table 5). This pattern supports other work on urban land-sparing which 598 highlights the merits of land-sharing configurations on green space use . It also 599 suggests, however, that increasing urban residential density, through compaction and in-filling may offer 600 opportunities for sparing non-developed land whilst ensuring local access to green space. 601 602 In terms of air quality, domestic garden cover showed a surprising negative association with mean 603 nitrogen dioxide concentrations: the strongest of all land-uses types for high urban areas. Specific land-604 covers within gardens did not seem to be responsible for this association ( Table 5), but that garden cover 605 correlated negatively (p < 0.01) with density of major roads (Table 2) may offer a potential explanation  606 and suggests urban form, rather than land-cover, as a critical factor. This idea is supported by results 607 reported elsewhere which suggest that complex geometric patterns created by fragmented urban forms 608 may reduce traffic-related congestion and pollution (Zhou et al., 2018). That tree cover in public green 609 spaces in low-urban areas was positively associated with mean nitrogen dioxide concentrations may 610 explain to some degree why public green-space cover overall was not statistically relevant to mean 611 nitrogen dioxide concentrations. This stands in contrast to findings in other studies highlighting the 612 ability of trees to remove nitrogen dioxide from the environment (Fantozzi et al., 2015). However, ours is 613 the first study of its kind to consider a range of vegetation types across different land-uses 614 simultaneously. The results of our regression models showed that tree canopy and lower vegetation types 615 exhibited contrasting associations with level of nitrogen dioxide with field layer vegetation showing the 616 greatest negative influence on ambient nitrogen dioxide at both levels of urbanity. Broader evidence on 617 the relationship between the urban canopy and ambient nitrogen dioxide is, however, mixed (Yli-618 Pelkonen et al., 2018) and known to be subject to meteorological factors (Grundström et al., 2015). 619 Specifically, ambient nitrogen dioxide has been shown to decrease with local air temperature (Ibid.). The 620 latter is particularly relevant given that tree cover was negatively associated with LST in our results and 621 implies a potential trade-off resulting from different socio-environmental outcomes related to the 622 presence of green infrastructure (i.e. urban cooling and air quality). Overall, cover by water in urban 623 areas suggested the greatest cooling effect by any land-cover, underlining the importance of waterways 624 and wetlands in the regulation of the urban micro-climate (e.g. Gomez-Baggethun et al., 2013). 625 626 627 Moving the land-sparing-sharing debate forward in urban areas.

629
The analysis presented here demonstrates how a landscape approach, incorporating spatially coincident 630 measures of land-use and land-cover, can be employed to unpick spatial and ecological complexities 631 relevant to sustainable urban development. Our analysis suggests three pathways for future evaluation 632 and research on landscapes subject to the process of urbanization. Firstly, scale (spatial units) should be 633 considered in planning and research where multiple socio-environmental concerns are to be addressed. In 634 the case of the former, we suggest that a modular approach working at smaller, local scales of analysis 635 should be employed to capture variables that are highly spatially sensitive. Concurrently, research should 636 focus on evaluating the potential for up-scaling analysis of small-scale phenomena (e.g. micro-climate 637 regulation) to align with larger theoretically established units of investigation of others (e.g. species 638 distribution). Secondly, spatial context in terms of levels of urbanity should be equally considered as a 639 highly significant mediating factor in the determination of optimal land-use configurations. Not only do 640 levels of urbanization modify the spatial characteristics of landscapes, but from the perspective of 641 landscape resilience and ecosystem services provision, different contexts will dictate the nature of 642 management goals related to spatial planning. For example, in urban areas where natural green cover is 643 high fragmented but may also exhibit high heterogeneity, developing landscape configurations which 644 increase connectivity per unit area may take priority over increasing diversity. Conversely, in peri-urban 645 areas where green cover consists of larger and more connected patches, but highly homogenous (e.g. due 646 to agricultural practices), land-use-land-cover combinations which promote landscape complexity rather 647 than cohesion may be prioritised. Further, our results suggests that, even when different landscape 648 configurations are promoted in urban and peri-urban areas, this may in reality involve parallel promotion 649 of the same land-use type. However, we concede that the current study used a highly simplified 650 dichotomous take on an urban-to-peri-urban gradient, controlling for overall green land-cover within each 651 zone. In reality urban-rural gradients will consist of multiple degrees of urbanisation and human density. 652 Furthermore, overall greenness of the environment and the merits of land-sparing versus sharing 653 outcomes are likely to be subject to non-linear functional relationships (Stott et al., 2015). Therefore, our 654 findings should be tested, ideally across landscapes which exhibit multiple combinations of green land-