香京julia种子在线播放

The study was carried out within a 56.5 km² area including the populated area of Innsbruck (Austria) and its surroundings (approximately 47°13N, 11°19E–47°17N, 11°26E; elevation ranged from 574 to 1,024 m; Figure 1). Innsbruck has a continental climate with cold and dry winters (average min −11°C, average max 4°C, average total precipitations/month 88 mm as rain and snow), warm and wet summers (average min 3°C, average max 21°C, average total precipitations/month 192 mm) (World Weather Online, 2022). With its 104.9 km² and 131,500 habitants, Innsbruck is a small to medium size city, as the majority of cities in Europe (Nabielek et al., 2016). The city is a mosaic of buildings and concrete areas (i.e., commercial, residential and industrial areas), urban parks (e.g., Innsbruck Hofgarten, Rapoldi Park) and urban corridors (e.g., along the Inn river that crosses the city). Its woody vegetation is diverse and includes maples, beeches, birches, spruces, pines, plane trees, oaks, walnut trees, horse chestnuts, poplars, dogwoods, apple trees, sherry trees, plum trees, thuyas, etc. Innsbruck is surrounded by villages (e.g., Axams, Natters, Aldrans), coniferous and mixed-forests (dominated by beech, oaks, firs, larches and pines), agricultural land and natural areas, including the 727 km² Karwendel Natural Park. There characteristics result in a pronounced gradient from urban to natural or near-natural landscapes. Such a patchy landscape offers an ideal research site to understand urban ecosystems. The 56.5 km² grid was divided into 320 cells of 500 m × 500 m. Each cell centroid was considered as a potential sampling site. Out of these 320 potential sites, 180 were finally selected in a way to (i) cover the urbanisation gradient range and (ii) assure sampling feasibility (sites located on the airport ground, in large agricultural or concrete areas without trees were excluded).

We estimated the percent area of distinct land use/land cover (LULC) classes as well as the average value of the composite landscape index “distance to nature” (D2N), within 100, 500, and 1,000 m radii around each sampling site. LULC data was derived from the Land Information System Austria (LISA) which provides a detailed vector-based dataset for the study area with 13 LC classes (Banko et al., 2014). From these data, we computed four indexes of urbanisation levels: percent of impervious surface cover, vegetation cover (i.e., the percentage of land covered with trees, shrubs, or herbs), high vegetation cover (i.e., the percentage of land covered with trees) and mid-high vegetation cover (i.e., the percentage of land covered with trees or shrubs). In addition, we calculated D2N to quantify the anthropogenic influence on a given site by considering the degree of naturalness of a location as well as the distance to the next natural habitat patch (Rüdisser et al., 2012). Finally, we calculated the distance between each sampling site and (i) Innsbruck city centre (47°16′2″N, 11°23′34″E), (ii) the closest neighbourhood (defined as a group of a minimum of five residential buildings), and (iii) the closest house (or other residential building) using Google maps. This means that each 180 site has been characterised by 18 different indexes of urbanisation level (Figure 2). In addition, we categorised the high vegetation cover of each site as either mainly deciduous, mainly coniferous (when 70% of the high vegetation cover was composed of broadleaf trees or coniferous trees, respectively) or mixed.

Arthropods were sampled every other month during one year from October 2020 to August 2021. Temperatures during the time of the study were normal, but average rainfall was particularly high in December 2020 (176.3 mm, mostly as snow) compared to previous years (World Weather Online, 2022). There was snowfall from early-December 2020 to mid-April 2021. Each of the six sampling sessions lasted 10 days and arthropods were sampled at three new sites per day, from 8 am to 2 pm. In total, 180 sites (30 sites per sampling session) were visited once during the study. The 30 sites within each session were selected in a way to representatively cover the sampling grid and the urbanisation gradient. At each sampling site, five trees and five bushes were selected. Arthropods in the canopy and the bush layer were sampled using the branch beating method (two branches per tree and bush were beaten five times above an entomologist umbrella). In addition, arthropods on tree bark (from the ground to 1.5 m high) were sampled using a vacuum. Arthropods were stored in 80% ethanol until identification. All the individuals were identified to the family level, except the following taxonomic groups, that were identified to the lowest taxonomic level as possible. The order of Diptera was sorted into the two suborders Nematocera and Brachycera. Hymenoptera was sorted into the paraphyletic infraorder Parasitica, the suborder Symphyta and the family Formicidae. The subclass of Acari and the order of Collembola were sorted to order level. All the Araneae were identified to the family level, except for Thomisidae and Philodromidae that were pooled together and classified as crab spiders. The number of individuals per taxonomic group was counted. Because of the high number of springtails (Collembola), the abundance was estimated by measuring the volume of the tube filled with individuals. While such method may be biased in case springtail size differs along the rural-urban gradient, the fact that our analyses on springtail occurrence and abundance provide similar results (see section 3.4 and Supplementary Appendix E) suggests that such a bias is unlikely to change our conclusions.

Statistical analyses were performed using R software (version 4.1.2). Arthropod abundance (the total number of individuals), richness (the number of different taxonomic groups) and diversity (the Shannon diversity index) were compared using linear models with abundance (after log-transformation), richness or diversity as the response variable, and urbanisation level (computed as either impervious surface cover, vegetation cover, high vegetation cover, mid-high vegetation cover at a 100, 500, or 1,000 m radius around the sampling point) as the explanatory variable. Because we expected urbanisation to have different effects on arthropod communities in different seasons and micro-habitats, the month (i.e., October, December, February, April, June, and August), the micro-habitat (i.e., the canopy, the bush layer, and tree bark), their interaction as well as their interactions with the urbanisation level were added as explanatory variables. For each model, we performed a backward stepwise selection using the AIC (Sakamoto et al., 1986). A Type III Wald Chi-square test Anova was used to determine the significance of retained variables in the final models. When “month,” “micro-habitat” or their interaction were retained in the models, contrasts among groups were tested using the Tukey’s method for pairwise comparisons of estimated marginal means (“emmeans” function of the “emmeans” package) (Lenth, 2022). When interactions between “urbanisation level” and “month” and/or “micro-habitat” were retained in the models, the association between the response variable and “urbanisation level” was tested for each group using the “emtrends” function of the “emmeans” package (“emtrends” performs t-test adjusted for multiple comparisons for testing that slopes for each level of the factor are not equal to zero). However, because different taxonomic groups were identified to different taxonomic levels (e.g., family, order) and because the taxonomic composition differed between the three micro-habitats (see section 3.4.), comparing arthropod richness and diversity between micro-habitats was not relevant in our study.

Differences in the taxonomic composition of arthropod communities were tested using partial linear constrained ordination methods based on Hellinger distances (pRDA) on log-transformed abundance data and presence-absence data using the “rda” function of the “vegan” package (Oksanen et al., 2020). The global models included the 18 indexes of urbanisation level as well as “month” and “micro-habitat” as constraining variables. Because urbanisation level was associated with the type of high vegetation (i.e., less urbanised sites were more often coniferous forests), the latest was added as conditional variable. First, the significance of the global test was tested. Second, the environmental variables that were the most important to explain the compositional changes were identified by forward selection using the “ordistep” function from “vegan.” The variance explained by the constrained ordination and by each of the components was tested by Monte Carlo permutation test (“anova” function in the package “vegan”). Variation partitioning was calculated using the “rdacca.hp” function of the package of the same name (Lai et al., 2022). The contribution of each selected constraining variable to the components of the ordination was fitted by linear contribution scores using the “envfit” function. The ordination diagrams were created with the “ordiplot” function from “vegan,” which coordinates were extracted for use in “ggplot2” (Wickham, 2016). Because urbanisation indexes shared contribution to the components of the ordination with “month” and “micro-habitat” (see Table 2), the ordination analysis on the full dataset did not allow to clearly measure the effects of urbanisation on the variation in taxonomic composition between sites. For this reason, ordinations were performed (i) per month while adding the micro-habitat as conditional variable and (ii) per micro-habitat while adding the month as conditional variable.

In 10 out of the 18 models, arthropod abundance was influenced by the interaction between urbanisation level and the micro-habitat (Table 1A): arthropod abundance in the bush layer increased with increased urbanisation level (with increasing impervious surface cover or D2N, or with decreasing high or mid-high vegetation cover) (Table 1B). For instance, as the impervious surface cover in a 100 m radius increased by 10%, arthropod abundance in the bush layer increased by a bit more than six individuals (Figure 3). Indeed, an average of 67 individuals were collected at highly urbanised sites (i.e., at sites for which impervious surface covered more than 45% of the land in a 100 m radius—the 3rd quartile), while only 45 were collected at less urbanised sites (i.e., at sites for which impervious surface covered less than 9% of the land in a 100 m radius—the 1st quartile). On the contrary, the abundance of arthropods on tree bark decreased with increasing urbanisation level, while arthropod abundance in the canopy did not significantly change along the rural-urban gradient (Table 1B). For example, as the impervious surface cover in a 100 m radius increased by 10%, arthropod abundance on tree bark decreased by a bit more than eight individuals (Figure 3). Indeed, an average of 95 individuals were collected at less urbanised sites (i.e., at sites for which impervious surface covered less than 9% of the land in a 100 m radius), while 87 individuals were collected at highly urbanised sites (i.e., at sites for which impervious surface covered more than 45% of the land in a 100 m radius). Moreover, arthropod abundance was influenced by the interaction between distance to the closest house and month (Table 1A): in February and April, arthropod abundance decreased with increasing distance to the closest house (Table 1C). Finally, arthropod abundance varied in response to the month and the micro-habitat (Table 1A); variations in arthropod abundance between months are detailed in Supplementary Appendix A.

In 13 out of the 18 models, arthropod richness is influenced by the interaction between urbanisation level and micro-habitat (Table 1A): in the canopy, richness decreased with increasing urbanisation level which was also true for the bush layer when considering impervious surface cover in a 1,000 m radius, or vegetation cover in a 500 and 1,000 m radius as index of urbanisation (Table 1B). For instance, as the impervious surface cover in a 1,000 m radius increased by 10%, arthropod richness in the canopy decreased by 0.63 (Figure 3): an average of 13 taxonomic groups were collected at less urbanised sites (i.e., sites for which impervious surface covered up to 13% of the land in a 1,000 m radius—the 1st quartile), while only 10 were collected at highly urbanised ones (i.e., sites for which impervious surface covered more than 45% of the land in a 1,000 m radius—the 3rd quartile). In the bush layer, as the impervious surface cover in a 1,000 m radius increased by 10%, arthropod richness increased by 0.27 (Figure 3): an average of 10 and 9 taxonomic groups were collected at less and highly urbanised sites, respectively. Arthropod richness also increased with increasing distance to the city centre or with decreasing D2N, whatever the micro-habitat (Table 1A). In two models only (when considering impervious surface in a 100 m and 500 m radius), richness is also influenced by the interaction between urbanisation level and month (Table 1A): in June and October, it decreased with increasing urbanisation level; the direction of the relationship was the same in April and February, although the correlation was not significant (Table 1C). Finally, arthropod richness is affected by the interaction between micro-habitat and month (Table 1A); variations in arthropod richness between months are detailed in Supplementary Appendix B.

In 13 out of the 18 models, arthropod diversity was influenced by the interaction between urbanisation level and micro-habitat (Table 1A): in the canopy and the bush layer, diversity decreased with increasing urbanisation level (Table 1B). For instance, the Shannon diversity index in the canopy was 1.93 (out of a maximum of 2.55) at less urbanised sites (i.e., sites for which impervious surface covered up to 13% of the land in a 1,000 m radius—the 1st quartile), while it was 1.58 (out of a maximum of 2.30) at highly urbanised ones (i.e., sites for which impervious surface covered more than 45% of the land in a 1000 m radius—the 3rd quartile) (Figure 3). In the bush layer, it was 1.66 (out of a maximum of 2.31) and 1.59 (out of a maximum of 2.28) at less and highly urbanised sites, respectively (Figure 3). In only three models, arthropod diversity was influenced by the interaction between urbanisation level and month (Table 1A): diversity decreased with increasing urbanisation level (impervious surface cover in a 500 and 1,000 m radius, or vegetation cover in a 500 m radius) in June only (Table 1C). In the remaining models, arthropod diversity varied between months (Table 1A); variations in arthropod diversity between months are detailed in Supplementary Appendix C.

The results of the pRDA were largely similar when using taxa abundance and presence/absence. Therefore, we present below the results of the pRDA on abundance data; the results of the pRDA on presence/absence data are detailed in Supplementary Appendix E. The global constrained ordination showed that the environmental variables significantly explained the variance in taxonomic composition between sampling sites (F = 6.08, df = 25, P = 0.001): all 20 environmental variables explained 22.6% of the variance in the dataset. The conditional variable “tree type” explained 2% of the variance. From these 20 variables, six significantly explained the variation in taxonomic group composition: “month,” “micro-habitat,” “impervious surface cover (500 m),” “D2N (100 m),” “distance house,” and “high vegetation cover (1,000 m).” All six selected environmental variables explained 20.3% of the variation in taxonomic group compositions (F = 12.38, df = 11, P = 0.001). Variation partitioning was as follows: “month” 10%, “micro-habitat” 8.4%, “impervious surface (500 m)” 0.8%, “D2N (100 m)” 0.6%, “distance house” 0.3%, and “high vegetation (1,000 m)” 0.5%. Overall, urbanisation level explained 10.7% of the total variance explained by the ordination analysis, while “month” explained 48.5% and “micro-habitat” explained 40.8%. Out of the 11 constrained axes, the first six axes were significant: RDA1 (F = 64.44, P = 0.001), RDA2 (F = 31.85, P = 0.001), RDA3 (F = 15.80, P = 0.001), RDA4 (F = 8.23, P = 0.001), RDA5 (F = 5.76, P = 0.001), and RDA6 (F = 3.17, P = 0.001). RDA1 is strongly correlated with “micro-habitat” while RDA2, RDA3, and RDA5 are primarily associated with “month.” RDA4 and RDA6 are associated with “month” and the four indexes of urbanisation level (Table 2).

RDA1 and RDA2 were interpreted to understand how arthropod communities varied according to “month” and “micro-habitat,” independently of the urbanisation level (Figure 4 and Supplementary Figure D1). RDA1 separates arthropod communities on tree bark, characterised by higher abundances of springtails (Collembola) and oribatid mites (Oribatida), from arthropod communities in the bush layer and the canopy, characterised by higher abundances of aphids (Aphididae) and lice (Psocodea) nymphs. RDA2 separates arthropod communities in winter (i.e., December and February), characterised by higher abundances of planthoppers (Issidae) and outer barklice (Ectopsocidae), from arthropod communities in summer (i.e., June and August), characterised by higher abundances of ants (Formicidae) and sac spiders (Clubionidae). Finally, communities in February were more bark-like while in October, they were more bush and canopy-like; this is in agreement with the results of the models on arthropod abundance (see section 3.1.).

Constrained ordinations were tested per micro-habitat and per month to better identify the effects of urbanisation level on taxonomic composition. The full data set on environmental variables significantly explained the variance in taxonomic composition between sampling sites irrespectively of the model, except for the RDA on tree bark (Table 3). Depending on model, the 18 proxies of urbanisation level explained together between 8 and 22.8% of the variation in arthropod community composition among sites; between one and six environmental variables significantly explained such variation (Table 3) but only one or two axes were significant per model (Figure 5).

Response to urbanisation was taxon, month and micro-habitat specific. Overall, the numbers of spiders from four of the ten families present in our study area—tangle web spiders (Theridiidae), orb-weaver spiders (Araneidae), ghost spiders (Anyphaenidae), and sheetweb weavers (Linyphiidae) (in the canopy and the bush layer), springtails (in the canopy and on tree bark), and pin cushion millipedes (Polyxenidae) (on tree bark) decreased along the rural-urban gradient. On the contrary, the abundances of crab spiders (in the bush layer and on tree bark), barklice (in the canopy and the bush layer), flies (in the canopy and on tree bark), aphids (in the canopy) and leafhoppers (Cicadellidae) (on tree bark) increased with increasing habitat urbanisation level. Other taxonomic groups responded positively or negatively to the urbanisation level at specific time of the year. Of interest is the fact that more taxonomic groups negatively responded to urbanisation in February, while more taxonomic groups positively responded to urbanisation in August and October; in addition, more taxonomic groups responded negatively to urbanisation in the canopy and the bush layer than on tree bark. The results of the redundancy analyses per month (while controlling for between micro-habitat differences) and per micro-habitat (while controlling for between month differences) are summarised in Table 4; they are based on the interpretation of the redundancy analysis triplot for each month or for each micro-habitat (Figure 5). When only one RDA component significantly structured the communities, the results are based on the “species” coordinates (“scores”) on this single axis. Largely similar results were obtained on presence/absence data (see Supplementary Appendix E). Therefore, the results above can also be interpreted as a variation in taxonomic group occurrence along the rural-urban gradient.

Whatever the season, both the richness and the diversity of arthropods in the canopy and in the bush layer significantly decreased with increasing urbanisation level. More specifically, arthropod richness in the canopy and in the bush layer were around 30 and 20% higher at less urbanised sites than at highly urbanised ones, respectively. Similarly, arthropod diversity in the canopy and in the bush layer was around 10% higher at less urbanised sites than at highly urbanised ones. Such a decrease in arthropod richness and diversity along the rural-urban gradient likely results from urban environments selectively filtering species based on their traits. Our study shows that urbanisation mainly filters web spiders and springtails, which occurrence decreased along the rural-urban gradient. Both groups being wingless, our results support the findings of previous studies that suggested that arthropod dispersal ability is one of the main trait filtered by urbanisation, for instance with non-flying species being replaced by flying species along the rural-urban gradient (Kotze et al., 2011; Vergnes et al., 2014; Buchholz et al., 2018; Piano et al., 2020; Korányi et al., 2022). Such a hypothesis is also supported by our results on micro-habitat-specific effects of urbanisation (see section 4.3.).

Contrary to arthropod communities in the canopy and the bush layer, communities on tree bark did not vary with urbanisation level in terms of richness and diversity. Compared to the canopy, tree bark is less exposed to solar radiations. Moreover, due to its structure, it increases heat loss through conduction and convection (Henrion and Tributsch, 2009). By offering cooler habitats (Briscoe et al., 2014), tree bark may dampen the selective pressure resulting from the urban heat island (Miles et al., 2019). Alternatively, because springtails and oribatid mites, the most abundant taxonomic groups on tree bark, were not identified to the family level, it might be that this study underestimated the actual richness of arthropods on tree bark, which might have reduced the possibility to measure variations in richness between sites. Results on taxonomic composition showed that springtail abundance on tree bark decreased with increasing the urbanisation level, thus supporting this second hypothesis (see section 4.3).

Unexpectedly, whatever the season, arthropod abundance in the bush layer increased with increasing urbanisation level. In particular, the abundances of barklice, and of crab spiders (Thomisidae and Philodromidae) increased along the rural-urban gradient. This pattern may result from differences in the vegetation structure along the rural-urban gradient. Indeed, urban green areas are often composed of isolated trees and/or hedges of shrubs. Therefore, shrubs in urban areas may be more productive, produce more nutritive leaves and sustain a higher number of herbivores compared to the often light-limited understorey in areas with higher tree cover (Basset et al., 1992). Surprisingly, arthropod overall abundance in the canopy did not vary significantly with urbanisation level. This could be explained by the fact that the abundances of aphids, barklice and nematoceran flies (e.g., mosquitos, crane flies, etc.) increased, while the abundances of springtails and several groups of spiders decreased along the rural-urban gradient (see section 4.4), thus resulting in different communities but similar numbers of individuals in different habitats. This result strongly suggests that urbanisation negatively affects non-flying arthropods while flying insects do manage to colonize and thrive in urban areas. Habitat fragmentation in urban areas is certainly the main driver of such a filtering pattern, as species with low dispersal capacities (here without wings) can hardly colonize such habitats (Fenoglio et al., 2021). This pattern is expected to be exacerbated in the case of canopy-dwelling arthropods, as trees in urban areas are usually isolated from each other. In line with this hypothesis, arthropod overall abundance on tree bark decreased with increasing urbanisation level. Such a decrease in overall abundance was mainly triggered by a decline in the number of springtails and pin cushion millipedes. While the abundances of other groups with higher mobility like brachyceran flies (e.g., hover flies, house flies, dagger fliers, etc.) and leafhoppers increased on tree bark along the rural-urban gradient, this did not balance the overall loss of individuals.

The effects of urbanisation were taxon-specific, meaning that some taxonomic groups thrived while other suffered from urbanisation. Most importantly, 4 out of 10 families of spiders (tangle-web spiders, orb-weaver spiders, sheetweb spiders and ghost spiders) were consistently found in lower densities in the canopy and the bush layer of highly urbanised sites than of less urbanised ones. On the contrary, the abundance of crab spiders in the bush layer and on the bark consistently increased along the rural-urban gradient. Our results are in line with the results of previous studies that showed that urbanisation shifts spider taxonomic composition (Magura et al., 2010; Buchholz et al., 2018; Lowe et al., 2018; Lövei et al., 2019; Piano et al., 2020). However, while previous work suggest that urbanization increases the occurrence of species with higher dispersal capacity (species using ballooning), this pattern could not be measured in our study as sheet weavers, tangle-web spiders and orb-weaver spiders, the main families that were negatively affected by urbanisation in our study, are also the predominant families involved in ballooning (Blandenier, 2009; Simonneau et al., 2016). On the other hand, our study suggests that the filter explaining spider communities along the rural-urban gradient acts on habitat affinity and/or hunting mode. Indeed, the majority of “crab spider” (Thomicidae and Philodromidae) species prefer open-habitats (scrub, hedges, park and garden, wood pasture) (Bee et al., 2017), and are ambush predators or active hunters (Bee et al., 2017). On the contrary, the spider families that declined along the rural-urban gradient use webs to catch their prey. We could argue that web-building species have a lower hunting success in urban areas because of the lack of suitable support where attaching the webs, or because of the webs being damaged by green space management practices. However, previous studies showed that urbanisation do benefit some orb-weaver spiders (Lowe et al., 2014), who, for instance, take advantage of artificial light (Willmott et al., 2019). Identifying spiders to a lower taxonomic or functional level will certainly shed some light on the species traits that are filtered in the urban space and on the environmental characteristics acting as filters.

Overall, more urbanised sites had higher abundances of aphids (in the canopy), barklice (in the canopy and the bush layer), flies (nematoceran flies in the canopy and brachyceran flies on tree bark), and leafhoppers (on tree bark). Therefore, our study suggests that urbanisation favours herbivorous species but disfavours most spiders, meaning arthropod main predators (Nyffeler and Birkhofer, 2017). Thus, our results suggest that these communities are shaped by top-down controls, with urbanisation decreasing the abundance of several groups of spiders, resulting in the increase in the abundance of their prey. In the same way, the abundance of spiders and other predatory arthropods decreased along a rural-urban gradient in Budapest (Hungary), which was also associated with the outbreak of aphids on urban trees (Korányi et al., 2021). Similarly, aphid suppression increased with increasing the diversity of aphid natural enemies in a field experiment carried out along a rural-urban gradient in south central Wisconsin (USA) (Bennett and Gratton, 2012b).

The effects of urbanisation on arthropod abundance, richness and diversity were rather similar between months. However, we found that the proximity to settlements increased arthropod abundance in April, and, in a lower extent, in February. This was confirmed by the analysis on taxonomic group composition that showed that, in April only, arthropod communities were structured by the distance to the closest house. More specifically, in April, the abundances of springtails, tangle-web spiders and nematoceran flies increased in proximity to houses. Because houses are usually surrounded by evergreen hedging trees such as Thuja and early flowering shrubs such as Winter Jasmine (Jasminum nudiflorum) and Forsythias (Forsythia suspense), the effect measured may result from the earlier appearance of arthropods in residential areas than in less urbanised environments, because of the earlier availability of food resources (Luder et al., 2018; Fisogni et al., 2020). In addition, our results suggest that the negative effect of urbanisation on arthropod richness and diversity in the canopy and the bush layer is greater in June than in the other months, although such correlations were measured with few urbanisation indexes only and were not evidenced by the analysis on taxonomic group composition. Replicating the study would be useful to verify this result and formulate strong conclusions.

Contrary to the weak season-dependent effects of urbanisation on arthropod overall abundance, richness and diversity, the effects of urbanisation on the taxonomic composition of the communities strongly varied between months. Interestingly, in August and October, even though our results do not show that urban sites support higher numbers of arthropods overall, there were more taxonomic groups that benefited from urbanisation than of groups that declined along the rural-urban gradient. This result is in line with previous studies that demonstrated that, in more urban areas, because of warmer temperatures and/or higher food availability, adult survival of some arthropod species is extended even after the end of the summer (Lowe et al., 2016). Importantly, except for a particularly rainy December 2020, weather conditions during the time of this study were normal (World Weather Online, 2022), meaning that the results obtained in this study should accurately represent the main differences in arthropod communities along the rural-urban gradient. However, we cannot exclude that the link between arthropod communities and the urbanisation level may be different during years with unusual weather conditions.

While the variation in arthropod abundance, richness and diversity explained by the model only slightly differed between models using different urbanisation level proxies, models using the percentage of impervious surface cover were systematically performing better (i.e., they were systematically in the top three of the models with the highest adjusted R²). Moreover, the percentage of impervious surface in a 500 m radius explained the highest proportion of the variation in arthropod taxonomic composition between sites. In addition, the percentage of impervious surface cover was selected in seven out of the nine ordination analyses run per month and per micro-habitat. Interestingly, arthropod abundance was better explained by local urbanisation level (i.e., the distance to the closest house and the “distance to nature” and percentage of impervious surface cover in a 100 m radius around the sampling point). This result is in line with a previous study showing that the local urbanisation level (within a 200 m × 200 m cell) better explains arthropod abundance than the urbanisation level measured at the scale of the landscape (within a 3 km × 3 km cell) (Piano et al., 2020). On the contrary, arthropod richness and diversity were better explained by the urbanisation level at a larger scale (i.e., the percentage of impervious surface cover or vegetation cover in a 1,000 m or 500 m radius around the sampling point). To explain these results, we suggest that richness and diversity reflects mostly the number of groups that managed to colonize the area, which depends on the fragmentation of the landscape (Buchholz et al., 2018; Fattorini et al., 2018). On the contrary, abundance reflects primarily population success in terms of reproduction and survival, which depends on the quality of the local habitat (Lowe et al., 2014). All in all, our study supports the importance of measuring urbanisation level at different spatial scale in order to uncover the effects of urbanisation on arthropod communities (Egerer et al., 2017; Piano et al., 2020). However, while some studies that did consider multiple spatial scales classified the sampling points into categories of urbanisation level (e.g., low, intermediate, high), we advise measuring continuous level of urbanisation; indeed, measuring categorical variables reduces the statistical power, increases the risk of false positive results and prevents from testing non-linear relationships (Altman and Royston, 2006). As for the environmental variable to measure, we advise using the percentage of impervious surface, which in general best explained changes in arthropod community composition in our study, but also in previous studies (Bennett and Gratton, 2012a; Lagucki et al., 2017; Maher et al., 2022).