DOI: 10.5061/dryad.qv9s4mwpf
title: Arthropod traits as proxies for abundance trends in the Azorean Islands

description: Human activities drive ecological transformation, impacting island
 ecosystems from species diversity to ecological traits, mainly through
 habitat degradation and invasive species. Using two unique long-term
 datasets we aim to evaluate whether species traits (body size, trophic
 level, dispersal capacity, and habitat occupancy) can predict temporal
 variations in the abundance of endemic, Indigenous (endemic and native
 non-endemic), and exotic arthropods in the Azores Islands. We found that
 body size is crucial to predict arthropod abundance trends. Small-bodied
 herbivorous arthropods showed a decrease in abundance, while large-bodied
 indigenous arthropods increased in abundance, mainly in well-preserved
 areas. Also, large-bodied exotic arthropods increased in abundance across
 the entire archipelago. Moreover, endemic canopy dwellers increased in
 abundance, while endemic ground dwellers decreased in abundance.
 Simultaneously, exotic arthropods showed the opposite result, increasing
 abundance in the ground while decreasing abundance in the canopy. Finally,
 habitat influenced both endemic and exotic spider abundance trends.
 Endemic spiders that occupy solely natural habitats experienced a decline
 in abundance, while exotic spiders in the same habitats increased in
 abundance. Our study underscores the significance of arthropod species
 traits in predicting abundance changes in island ecosystems over time, as
 well as the importance of monitoring species communities. Conservation
 efforts must extend beyond endangered species to protect non-threatened
 ones, given the increased extinction risk faced by even common species on
 islands. Monitoring and restoration programs are essential for preserving
 island ecosystems and safeguarding endemic arthropod populations.
description: We carried out this study in the Azores Archipelago, which
 encompasses nine volcanic islands and several islets and seamounts. The
 archipelago is located in the northern Atlantic Ocean, approximately
 between 37° and 40° N latitude and 24° and 31° W longitude, spread along a
 distance of approximately 615 km from East to West. The archipelago is
 divided into three distinct island groups: western (Corvo and Flores),
 central (Faial, Pico, São Jorge, Graciosa, and Terceira), and eastern (São
 Miguel and Santa Maria). On all islands, the climate is characterized as
 temperate oceanic with high levels of relative atmospheric humidity, which
 can reach 95% in the elevated native semi-tropical evergreen laurel forest
 (Elias et al. 2016, Borges et al. 2022a, b, c). Human presence on these
 islands, dating back to the 15th century, has significantly altered the
 landscape (Norder et al. 2020). The once-pristine native forests have
 given way to exotic tree plantations, agricultural and pastoral fields,
 and urban settlements. Consequently, the original native forest currently
 occupies a mere 5% of the archipelago's total land area, primarily
 confined to the remote and elevated regions of difficult access (Gaspar et
 al. 2008, Triantis et al. 2010, Elias et al. 2016, Borges et al.
 2022d). We sampled arthropods following the
 standardized sampling protocols of two long-term monitoring projects in
 the Azores: (i) the ‘Biodiversity of Arthropods from the Laurisilva of the
 Azores’ (BALA), from 1997 to 2022 (Borges et al. 2006, 2016); and (ii) the
 ‘Long Term Ecological Study of the Impacts of Climate Change in the
 Natural Forest of Azores’ (SLAM), from 2012 to 2022 (SLAM), from 2012 to
 2022 (Borges et al. 2020, 2022d, Costa and Borges 2021, Lhoumeau and
 Borges 2023). We conducted the BALA project in three
 distinct campaigns, between 1997 and 2004 (BALA I), 2010 and 2011 (BALA
 II) and 2019 and 2022 (BALA III). We collected arthropods from native
 forest fragments on seven islands. Corvo and Graciosa were not sampled due
 to the lack of native forest patches. Two sampling methods were used:
 pitfall traps to capture ground-dwelling arthropods and canopy beating to
 capture canopy-dwelling arthropods (maximum height of 3-4 m). All the
 samplings occurred in boreal summer, between June and September. More
 details of the methods can be found elsewhere (Ribeiro et al. 2005, Borges
 et al. 2006, 2016, Gaspar et al. 2008) and data is now openly available
 (Pozsgai et al. 2024). We collected the SLAM project
 data once in each boreal season (spanning three months), continuously
 between 2012 and 2022. Sampling was conducted in a total of 10 plots,
 located only on Terceira island. Plots were set in areas that are among
 the best-preserved native forests fragment, with minimal human
 disturbance. Arthropods were sampled using passive flight interception
 SLAM traps (Sea, Land and Air Malaise trap) at each plot. Each trap had
 dimensions of 110 cm x 110 cm x 110 cm and contained propylene glycol
 (pure 1,2-propanediol) to capture, kill and conserve the specimens (Borges
 et al. 2020, 2022d, Costa and Borges 2021, Lhoumeau and Borges 2023). More
 details of the methods can be found elsewhere (Borges et al. 2020, 2022d,
 Costa and Borges 2021, Lhoumeau and Borges 2023). Most
 of the collected arthropods were sorted to species level, and nomenclature
 follows the most recent checklist of Azorean arthropods (Borges et al.,
 2022a). The remaining specimens that were not identified to species level
 were sorted to morphospecies and were excluded from the analyses. Also,
 all Crustacea, Collembola, Diplura, Diptera and Hymenoptera (except
 Formicidae) species were not considered in this study. Nominated species
 were then classified according to their biogeographic origin, into the
 following three categories (Borges et al. 2022a): i) endemic species, i.e.
 those exclusive to the Azores; ii) indigenous species, i.e. those that are
 a combination of endemic and native non-endemic Azorean species, which can
 also be found in neighboring archipelagos (Madeira and the Canary Islands)
 and/or in the Mediterranean basin, and most likely reached the Azores
 through long-distance dispersal; and iii) exotic species, i.e. those
 believed to have reached the Azores by human actions (Borges et al., 2006,
 2022a; Borges and Wunderlich, 2008). For all sampled
 species, we selected a total of six functional traits for which we had
 <em>a priori</em> expectations about how they may respond to
 increasing habitat disturbance (e.g. body size, dispersal ability), traits
 that are important in species interaction (e.g. trophic status,
 specialization) and traits that are common to all arthropod groups
 encountered in our study (Gossner et al. 2015, Simons et al. 2016, Rigal
 et al. 2018, Chichorro et al. 2022a) (Fig. 1). The six traits were: (i)
 body size, that was standardized by the z-score within each order, i.e.,
 average size of the order minus the size of the species divided by the
 standard deviation of size in the order, (ii) vertical stratum that the
 species occupy in the vegetation (henceforth verticality), expressed as
 average verticality (AVS), from 0 to 1 where 0 is exclusive ground
 dwelling species and 1 is the exclusive canopy dwelling species, (iii)
 feeding trophic group, expressed in four categories (carnivorous,
 fungivorous, herbivorous and omnivorous); dispersal ability within and
 between islands, classified into (iv) four categories (ballooning,
 crawling, flying and phoretic) and into (v) low (crawl, phoretic) or high
 (ballooning and flying) dispersal capacities; and (vi) habitat occurrence
 (where species were present) as categorical variables (presence in natural
 habitat, in anthropogenic habitat or in both natural and anthropogenic
 habitat) (see more details in Table 1). In the case of the verticality, we
 followed the framework described in Costa et al. (2023). Using the BALA
 databases (I to III), we attributed a verticality score to the sample
 types, with 0 assigned to pitfalls, 0.5 to beating samples from bushes,
 and 1 to trees. The relative abundance for each species across each
 stratum in the forest fragments was obtained dividing its abundance by the
 total number of spiders sampled in that stratum and site was standardized
 for variations in the sampling method. Then, we normalized the values by
 dividing each species' stratum relative abundance by the sum of its
 values along all strata within each forest fragment. All these were
 calculated using the equations described for average verticality in Costa
 et al. (2023). When we analyzed spiders separately, only three traits were
 used: (i) body size, (ii) average verticality (AVS) and (iii) habitat
 occurrence. Due to the homogeneity of spiders’ feeding trophic level (all
 carnivores) and dispersal ability (only one species was a crawling spider,
 all others were considered possible ballooners), we removed these traits
 from the analysis. Moreover, habitat occurrence for spiders only had two
 levels and were analyzed as a binary variable (inhabiting only natural
 habitats or both natural and anthropogenic habitats). Apart from body
 size, measured on the individuals sampled in these studies, data for the
 functional traits were compiled from: (i) the Azorean Biodiversity Portal
 (ABP 2024) or IUCN SSC Atlantic Islands Invertebrate Group Portal (ABP
 2024, AIISG 2024); (iii) published data, papers and descriptions of
 particular species; and (iv) personal knowledge on the species’ natural
 history by PAVB. We partitioned the data for the
 analyses into six different datasets, of which three were the Scenario
 Analysis and three were the Sensitivity Analysis (Supplementary Material).
 For the Scenario Analysis, we used the complete BALA dataset (hereafter
 BALA full) and the SLAM dataset. Since the SLAM data is limited to
 Terceira island, we also used the BALA data from Terceira only (hereafter
 BALA Terceira) to compare the results obtained from the two sampling
 strategies (Fig. 1 and Supplementary Table 1). For the Sensitivity
 Analysis, we used the BALA dataset excluding Pico island (hereafter BALA
 without Pico), the BALA dataset excluding Terceira island (hereafter BALA
 without Terceira), and the BALA dataset excluding Pico and Terceira
 islands (hereafter BALA without Pico and Terceira). Since the native
 forest is not equally preserved across all Azores islands, we tested the
 removal of Pico and Terceira islands, which were the most preserved native
 forests throughout the archipelago (Tsafack et al. 2023) (Supplementary
 Table 1). Based on the time length of our datasets (25
 years for BALA and 10 years for SLAM) and to improve the reliability of
 trends and traits analyses, we excluded species with less than 10 adult
 individuals in each of BALA full or SLAM datasets. In this way, we
 mitigate the potential data noise caused by rare species (Martínez-Núñez
 et al. 2024), which we considered as those with one or less individuals
 per year in ten years or more. Then, we compiled the abundance of each
 species per: (i) BALA sampling (BALA I, II and III) and island sites (30),
 for all BALA datasets and (ii) SLAM years (10 years) and sites (10) for
 the SLAM dataset (Supplementary Table 1). In this way, we were able to
 obtain a abundance trend (slope) of each species in each dataset (see
 below). Each dataset was further separated into four groups of species:
 all arthropods, only herbivorous arthropods, only spiders and only
 beetles, the last two being the most diverse groups of the sampled
 arthropods. Also, the same groups were studied according to their
 biogeographic origins: all species together, only endemic species, only
 indigenous species and only exotic species (Supplementary Table 1).
 Therefore, we had a total of 16 separate analyses for each dataset (four
 arthropod groups by four biogeographic origins; 64 Bayesian frameworks,
 see below). To obtain the abundance trend of each
 species in each dataset (slope values, β), we fitted null (intercept only)
 GLMM models with Negative Binomial distribution and random effect
 (Goldstein and de Valpine 2022). The response variables were each species
 abundance (one species per model) and the random effects were specific to
 each dataset: for BALA full, BALA without Pico, BALA without Terceira, and
 BALA without Pico and Terceira, islands were the random variable (seven
 islands); and for BALA Terceira and SLAM, sites were the random variables
 (10 sites). Hence, the intercept of the response variable with a positive
 estimate indicated a positive abundance trend over time, while a negative
 estimate value indicated a negative abundance trend over time. GLMM models
 were performed and <em>Conditional</em> R-squared (delta
 method, to estimate standard errors of transformations of a random
 variable, a first-order Taylor approximation (Parr 1983)) were obtained
 through the MuMIn package (Bartoń 2023) in the R environment (R Team
 2022).<em> </em> We fitted a Bayesian
 framework, weighted by <em>Conditional</em> R-squared values
 obtained for each species, to assess how changes in species abundance
 trends (response variable) could be predicted by species traits
 (explanatory variables) (Fig. 1). Distinct random effects were chosen to
 fit each framework for each dataset, being either the arthropod order
 and/or the species origin (Supplementary Table 1). For instance, when we
 analyzed all arthropods, both order (each arthropod order) and origin
 (endemic, native non-endemic or exotic) were used as random effects. At
 the same time, when indigenous arthropods were analyzed, order was used as
 previously (each arthropod order) but origin was only endemic or native
 non-endemic, as random effects (Supplementary Table 1). All Bayesian
 frameworks were built with the help of the <em>jagsUI
 </em>package (Kellner and Meredith 2021) in R environment (R Team
 2022), with five simultaneous chains, default priors, one million
 interactions, 800.000 burn-ins and 10 thins. Variables were considered to
 be significant when 97.5% of the credible intervals (CRI) were either
 positive or negative, not passing through zero (Hespanhol et al.
 2019).
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description: # Azorean arthropod abundance trends and species traits and Bayesian model
 script
 [https://doi.org/10.5061/dryad.qv9s4mwpf](https://doi.org/10.5061/dryad.qv9s4mwpf) ## Description of the data and file structure The data used for the Bayesian analysis of Azorean arthropod species and their traits. The species data were derived from samplings on the Azorean Islands for the past 25 years. However, this compilation and methods used to evaluate species is novel. Each line is an independent species. * Order: Indicate the arthropod order. * Morpho: Indicate the species or morpho-species number. * Origin: Indicate if the species is endemic, native non-endemic, or exotic in Azores. * Body size: Indicate the body size of the species. Values were standardized by each arthropod order. * Diet: Indicates what is the species' trophic level, indicating if it is a carnivore, herbivore, or omnivore. * Dispersal ability: Indicate how is the main source of dispersal for this species, if it flies, balloons, craws, or if is phoretic. * Dispersal ability (binary): Also indicates the dispersal ability, but divided as low dispersal (craw or phoretic) and high dispersal (fly, balloon). * Habitat presence: Indicates whether the species occupy anthropogenic or natural habitats.  * Verticality AVG: Indicate where the species occupy the vertical stratum of the forest, the canopy, or the ground. * SLAM Slope: Indicate the slopes obtained for species abundance trends with the SLAM data. * SLAM Slope Delta R2: Indicate the R2 obtained for species abundance trends with the SLAM data. * BALA Full Slope: Indicate the slopes obtained for species abundance trends with the BALA full data. * BALA Full Slope Delta R2: Indicate the R2 obtained for species abundance trends with the BALA full data. * BALA NoPic Slope: Indicate the slopes obtained for species abundance trends with the BALA dataset without the Pico Island data. * BALA NoPic Slope Delta R2: Indicate the R2 obtained for species abundance trends with the BALA dataset without the Pico Island data. * BALA NoTer Slope: Indicate the slopes obtained for species abundance trends with the BALA dataset without the Terceira Island data. * BALA NoTer Slope Delta R2: Indicate the R2 obtained for species abundance trends with the BALA dataset without the Terceira Island data. * BALA NoPTe Slope: Indicate the slopes obtained for species abundance trends with the BALA dataset without the Pico and Terceira Islands data. * BALA NoPTe Slope Delta R2: Indicate the R2 obtained for species abundance trends with the BALA dataset without the Pico and Terceira Islands data. * BALA OnlTer Slope: Indicate the slopes obtained for species abundance trends with the BALA dataset with only Terceira Island data. * BALA OnlTer Slope Delta R2: Indicate the R2 obtained for species abundance trends with the BALA dataset with only Terceira Island data. Missing data codes: NA = not available or not applicable. The supplementary material provided here is the results mainly from the analysis made with the BALA dataset without the Pico Island data; BALA dataset without the Terceira Island data; BALA dataset without the Pico and Terceira Islands data; and BALA dataset with only Terceira Island data. ## Code/Software The basic scripts for the Bayesian analyses were uploaded along with the data. A very similar script was used for all the different analyses made, only changing the objects or columns derived from each dataset for arthropod groups.