ARTICLE INFO

ABSTRACT

Contents lists available at ScienceDirect
Global Ecology and Conservation
journal homepage: www.elsevier.com/locate/gecco
Taxonomic, functional, and phylogenetic diversity of aerial insectivorous bats decay on forest islands created by a mega Amazonian dam
Paulo Estefano D. Bobrowieca,b,1,*, Raffaello Di Ponzio a,c,2, Guthieri Teixeira Colombo a,3, Carlos A. Peres a,d,e,4, Maíra Benchimol f,5
a Programa de P´os-graduaç˜ao em Ecologia, Instituto Nacional de Pesquisas da Amazˆonia, Manaus 69080-971, Brazil
b Instituto Tecnol´ogico Vale, Bel´em 66055-090, Brazil
c Programa de P´os-graduaç˜ao em Ecologia, Conservaç˜ao e Manejo da Vida Silvestre, Instituto de Ciˆencias Biol´ogicas, Universidade Federal de Minas Gerais, Belo Horizonte 31270-910, Brazil
d Centre for Ecology, Evolution and Conservation, School of Environmental Sciences, University of East Anglia, Norwich NR47TJ, UK
e Instituto Juru´a, Manaus 69083-300, Brazil
f Programa de P´os-graduaç˜ao em Ecologia e Conservaç˜ao da Biodiversidade, Laborat´orio de Ecologia Aplicada `a Conservaça˜o, Universidade Estadual de Santa Cruz, Ilh´eus 45662-900, Brazil

Keywords:
Central Amazon
Conservation strategies Diversity dimensions
Habitat fragmentation Hydropower projects Land-bridge systems

Hydroelectric dams have proliferated across the tropics, leading to extensive landscape changes driven by habitat loss and fragmentation of lowland forests. Assessing their effects on biodiversity and designing effective conservation strategies require a comprehensive understanding of both the local habitat context and landscape-scale perspective. We investigated the influence of local, patch and landscape-scale variables on the taxonomic, functional, and phylogenetic diversity of aerial insectivorous bats in a 30-year-old archipelagic forest landscape in Brazilian Amazonia. Bats were surveyed using passive recorders across 28 forest islands and six adjacent continuous forest sites. Taxonomic, functional, and phylogenetic diversity facets were calculated within a Hill numbers approach that considers the importance of rare, common, and dominant species. We analyzed the response of bat diversity to both local and landscape changes, using vegetation structure, patch, and landscape variables as predictors. Taxonomic, functional, and phylogenetic diversities were reduced on forest islands. All facets of diversity for species richness (q = 0) and common species (q = 1) were positively influenced by local and patch-scale characteristics, including tree species diversity and forest cover, but negatively affected by edge area around the islands. Taxonomic diversity was found to be a good indicator of phylogenetic diversity. Undisturbed continuous forests and islands that support species-rich tree floras and lower edge habitat density, harboured greater numbers of aerial insectivorous bats, as well as phylogenetically diverse assemblages exhibiting broader ecological functions. Bat diversity was most influenced by
* Corresponding author at: Programa de Po´s-graduaça˜o em Ecologia, Instituto Nacional de Pesquisas da Amazˆonia, Manaus 69080-971, Brazil.
E-mail address: paulobobro@gmail.com (P.E.D. Bobrowiec).
1 0000-0002-8945-6105
2 0000-0002-7527-3594
3 0000-0001-9321-6772
4 0000-0002-1588-8765
5 0000-0002-1588-8765
https://doi.org/10.1016/j.gecco.2025.e03488
Received 17 July 2024; Received in revised form 7 January 2025; Accepted 9 February 2025
Available online 11 February 2025
2351-9894/© 2025 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
habitat quality, highlighting the need to establish protected forest areas that include large islands (>100 ha) around land bridge island systems.
1.		Introduction
It is widely recognized that the main threat to biodiversity globally is the extensive conversion of natural ecosystems into human- modified landscapes, especially in developing tropical countries (Newbold et al., 2014). This shift in land cover results in habitat loss, fragmentation, and environmental degradation, with consequent loss of species, ecosystem services, and biological lineages (Laurance et al., 2018). Although hailed as a sustainable energy source, hydroelectric infrastructure has multifaceted effects on riverine habitats (Finer and Jenkins, 2012; Michalski and Norris, 2023). Vast areas of intact vegetation are often submerged under large reservoirs, with upland forests transformed into isolated islands surrounded by an open-water matrix (Benchimol and Peres, 2015a). As a result, hydroelectric archipelagos subdivide, isolate, and disrupt terrestrial wildlife populations and disrupts natural movement patterns (Benchimol and Peres, 2015b). This disturbance triggers a cascade of ecological consequences, such as reduced biodiversity, changes in species composition (Di Ponzio et al., 2023), hindrance of gene flow (Meyer et al., 2008; Santo-Silva et al., 2021), and disruptions in ecological processes (Emer et al., 2013). In forest fragments adjacent to a terrestrial matrix, surrounding second-growth provides resources such as shelter, food, and protection from predators for several species (Prevedello and Vieira, 2010) and increases connectivity between forest patches, thereby facilitating dispersal across the landscape and contributing to greater species diversity within forest patches (Benchimol and Peres, 2015b; Lees and Peres, 2009). On the other hand, in human-made aquatic systems the pervasive negative fragmentation effects are enhanced due to the low matrix quality surrounding forest islands (Mendenhall et al., 2014), potentially culminating in greater biodiversity loss (Palmeirim et al., 2024).
In high-contrast systems such as archipelagic anthropogenic forest landscapes, the matrix created by vast water-bodies is inhospitable to virtually all forest species. Furthermore, this environment remains unaltered over time, representing a long-term challenge to species connectivity among patches within the landscape (Meyer et al., 2008). The open aquatic matrix also exacerbates edge effects, changing the physical, chemical, and biological characteristics of insular forests by raising temperatures and reducing understorey humidity (Arroyo-Rodríguez et al., 2017). These local environmental changes subsequently modify forest structure and tree species composition, especially on small islands (< 10 ha), where increased tree mortality leads to more simplified vegetation (Benchimol and Peres, 2015a). Species-poor tree assemblages on small islands homogenize resources and niches available to wildlife, resulting in greater rates of biodiversity loss compared to large islands (> 100 ha) and continuous forest (Farneda et al., 2018; Benchimol and Peres, 2021). At the landscape scale, forest cover and isolation are the main determinants of species persistence in fragments (Rabelo et al., 2017; Palmeirim et al., 2022). Large islands surrounded by extensive forest areas tend to be less impacted by isolation, supporting higher diversity and exhibiting species composition profiles analogous to undisturbed continuous forests (Jones et al., 2021; Di Ponzio et al., 2023). Therefore, disentangling the effects of local, patch and landscape variables on patterns of species diversity on islands artificially created by major hydropower infrastructure is crucial to propose sound conservation strategies focused on existing and future dams.
Assessing the effects of human-driven landscape change on biodiversity requires a more comprehensive understanding that extends beyond traditional measures of biodiversity (Ricotta et al., 2016). Taxonomic diversity is an essential metric to evaluate the impact of landscape changes on overall species richness, abundance, and composition. However, relying solely on taxonomic diversity may overlook critical aspects of assemblage functionality, which provide a more comprehensive understanding of human impacts on biodiversity (Cisneros et al., 2015; Carvalho et al., 2023). Functional diversity, which considers the range of ecological roles and traits exhibited by species, provides insights into the resilience and stability of ecosystems (Petchey et al., 2007; Cadotte et al., 2013). Human-induced landscape alterations may lead to the dominance or decline of specific functional groups, negatively influencing ecosystem processes and services (Kuebbing et al., 2018; Weiss and Ray, 2019; Bogoni et al., 2020). Moving beyond functional diversity, phylogenetic diversity considers the evolutionary relationships among species, providing a deeper perspective on the historical trajectories of biodiversity (Webb et al., 2002). This metric helps discern whether human-driven changes disproportionately affect specific evolutionary lineages (Srivastava et al., 2012; Farneda et al., 2022). Therefore, integrating taxonomic, functional, and phylogenetic diversity provides a more nuanced understanding of how human activities shape biodiversity, thereby assisting the development of conservation strategies and sustainable landscape management efforts (Ramos Pereira et al., 2018; Lo´pez-Baucells et al., 2022).
Bats stand out among the myriad tropical mammalian taxa for their remarkable taxonomic, functional, and phylogenetic diversity (Wilson and Mittermeier, 2019), which makes them a good model group for biodiversity assessments. Despite their relatively recent divergence, bats have undergone rapid adaptive radiation, resulting in a wide variety of species with distinct adaptations (Garbino and Tavares, 2018). This diversification has allowed them to occupy diverse ecological niches with broad morphological diversity, a variety of feeding habits, foraging strategies, habitat uses and shelters (Rossoni et al., 2019). As voracious insect predators, aerial insectivorous bats play a vital role in invaluable ecosystem services, such as suppressing agricultural pests and disease vectors for humans (Aguiar et al., 2021; Tuneu-Corral et al., 2023). However, investigations of the responses of bat assemblages to fragmentation in tropical artificial reservoir islands have neglected aerial insectivorous bats (but see Estrada-Villegas et al., 2010; Di Ponzio et al., 2023; Hazard et al., 2023). Furthermore, most studies that used bats as models to assess dam impacts on biodiversity have focused only on species composition changes and bat activity (but see Farneda et al., 2020a). To date, only two studies assessed the response of functional diversity and filtering of functional traits in aerial insectivorous bats in tropical reservoir archipelagos (Colombo et al., 2023; Hazard et al., 2023). These studies showed that small, degraded islands and landscapes dominated by forest edges exhibited a loss of species, activity, and functional diversity. However, the degree to which multiple dimensions of aerial insectivorous bat diversity are simultaneously affected by landscape and patch attributes and local vegetation quality in human-made archipelagic environments has not yet been investigated (Lo´pez-Baucells et al., 2022).
Here, we investigated the influence of local, patch and landscape-scale variables on the taxonomic, functional, and phylogenetic diversity of aerial insectivorous bats in a 30-year-old forest archipelago created following the construction of a major dam in central Brazilian Amazonia. Using aerial insectivorous bats as models, we used multiple diversity dimensions of Hill numbers, an approach that considers the importance of rare, common, and dominant species, to assess the effects of island habitat fragmentation on biodiversity. Specifically, we examined the: (a) response of different dimensions of bat diversity (taxonomic, functional and phylogenetic) to landscape insularization, comparing continuous forest sites with forested islands; (b) importance of the local, patch and landscape-scale variables of both insular and continuous forests as determinants of bat diversity in multiple dimensions; and (c) intercorrelation between diversity dimensions to determine if taxonomic diversity alone is sufficient to predict changes in functional and phylogenetic diversity. As continuous forests and large islands tend to shelter more bat species, greater functional diversity (Colombo et al., 2023) and a more uniform distribution of bat assemblages than small islands (Di Ponzio et al., 2023), we predict that the taxonomic, functional, and phylogenetic diversity of insectivorous bats will be highest in continuous undisturbed forest sites. We also anticipate that high-quality vegetation features, both at the local and landscape scales (highly forested landscapes retaining high tree species richness), will support assemblages with greater taxonomic, functional, and phylogenetic diversity. As species and functional richness and activity were important metrics in structuring bat assemblages in the study area (Colombo et al., 2023; Di Ponzio et al., 2023), our previous predictions will be most evident when considering both richness and common species. We also predict that all dimensions of diversity will be positively correlated, as supported by studies on other vertebrate taxa (Palmeirim et al., 2021).

Fig. 1. Spatial distribution of 28 forest islands (orange polygons) and six continuous forest sites (large red dots) surveyed within the Balbina Hydroelectric Dam landscape in central Brazilian Amazonia. Inset map (bottom left) shows the location of the Balbina Dam. The aerial photograph in the upper right corner shows a group of small islands (Photo credit: E. M. Venticinque). Inset figure (bottom right) illustrates a passive recorder at the island centroid, surrounded by radial buffers at 500 m, 1000 m, 1500 m, and 2000 m distances.
2.		Methods
2.1.		Study area
This study was conducted in a set of forest islands and adjacent mainland continuous forests within the Balbina Hydroelectric dam landscape (Fig. 1; 1◦01’–1◦55’ S and 60◦29’–59◦28’ W), located 135 km northeast of Manaus, State of Amazonas, Central Amazonia, Brazil. The Balbina Dam was installed in the Uatum˜a River, an Amazon River tributary, and began operating in 1986. The floodwaters inundated ~2996 km2 of dense submontane rainforest and created 3525 forest islands ranging from 0.2 to 4878 ha (Benchimol and Peres, 2015a; Fig. 1). Annual average precipitation is 2420 mm (2113 – 2716 mm) and average temperature is 26.7 ºC (Benchimol and Peres 2015a). The dry season occurs from June to October and the wet season from November to July. The strictly protected Uatum˜a Biological Reserve (REBIO Uatum˜a) was created in 1990 to mitigate forest loss and other drastic environmental impacts induced by the river damming. This forest reserve comprises nearly 1 Mha, including several forest islands and a vast continuous forest on the left bank of the reservoir (Benchimol and Peres, 2015a).
We selected 28 forest islands and six continuous forest sites (CF) adjacent to the reservoir that had been previously sampled for terrestrial vertebrates and trees (Benchimol and Peres, 2015b). Islands were selected based on their size (0.83 – 1690 ha, mean ± SD: 245.9 ± 426.1 ha) and distance to the nearest continuous forest (44 – 13,217 m; 4676.9±3817.2 m), encompassing more than 200, 000 ha of the study area. The minimum and maximum distance between surveyed islands was 1 km and 67.8 km, respectively (26.3 ± 15.0 km; Fig. 1).
On each island and CF site, plots of 0.25 ha (250 m × 10 m for islands >10 ha and continuous forest sites) and 0.125 ha (125 m × 20 m for islands <10 ha) were established (see Benchimol and Peres, 2015a) to quantify local vegetation variables. Given that the number of forest plots per island ranged from one to four, we selected one plot to deploy one autonomous recorder, in which the nearest to the island centroid was chosen, to minimize confounding forest edge effects.
2.2.		Bat surveys
We sampled aerial insectivorous bats on each sampling site using autonomous recording stations (Song Meter 2 BAT+) coupled with one omnidirectional SMX-US microphone (Wildlife Acoustics Inc., Massachusetts, USA), between July and October 2016. One recorder was positioned 1.5 m above ground at the centre of each plot and programmed to record bat calls from 17:30–06:30 h, during four consecutive nights, except for 3 islands sampled for only 3 nights due to heavy rains. Thus, each plot was sampled for 39 or 52 h, totaling 1729 recorder-hours over 133 sampling nights. Recorders were programmed in real-time with a full spectrum resolution of 32- bit, and the sample rate was set at 384 kHz. Only sounds exceeding 18 dB from ambient noise were recorded. We considered bat-passes as the unit of measure for bat activity. A bat pass represented at least two search-phase pulses characteristic of a species or phonic complex (species with similar calls that cannot be confidently distinguished) in a 5-sec file (Appel et al., 2017; Di Ponzio et al., 2023). Feeding buzzes, distress and social calls were not included in this study. Bat activity at each site was represented by the sum of bat activity divided by the number of sampling nights.
We identified bat species by their species-specific calls or phonic complexes (sonotypes) using Neotropical bat acoustic literature (L´opez-Baucells et al., 2016; Arias-Aguilar et al., 2018) and by comparing sonograms with bat acoustics libraries from the Adolpho Ducke Forest Reserve and Biological Dynamics of Forest Fragments Project, located 120 and 60 km from the Balbina dam, respectively. The bat species composition of these sites is expected to be the same as at Balbina. Calls of Phyllostomidae and Molossidae families were excluded from analysis due to the low reliability in species identification. Recordings were manually analyzed using the Kaleidoscope 4.0.4 software (Wildlife Acoustics Inc., Massachusetts, USA).
2.3.		Taxonomic, functional, and phylogenetic metrics
We estimated taxonomic, functional, and phylogenetic diversity of each site using the Hill numbers framework (Chao et al., 2014) in the hillR package (Li, 2018). Hill numbers, or effective number of species, is a unified measure of diversity to estimate and compare taxonomic entities (here, species), phylogenetic entities (branches of unit length), and functional entities (pairs of species with unit distance between species), under a single framework (Chao et al., 2014). These estimates are sensitive to both the richness (the number of different entities) and evenness (the distribution of abundance among those entities, based herein on bat activity) of community components (Chao et al., 2014). We used the argument “q” to control the weight of species abundance, in which diversity values become more sensitive to species abundance as q order increases. We considered diversity estimates of order q = 0, q = 1 and q = 2; for q = 0, species/sonotype activity is ignored and a similar weight is given to rare and common species (i.e., species richness); for q = 1, species/sonotypes are weighted according to its activity in the assemblage (i.e., exponential of Shannon entropy); for q = 2, greater weight is given to more common than rarer species/sonotypes, thus favouring “dominant” species/sonotypes in the assemblage (i.e., inverse of the Simpson index). To estimate functional diversity (D_q value from ‘hill_func’ function), we selected six bat traits that have shown potential sensitivity to habitat fragmentation in aerial insectivorous bats across tropical forests (Nún˜ez et al., 2019; Moir et al., 2021; Colombo et al., 2023): two ultrasound characteristics (frequency of maximum energy and call structure), three body measurements (body mass, relative wing loading, and aspect ratio), and vertical stratification of flight activity as a measure of forest habitat use (Table S1). Wing data was not available for Saccopteryx canescens, Pteronotus personatus, Peropteryx kappleri, P. trinitatis, and Furipterus horrens, so we estimated the values using the linear regression equations for relative wing loading and aspect ratio as a function of body mass as proposed by Colombo et al. (2023). A description of species traits is presented in Table S1 from Colombo et al. (2023)	. To estimate phylogenetic diversity, we used the species-level phylogeny of bats proposed by Shi and Rabosky (2015), which was pruned to include only aerial insectivorous bat species that occur at Balbina. Pteronotus alitonus was not present in the phylogeny and was substituted by its closest congener P. parnellli; the Eptesicus brasiliensis/chiriquinus phonic complex was attributed as E. brasiliensis, the most basal species of the phonic complex in the phylogeny. This substitution procedure by close congeners does not result in meaningful changes in the phylogenetic distance matrix of each assemblage.
2.4.		Environmental variables
On each plot, we measured the following local vegetation variables: (i) the number of adult tree species (tree richness) with diameter at breast height (DBH) ≥ 10 cm; (ii) the number of tree stems (tree abundance) with DBH ≥ 10 cm; (iii) forest basal area (FBA) of the trees bearing fleshy fruits; (iv) fire severity; and (v) vegetation clutter (see Colombo et al., 2023 for further details) (Table S2). At the patch-scale, we measured: (vi) forest area (in hectares; for continuous forests, we assigned a value 10-fold larger than our largest island of ~1690 ha as proposed by Colombo et al., 2023); (vii) island isolation (shortest distance between the surveyed island and nearby continuous forest in meters; for the continuous forest, a value equal to 0 was assigned); and (viii) the shortest distance between the ultrasound recorder and the forest edge (in meters) (Table S2). At the landscape-scale, we measured: (ix) landscape-scale
Table 1
Best-supported models from the 95 % confidence set (ΔAICc ≤ 2.0) of Generalized Linear Models (GLMs) assessing the relationship between taxonomic, functional, and phylogenetic diversity and seven local, patch and landscape scale predictors of the Balbina Hydroelectric Dam, Central Amazon, Brazil.
Response variables	Model distribution family	Predictor variables	AICc	ΔAICc	wi	
Taxonomic diversity						
q =0	Poisson	Edge area (1000 m) + Tree richness	138.9	0.00	0.14	
		Edge area (1000 m) + Forest area	139.5	0.53	0.10	
		Edge area (1000 m)	139.8	0.86	0.09	
q =1	Gaussian	Edge area (2000 m)	100.0	0.00	0.09	
		Forest area	100.6	0.66	0.06	
		Edge area (2000 m) + Forest area	100.9	0.90	0.06	
q =2	Gamma	Forest area	76.6	0.00	0.07	
		Edge area (2000 m)	76.8	0.22	0.06	
		Null model	77.7	1.19	0.04	
		Forest area + Tree abundance	77.8	1.21	0.04	
		Forest area + FBA	77.8	1.22	0.04	
		Edge area (2000 m) + Forest area	77.9	1.38	0.04	
		Edge area (2000 m) + Distance to CF	78.4	1.83	0.03	
		Edge area (2000 m) + Tree richness	78.4	1.84	0.03	
Functional diversity						
q =0	Gaussian	Edge area (1000 m) + Tree richness	165.4	0.00	0.23	
q =1	Gaussian	Edge area (2000 m) + Tree richness	132.4	0.00	0.13	
		Edge area (2000 m) + Forest area	134.3	1.88	0.05	
		Edge area (2000 m)	134.4	1.94	0.05	
		Edge area (2000 m) + Tree richness + Vegetation clutter	134.4	1.95	0.05	
q =2	Gaussian	Forest area	126.3	0.00	0.07	
		Edge area (2000 m) + Tree richness	126.3	0.02	0.07	
		Edge area (2000 m)	127.0	0.77	0.05	
		Tree richness	127.4	1.10	0.04	
		Null model	127.6	1.38	0.04	
		Edge area (2000 m) + Forest area	127.7	1.40	0.04	
		Edge area (2000 m) + Vegetation clutter	128.0	1.75	0.03	
Phylogenetic diversity						
q =0	Gaussian	Edge area (1000 m) + Tree richness	369.6	0.00	0.17	
		Edge area (1000 m)	370.4	0.78	0.11	
		Edge area (1000 m) + Forest area	371.0	1.33	0.09	
q =1	Gaussian	Edge area (2000 m)	336.4	0.00	0.10	
		Edge area (2000 m) + FBA	337.5	1.17	0.05	
		Null model	337.6	1.28	0.05	
		FBA	337.7	1.31	0.05	
		Edge area (2000 m) + Distance to CF	337.7	1.36	0.05	
		Edge area (2000 m) + Tree abundance	338.2	1.79	0.04	
q =2	Gaussian	Null model	322.0	0.00	0.08	
		FBA	322.1	0.17	0.07	
		Edge area (2000 m)	322.5	0.51	0.06	
		Edge area (2000 m) + Distance to CF	323.3	1.35	0.04	
		Edge area (2000 m) + FBA	323.5	1.56	0.04	
		Tree abundance	323.7	1.75	0.03	
		Edge area (2000 m) + Tree abundance	323.8	1.83	0.03	
forest cover (i.e., old-growth and/or secondary vegetation in different stages of regeneration, in percentage); and (x) edge area, which represents the sum of the edge area of all patches, considering 30 m from the edge to the interior of the forest; for the continuous forest we assigned the value of the patch with the smallest edge area. We selected this edge width because it is the area with the greatest invasion of disturbance-adapted plants and understorey-foliage density (Laurance et al., 2002) that can hinder the flight of bats (Table S2). Variables (i) to (iv) were obtained from Benchimol and Peres (2015a) within the same forest plots. All patch and landscape metrics were measured in QGIS software (Quantum GIS, 2018) using RapidEye© high-resolution (5-m pixel) imagery of the Balbina archipelago and adjacent continuous forest (see Benchimol and Peres, 2021).
Multiscale analysis is considered an appropriate approach when species and taxonomic groups have distinct life history characteristics that influence how they use and move through the landscape (Jackson and Fahrig, 2015), including bats (Gorresen and Willig, 2004; Carvalho et al., 2023). Thus, we measured forest cover and edge area at different buffer radii (500, 1000, 1500 and 2000 m) with a bat recorder at the centre of the buffer (Fig. 1). Those radial sizes were selected to encompass the home range sizes of aerial insectivorous bat species recorded at Balbina (Bradbury and Vehrencamp, 1976).
2.5.		Data analysis
We compared q orders of each dimension of diversity between insular and continuous forest sites using permutation t-tests with 9999 randomizations (‘perm.test’ function in the broman package; Broman, 2021). We further assessed the intercorrelation between each diversity dimension and evaluated the usefulness of each dimension of diversity as a surrogate of other dimensions. To do this, we use Pearson statistics to correlate the three dimensions to the same order of q.
We evaluated the effects of local, patch and landscape predictors on each dimension of bat diversity and q order using Generalized Linear Models (GLMs). We selected the GLM family checking the residual distributions, over-and under-dispersion and presence of outliers using the ‘simulateResiduals’ function in the DHARMa package (Hartig, 2021). The most suitable GLM family for each diversity dimension is presented in Table 1. Before modelling, fire severity, distance to the forest edge, forest area, landscape-scale forest cover, and edge area were log-transformed to normalize distributions. All predictor variables were also standardized to a mean of zero and a standard deviation of one to facilitate comparisons of their effects size (‘decostand’ function and standardized method in vegan package; Oksanen et al., 2020).
We tested the ‘scale of effect’ (Jackson and Fahrig, 2015) of landscape variables (forest cover and edge area) by selecting the ‘best’ scale for each dimension of diversity. First, we tested for multicollinearity among all 10 predictor variables using the Variance Inflation Factor (VIF) using the usdm package (‘vif’ function; Naimi et al., 2014) for each scale separately. Our analysis indicated high multicollinearity (VIF ≥ 10, as suggested by Naimi et al., 2014) of fire severity, distance to edge, and forest cover at all scales. We therefore removed those variables from subsequent analyses, retaining seven predictor variables for each scale of all diversity dimensions (tree species richness, tree abundance, vegetation clutter, FBA, distance to CF, forest area, and landscape edge area). After this procedure, we selected the ‘best’ scale of the landscape edge area for each dimension of diversity using the Akaike Information Criterion corrected for small sample sizes (AICc) using the MuMIn package (Barto´n, 2020). Higher Akaike weight values (wi) identified the ‘best’ models (Table S3).
After selecting the scale, we constructed all possible combinations of the seven predictor variables of each GLM using the ‘dredge’ function of the MuMIn package. We then ranked the set of predictor variables using the Small-Sample Corrected Akaike Information Criterion (AICc) and considered as plausible models those with ΔAICc ≤ 2.0 (Burnham and Anderson, 2002). We also measured the relative importance of each predictor variable using the ‘sw’ function of the MuMIn package. In addition, we performed model averaging based on AICs with a 95 % confidence (Burnham et al., 2011). We computed both the magnitude and direction of the coefficients of predictor variables of the top-ranking model (ΔAIC ≤ 2.0) using the ‘model.avg’ function in the MuMln package.
3.		Results
We recorded 29,259 bat passes from 17 species and one phonic complex (Eptesicus brasiliensis/E. chiriquinus) across all 34 sampling plots (Table S4). Those species represent 10 genera and 5 families. The number of species recorded at each plot varied from 1 to 10 (mean ± SD = 4.6 ± 2.4), and bat activity ranged from 4 to 1940 (386.6 ± 495.4) bat passes. The most common species were Cen- tronycteris maximilliani (6196 passes), Saccopteryx bilineata (2200 passes) and Pteronotus rubiginosus (1246 passes), which were present in 22, 11, and 20 surveyed sites, respectively. No species was omnipresent at all sampling plots and only four species were recorded in more than 50 % of all surveyed plots (C. maximilliani, S. leptura, P. rubiginosus, Cormura brevirostris) (Table S4).
Comparisons of diversity dimensions between continuous forest and islands showed greater taxonomic diversity in continuous forest for all q orders (Table S5). For functional and phylogenetic diversity, only q = 0 was greater in CFs, while for orders that consider the weight of species activity (q = 1 and q = 2) there was no difference between habitats (Table S5).
Our best-supported models (ΔAICc ≤ 2) carry substantial uncertainty (wi ≤ 0.23) in explaining the influence of local vegetation, patch, and landscape variables on bat taxonomic, functional, and phylogenetic diversity (Table 1). Only the q = 0 and q = 1 orders received any support (wi ≥ 0.10; Table 1). For q = 2 the support was low (wi ≤ 0.07; Table 1). The best candidate model to predict taxonomic, functional, and phylogenetic diversity included landscape edge area and tree species richness (Table 1). Forest area also explained the taxonomic diversity in terms of species richness (q = 0). The results of the model-averages (Table 2) showed associations between the three dimensions of bat diversity and environmental variables similar to those found in the AIC models (Table 1). Landscape edge area was the most informative variable, showing high relative importance (> 0.75; Table 2). Edge area negatively influenced all dimensions of diversity for order q = 0, and functional diversity for order q = 1 (Fig. 2; Table 2). Tree richness positively

Table 2
Model average parameters and relative importance for top-ranked model (ΔAIC ≤ 2.00) in the 95 % confidence set of the relationship between taxonomic, functional, and phylogenetic diversity and local, patch and landscape scale predictors of the Balbina Hydroelectric Dam, Central Amazon, Brazil. Significant results are indicated in bold.
Response variables	Predictor variables	Estimate	Std error	z	P	Relative importance	
Taxonomic diversity q =0	(Intercept)	1.48	0.08	16.95	< 0.0001		
	Tree richness	0.14	0.10	1.35	0.18	0.45	
	Tree abundance	-0.01	0.10	0.05	0.96	0.22	
	Vegetation clutter	-0.02	0.09	0.24	0.81	0.22	
	FBA	-0.01	0.09	0.12	0.91	0.21	
	Distance to CF	-0.04	0.11	0.35	0.73	0.23	
	Forest area	0.14	0.12	1.13	0.26	0.41	
	Edge area (1000 m)	¡0.28	0.09	3.03	0.002	0.95	
q =1	(Intercept)	2.29	0.17	12.87	< 0.0001		
	Tree richness	0.18	0.24	0.74	0.46	0.31	
	Tree abundance	-0.09	0.21	0.42	0.67	0.23	
	Vegetation clutter	-0.08	0.19	0.40	0.69	0.22	
	FBA	-0.17	0.19	0.85	0.39	0.28	
	Distance to CF	0.10	0.23	0.43	0.67	0.24	
	Forest area	0.32	0.22	1.40	0.16	0.46	
	Edge area (2000 m)	-0.36	0.20	1.76	0.08	0.61	
q =2	(Intercept)	0.53	0.04	13.02	< 0.0001		
	Tree richness	-0.03	0.06	0.51	0.61	0.28	
	Tree abundance	0.03	0.05	0.63	0.53	0.26	
	Vegetation clutter	0.01	0.04	0.29	0.77	0.22	
	FBA	0.04	0.05	0.87	0.39	0.30	
	Distance to CF	-0.04	0.05	0.73	0.47	0.29	
	Forest area	-0.07	0.05	1.43	0.15	0.51	
	Edge area (2000 m)	0.06	0.04	1.32	0.19	0.47	
Functional diversity q =0	(Intercept)	6.48	0.45	13.79	< 0.0001		
	Tree richness	1.22	0.53	2.22	0.026	0.73	
	Tree abundance	0.21	0.54	0.36	0.72	0.21	
	Vegetation clutter	-0.18	0.51	0.34	0.73	0.21	
	FBA	0.05	0.52	0.10	0.92	0.20	
	Distance to CF	-0.30	0.54	0.53	0.60	0.22	
	Forest area	0.93	0.74	1.23	0.22	0.39	
	Edge area (1000 m)	-2.25	0.53	4.08	< 0.0001	1.00	
q =1	(Intercept)	3.82	0.28	13.30	< 0.0001		
	Tree richness	0.54	0.34	1.54	0.12	0.51	
	Tree abundance	0.17	0.34	0.49	0.63	0.23	
	Vegetation clutter	-0.30	0.30	0.97	0.33	0.30	
	FBA	-0.15	0.32	0.45	0.66	0.23	
	Distance to CF	0.10	0.37	0.28	0.78	0.22	
	Forest area	0.50	0.42	1.14	0.25	0.41	
	Edge area (2000 m)	¡0.71	0.32	2.14	0.032	0.76	
q =2	(Intercept)	3.33	0.25	12.72	< 0.0001		
	Tree richness	0.41	0.31	1.30	0.20	0.43	
	Tree abundance	0.16	0.31	0.50	0.62	0.24	
	Vegetation clutter	-0.18	0.27	0.63	0.53	0.25	
	FBA	-0.16	0.29	0.54	0.59	0.24	
	Distance to CF	0.12	0.33	0.37	0.71	0.24	
	Forest area	0.42	0.33	1.22	0.22	0.41	
	Edge area (2000 m)	-0.44	0.29	1.47	0.14	0.49	
Phylogenetic diversity q =0	(Intercept)	187.37	9.48	18.98	< 0.0001		
	Tree richness	16.28	10.17	1.54	0.12	0.50	
	Tree abundance	-0.34	10.92	0.03	0.98	0.20	
	Vegetation clutter	-3.66	9.59	0.37	0.71	0.21	
	FBA	-3.45	10.19	0.33	0.74	0.21	
	Distance to CF	-0.40	11.00	0.04	0.97	0.20	
	Forest area	10.89	13.14	0.80	0.42	0.30	
	Edge area (1000 m)	¡54.58	10.02	5.24	< 0.0001	1.00	

(continued on next page)
Table 2 (continued)
Response variables	Predictor variables	Estimate	Std error	z	P	Relative importance	
q =1	(Intercept)	102.66	5.56	17.75	< 0.0001
Tree richness	0.30	7.32	0.04	0.97	0.22
Tree abundance	-3.46	6.69	0.50	0.62	0.24
Vegetation clutter	-2.02	5.93	0.33	0.74	0.22
FBA	-7.81	6.04	1.24	0.21	0.39
Distance to CF	5.54	7.28	0.74	0.46	0.28
Forest area	4.98	7.24	0.67	0.51	0.26
Edge area (2000 m)	-11.32	6.36	1.72	0.09	0.60	
q =2	(Intercept)	87.50	4.49	18.74	< 0.0001
Tree richness	-0.86	5.81	0.14	0.89	0.22
Tree abundance	-3.68	5.34	0.66	0.51	0.26
Vegetation clutter	-1.01	4.79	0.20	0.84	0.21
FBA	-6.40	4.83	1.28	0.20	0.40
Distance to CF	5.06	5.71	0.86	0.39	0.30
Forest area	3.73	5.69	0.63	0.53	0.26
Edge area (2000 m)	-7.12	5.26	1.31	0.19	0.42	

influenced functional diversity for q = 0 (Fig. 2; Table 2).
The correlation between all dimensions of bat diversity was high (r ≥ 0.86; Fig. 3) for order q that considers species richness (q = 0). For orders that considered the weight of bat activity (q = 1 and q = 2), correlations with functional diversity were low (r ≤ 0.44; Fig. 3), while correlations between taxonomic and phylogenetic diversity were high (r ≥ 0.86; Fig. 3).
4.		Discussion
Our study demonstrates the long-term deleterious effects of landscape insularization at the Balbina Dam on various facets of insectivorous bat diversity. Taxonomic, functional, and phylogenetic diversities were reduced on ~30-year-old forest islands, which exhibited species-poor assemblages, reduced ecological functions and evolutionarily redundancy compared to continuous forests. The different dimensions of bat diversity were strongly influenced by forest habitat quality related to local and landscape-scale characteristics. Balbina forest islands, especially small ones, harboured species-poor tree floras and higher edge habitat density compared to adjacent continuous forests (Benchimol and Peres, 2015a). Accordingly, we found that assemblages located in edge-dominated landscapes with species-poor tree assemblages where the most affected by hydropower insularization. These relationships were evident for species richness (q = 0) and assemblages formed by common species (q = 1), but not for dominant species (q = 2). Finally, we showed that taxonomical diversity can be a good indicator of phylogenetic diversity, independently of how the data were weighted, but it cannot reflect functional diversity when species activity or dominance are weighted.
4.1.		Responses to insularization
Tracts of undisturbed forests exhibited greater species richness and overall bat activity compared to islands (Di Ponzio et al., 2023). Furthermore, the higher taxonomic diversity in continuous forests was associated with the presence of species with morphoecological characteristics and lineages that are more diverse and complete than those found in island assemblages. Consistent with our predictions, continuous forests therefore retained greater taxonomic, functional, and phylogenetic diversity compared to the Balbina Dam reservoir islands. Insularization can lead to the local extinction of species with conserved niches related to environmental conditions of the continuous forest. Over time, the loss of species with specific and conserved traits can erode the evolutionary history, reducing functional and phylogenetic diversities within fragmented ecosystems (Bregman et al., 2015).
Our findings differ with a similar study of aerial insectivorous bats in forest patches of a neighbouring landscape (~70 km west), the Biological Dynamics of Forest Fragments Project (BDFFP) (L´opez-Baucells et al., 2022). Although this landscape shares a similar bat fauna, the BDFFP habitat matrix is dominated by secondary forests after 30 years of regeneration, which are more permeable for bat mobility across the landscape. All three facets of diversity in the interiors of BDFFP forest fragments were not different from those in continuous forests (Lo´pez-Baucells et al., 2022). This indicates that the quality of the surrounding matrix can be more important for species habitat use than habitat quality within remnants. However, additional comparative studies focusing on matrix quality are essential to fully understand whether an open-habitat matrix dominated by water or pastures is more inhospitable to tropical bats than forested matrices undergoing advanced regeneration (Farneda et al., 2020b, 2024).
4.2.		Multi-faceted diversity associated with species activity
The disparity in diversity dimensions between islands and continuous forest habitats was influenced by the overall amount of bat activity, with diversity responses diverging between different orders of q. When the models were weighted by species abundance (q = 1), or when common species were given more weight (q = 2), no differences were found in the functional or phylogenetic diversity. Only taxonomic diversity was higher in continuous forests for all orders of q. The weight of bat activity exposed the dominance of a few

Fig. 2. Relationships between taxonomic diversity order q = 0 and forest edge area around the islands (a), functional diversity (order q = 0) and forest edge area (b), and tree species richness (c), functional diversity (order q = 1) and forest edge area (d), and phylogenetic diversity (order q = 0) and forest edge area (e) at the Balbina archipelagic landscape. Red and yellow circles indicate continuous forest sites (n = 6 sites) and surveyed islands (n = 28 islands), respectively. Grey areas around linear trend lines represent 95 % confidence intervals. Data points were jittered horizontally for visualization purposes.

Fig. 3. Correlations between taxonomic, functional, and phylogenetic diversity indices of aerial insectivorous bats surveyed at 34 forest sites within the Balbina forest archipelago. Dimensions of diversity were assessed using three orders of q (0, 1 and 2), which determine the sensitivity of the indices to the relative abundance (soundscape activity) of bat species.
species on islands from divergent lineages and divergent functional traits, thus increasing the phylogenetic and functional diversity on forest islands to the same level as the continuous forest. Therefore, the weight of bat activity resulted in changes in taxonomic diversity between the mainland and islands, but did not affect the functional and phylogenetic diversity of bat assemblages.
4.3.		Local and landscape‑scale drivers of multi-faceted diversities
Landscape forest edge was the best predictor of bat diversity at the taxonomic, functional, and phylogenetic levels at the Balbina archipelagic landscape, whereas tree diversity (species/ha) positively influenced patterns of functional diversity. Edge effects are powerful forces that drastically change the structure and diversity of tree assemblages and forest functioning, not least through changes in the physical profile of the environment (Nunes et al., 2023; Smith et al., 2023). Small islands are more prone to disturbances from windstorms, wind throws, soil desiccation and understorey fires compared to larger islands and continuous forests (Benchimol and Peres, 2015a). Small islands (<10 ha) harbour depauperate assemblages of tree species, accompanied by a severe reduction in the number of emergent trees as revealed by UAV LiDAR scanning (C. A. Peres, unpubl. data). This decline contributes to reduce the vertical stratification and vegetation complexity, and the availability of ecological niches for bats (Nún˜ez et al., 2019). Such conditions give rise to additive effects (Porensky and Young, 2013), including widespread defaunation of phytophagous insects (Storck-Tonon et al., 2020), suggesting of a reduction in the prey base for insectivorous bats.
At Balbina, the decrease in all diversity dimensions observed on edge-dominated forest landscapes and the reduced functional diversity in floristically impoverished small islands indicate a decline in both bat species and overall bat activity, coupled with alterations in species composition and the distribution of foraging traits (Colombo et al., 2023; Di Ponzio et al., 2023). This decline in species and bat activity not only impacts taxonomic diversity but also results in phylogenetically homogeneous and redundant assemblages (Farneda et al., 2015, 2022, 2024; Santo-Silva et al., 2021).
Niche conservatism suggest that differences in community structure between environments may result from niche conservatism, which limits the dispersal of lineages with different ecological traits between environments (Wiens and Graham, 2005). Species with strong niche conservatism may fail to colonize fragmented habitats if they lack the ability to adapt to degraded environments (Luther et al., 2020). Only species with traits suitable for altered conditions (e.g., tolerance to edge effects or disturbed habitats) persist. The prevalence of open-habitat foraging species around small islands and species that forage in small canopy gaps within highly forested landscapes further contribute to shifts in functional diversity (Colombo et al., 2023; Hazard et al., 2023). When dominant species (q = 2) were given more weight, no local vegetation, patch and landscape metrics played an important role in taxonomic, functional, and phylogenetic diversities. This indicates that dominant species are uniformly distributed along environmental gradients of remnant forest isolates, which are overwhelmingly dominated by small islands. This archipelagic configuration reflects overall patterns of vertebrate local extinctions (e.g. only 25 of all 3545 Balbina islands currently retains 80 % of a full complement of midsized to large vertebrate species: Benchimol and Peres, 2015b).
Several studies have indicated that forest area is a key predictor influencing the diversity patterns of lizards, frogs, forest birds, and non-volant mammals at Balbina (Palmeirim et al., 2024), and of small mammals across the Brazilian Atlantic Forest biome (Bovendorp et al., 2019). However, we failed to uncover strong responses in any of the dimensions of bat diversity to forest area. This indicates that bat diversity was primarily affected by the habitat quality, at both local and landscape scales, represented here by tree species richness and landscape forest edge (Benchimol and Peres, 2015a; Farneda et al., 2024). However, we cannot rule out the influence of forest cover on a landscape scale, since we removed this predictor variable from our analyses due to collinearity with other variables. The impact of island habitat amount and forest cover may result from a combination of direct and indirect effects mediated by relative edge area. However, patterns arising from habitat loss at Balbina still need to be disentangled from those attributed to habitat fragmentation per se (Didham et al., 2012; Ruffell et al., 2016). As most intense edge effects occur within 100 m of an edge (Laurance et al., 2002), small islands less than 200-m wide can be considered 100 % comprised of edge forests (Laurance et al., 2002). In Balbina, 98.6 % of all 3525 islands are comprised of at least 50 % in forest edges (or 20.9 % of the islands, considering a 30 m wide border as employed here), indicating marked overall habitat degradation. Furthermore, vegetation modification on small islands and their edges is an ongoing process at Balbina, with rare tree species prone to extinction facing reduced recruitment and density, along with the loss of forest biomass (Jones et al., 2021). This elevated edge-habitat ratio potentially benefits bats specialized on forest edges and open-space foraging, while adversely affecting forest-specialist species (Yoh et al., 2022; Hazard et al., 2023). This phenomenon elucidates the observed decrease in species abundance and richness, functional diversity, and phylogenetic diversification on small islands embedded within landscapes dominated by forest edges.
4.4.		Taxonomic diversity as a surrogate of functional and phylogenetic diversity
Our results show that taxonomic diversity can predict phylogenetic and functional diversity. Furthermore, the correlation between functional and phylogenetic diversity may indicate phylogenetic niche conservatism, in which closely related species tend to be more phenotypically similar to each other (Losos, 2008; Wiens et al., 2006). Given the high correlation between the three dimensions of diversity, we presume that landscape edge area and tree species richness had an influence on all three dimensions. Thus, our study provides evidence that the community structure of aerial insectivorous bats results from interacting ecological and evolutionary processes.
However, correlations between the three dimensions of diversity were found only for species richness data (q = 0). This phenomenon has been previously documented for small mammals and lizards within the Balbina archipelago (Palmeirim et al., 2021). On species-poor islands dominated by a single bat species, functional diversity increases disproportionately in relation to taxonomic diversity. However, the distance among phylogenetic branches still reflects taxonomic diversity in species-poor assemblages dominated by a single species. Thus, when a proxy of overall abundance (here, bat activity) is incorporated (q = 1 and q = 2), taxonomic diversity can still be a good indicator of phylogenetic diversity, but not of functional diversity.
5.		Conclusion
Developing countries have long been a target by the hydropower industry, which in emergent economies can be financed by the national treasury. The Brazilian Amazon basin alone may soon have the largest number of dams in South America (Zarfl et al., 2015; Moran et al., 2018), with 254 new hydroelectric plants being planned by government agencies. This alarming figure draws attention to developing more comprehensive protocols to better understand the effects of hydropower development on biodiversity. Forest insularization caused by dam impoundment is a dramatic reconfiguration of the landscape structure that transforms species-rich terrestrial habitats into aquatic environments (Jones et al., 2021). Species diversity in archipelagic landscapes of hydroelectric reservoirs depends on the morphological and behavioural characteristics of each species that facilitate dispersal through the inhospitable open-water matrix, as well as their use of relict habitats (Meyer et al., 2008; Colombo et al., 2023). Phylogenetically related species tend to share analogous life history characteristics, thereby leading to similar responses to the environmental attributes inherent to any given fragmented landscape (Burns and Strauss, 2011; Kamilar and Cooper, 2013). Findings emerging from this study follow this trend and show that taxonomic diversity was sufficient to predict the functional and phylogenetic richness of aerial insectivorous bats. The process of forest fragmentation has led to the depletion of all dimensions of diversity, which is largely attributed to the low-quality vegetation profile of small islands. Following a 30-year period of isolation, the vegetation on Balbina islands has been severely modified by edge effects; for instance, 64.5 % of the aggregate island area (sum of the area of all islands combined) is now comprised of edge habitat. This represents an extensive area encompassing 76,185 ha of low-quality forest to maintain the taxonomic, functional, and phylogenetic diversities of aerial insectivorous bats at levels comparable to those found in adjacent undisturbed continuous forests.
Across the Brazilian Amazon, plans are underway for the construction of over 100 new hydroelectric plants in the near future (SIGEL/ANEEL, 2022). Notably, energy strategists guiding development policy in Brazil are predisposed towards dams fuelled by extensive reservoirs (Fearnside, 2017). Given this future scenario, we highlight the need to establish protected forest areas around major dams, given that newly created forest islands cannot retain the original vegetation quality and, consequently, high biodiversity of bats and other key ecological groups. Comprehensive protection is therefore a prerequisite to preserve the integrity of forest ecosystems and ensure the provision of essential resources for a diverse array of bat species that play a crucial role in sustaining ecological services.
Ethics approval statement and declarations
This study was authorized and registered under licences from Instituto Chico Mendes de Conservaç˜ao da Biodiversidade - ICMBio (SISBIO #53340/1).
Funding
We are grateful to ICMBio (Chico Mendes Institute of Biodiversity), REBIO Uatum˜a/ICMBio and Gilmar Klein for logistical support during fieldwork. This study was funded by the The Rufford Small Grant Foundation (#19493-1), National Geographic Society (Young Explorer Grant #9997-16), Idea Wild, Wildlife Conservation Society Research Fellowship Program, The Conservation Food and Health Foundation, Programa A´ reas Protegidas da Amazˆonia, Amazonas Distribuidora de Energia S/A, Associaç˜ao Comunidade Waimiri Atroari, and National Geographic Society Explorer (grant #93497C awarded to CAP). PEDB was supported by a postdoctoral scholarship from Programa Nacional de Po´s-Doutorado/Coordenaça˜o de Aperfeiçoamento de Pessoal de Nível Superior (#88887.370067/ 2019-00). RDP and GTC were supported by Conselho Nacional de Desenvolvimento Científico e Tecnolo´gico (CNPq) studentships, and MB received a productivity grant from CNPq (#304189/2022-7).
CRediT authorship contribution statement
Paulo Estefano D. Bobrowiec: conceptualization; designed this study; data curation; formal analysis; funding acquisition; investigation; methodology; project administration; resources; supervision; visualization; writing - original draft; writing - review & editing; Raffaello Di Ponzio: conceptualization; designed this study; data curation; funding acquisition; investigation; methodology; project administration; resources; writing - review & editing; Guthieri Teixeira Colombo: investigation; writing - review & editing; Carlos A. Peres: conceptualization; designed this study, funding acquisition; writing - review & editing; Maíra Benchimol: conceptualization; designed this study; data curation; investigation; funding acquisition; writing - review & editing.
Declaration of Competing interest
The authors declare the following financial interests/personal relationships which may be considered as potential competing interests. Raffaello Di Ponzio reports financial support and travel were provided by Chico Mendes Institute of Biodiversity. Raffaello Di Ponzio reports financial support was provided by The Rufford Small Grant Foundation. Raffaello Di Ponzio reports financial support was provided by National Geographic Society. Raffaello Di Ponzio reports equipment, drugs, or supplies was provided by Idea Wild. Maira Benchimol reports financial support was provided by Wildlife Conservation Society Research Fellowship Program. Maira Benchimol reports financial support was provided by The Conservation Food and Health Foundation. Raffaello Di Ponzio reports travel was provided by Programa A´ reas Protegidas da Amazoˆnia. Raffaello Di Ponzio reports travel was provided by Associaç˜ao Comunidade Waimiri Atroari. Carlos Peres reports financial support was provided by National Geographic Society Explorer. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgements
We thank Dedeu and Thiago Bicudo for their assistance during fieldwork and Giulliana Appel for help in bat species identification. We thank the anonymous reviewer and Associate Editor Franciany Braga Pereira for commenting on drafts of the paper.
Appendix A. Supporting information
Supplementary data associated with this article can be found in the online version at doi:10.1016/j.gecco.2025.e03488.
Data Availability
Data will be made available on request.
References
Aguiar, L.M., Bueno-Rocha, I.D., Oliveira, G., Pires, E.S., Vasconcelos, S., Nunes, G.L., Frizzas, M.R., Togni, P.H., 2021. Going out for dinner-the consumption of agriculture pests by bats in urban areas. PLoS One 16 (10), e0258066. https://doi.org/10.1371/journal.pone.0258066.
Appel, G., Lo´pez-Baucells, A., Ernest-Magnusson, W., Bobrowiec, P.E.D., 2017. Aerial insectivorous bat activity in relation to moonlight intensity. Mamm. Biol. 85, 37–46. https://doi.org/10.1016/j.mambio.2016.11.005.
Arias-Aguilar, A., Hintze, F., Aguiar, L.M., Rufray, V., Bernard, E., Pereira, M.J.R., 2018. Who’s calling? acoustic identification of Brazilian bats. Mammal. Res 63, 231–253. https://doi.org/10.1007/s13364-018-0367-z.
Arroyo-Rodríguez, V., Melo, F.P., Martínez-Ramos, M., Bongers, F., Chazdon, R.L., Meave, J.A., Norden, N., Santos, B.A., Leal, I.R., Tabarelli, M., 2017. Multiple successional pathways in human-modified tropical landscapes: new insights from forest succession, forest fragmentation and landscape ecology research. Biol Rev 92 (1), 326–340. https://doi.org/10.1111/brv.12231.
Barto´n, K., 2020. MuMIn: Multi-Model Inference. R package version 1.43.17. 〈https://CRAN.R-project.org/package=MuMIn〉.
Benchimol, M., Peres, C.A., 2015a. Edge-mediated compositional and functional decay of tree assemblages in Amazonian forest islands after 26 years of isolation. J. Ecol. 103 (2), 408–420. https://doi.org/10.1111/1365-2745.12371.
Benchimol, M., Peres, C.A., 2015b. Predicting local extinctions of Amazonian vertebrates in forest islands created by a mega dam. Biol. Conserv 187, 61–72. https:// doi.org/10.1016/j.biocon.2015.04.005.
Benchimol, M., Peres, C.A., 2021. Determinants of population persistence and abundance of terrestrial and arboreal vertebrates stranded in tropical forest land-bridge islands. Conserv Biol. 35 (3), 870–883. https://doi.org/10.1111/cobi.13619.
Bogoni, J.A., Peres, C.A., Ferraz, K.M., 2020. Effects of mammal defaunation on natural ecosystem services and human well being throughout the entire Neotropical realm. Ecosyst. Serv. 45, e101173. https://doi.org/10.1016/j.ecoser.2020.101173.
Bovendorp, R.S., Brum, F.T., McCleery, R.A., Baiser, B., Loyola, R., Cianciaruso, M.V., Galetti, M., 2019. Defaunation and fragmentation erode small mammal diversity dimensions in tropical forests. Ecography 42, 23–35. https://doi.org/10.1111/ecog.03504.
Bradbury, J.W., Vehrencamp, S.L., 1976. Social organization and foraging in emballonurid bats: I. field studies. Behav. Ecol. Socio 1, 337–381. https://doi.org/ 10.1007/BF00299399.
Bregman, T.P., Lees, A.C., Seddon, N., MacGregor, H.E., Darski, B., Aleixo, A., Bonsall, M.B., Tobias, J.A., 2015. Species interactions regulate the collapse of biodiversity and ecosystem function in tropical forest fragments. Ecology 96 (10), 2692–2704. https://doi.org/10.1890/14-1731.1.
Broman, K.W., 2021. Broman: Karl Broman’s R Code. R package version 0.72-4. 〈https://CRAN.R-project.org/package=broman〉.
Burnham, K.P., Anderson, D.R., 2002. Model selection and multimodel inference: a practical information-theoretic approach. Springer, New York.
Burnham, K.P., Anderson, D.R., Huyvaert, K.P., 2011. AIC model selection and multimodel inference in behavioral ecology: some background, observations, and comparisons. Behav. Ecol. Socio 65, 23–35. https://doi.org/10.1007/s00265-010-1029-6.
Burns, J.H., Strauss, S.Y., 2011. More closely related species are more ecologically similar in an experimental test. PNAS 108 (13), 5302–5307. https://doi.org/ 10.1073/pnas.1013003108.
Cadotte, M., Albert, C.H., Walker, S.C., 2013. The ecology of differences: assessing community assembly with trait and evolutionary distances. Ecol. Lett. 16, 1234–1244. https://doi.org/10.1111/ele.12161.
Carvalho, W.D., Rosalino, L.M., Xavier, B.D., de Castro, I.J., Hilario, R.R., Marques, T.M., Toledo, J.J., Vieira, M.V., Palmeirim, J.M., Mustin, K., 2023. The relative importance of forest cover and patch-level drivers for phyllostomid bat communities in the Amazonian savannas. Land. Ecol. 38, 117–130. https://doi.org/ 10.1007/s10980-022-01565-y.
Chao, A., Chiu, C.H., Jost, L., 2014. Unifying species diversity, phylogenetic diversity, functional diversity, and related similarity and differentiation measures through Hill numbers. Annu Rev. Ecol. Evol. Syst. 45, 297–324. https://doi.org/10.1146/annurev-ecolsys-120213-091540.
Cisneros, L.M., Fagan, M.E., Willig, M.R., 2015. Effects of human-modified landscapes on taxonomic, functional and phylogenetic dimensions of bat biodiversity. Divers Distrib. 21, 523–533. https://doi.org/10.1111/ddi.12277.
Colombo, G.T., Di Ponzio, R., Benchimol, M., Peres, C.A., Bobrowiec, P.E.D., 2023. Functional diversity and trait filtering of insectivorous bats on forest islands created by an Amazonian mega dam. Funct. Ecol. 37 (1), 99–111. https://doi.org/10.1111/1365-2435.14118.
Di Ponzio, R., Colombo, G.T., Bicudo, T., Benchimol, M., Pereira, M.J.R., Peres, C.A., Bobrowiec, P.E.D., 2023. Aerial insectivorous bat responses to 30 years of forest insularization in a dam-created Amazonian archipelagic landscape. Biol. Conserv 285, e110222. https://doi.org/10.1016/j.biocon.2023.110222.
Didham, R.K., Kapos, V., Ewers, R.M., 2012. Rethinking the conceptual foundations of habitat fragmentation research. Oikos 121 (2), 161–170. https://doi.org/ 10.1111/j.1600-0706.2011.20273.x.
Emer, C., Venticinque, E.M., Fonseca, C.R., 2013. Effects of dam-induced landscape fragmentation on Amazonian ant–plant mutualistic networks. Conserv Biol. 27 (4), 763–773. https://doi.org/10.1111/cobi.12045.
Estrada-Villegas, S., Meyer, C.F.J., Kalko, E.K.V., 2010. Effects of tropical forest fragmentation on aerial insectivorous bats in a land-bridge Island system. Biol. Conserv 143 (3), 597–608. https://doi.org/10.1016/j.biocon.2009.11.009.
Farneda, F.Z., Rocha, R., L´opez-Baucells, A., Groenenberg, M., Silva, I., Palmeirim, J.M., Bobrowiec, P.E.D., Meyer, C.F.J., 2015. Trait-related responses to habitat fragmentation in Amazonian bats. J. Appl. Ecol. 52, 1381–1391. https://doi.org/10.1111/1365-2664.12490.
Farneda, F.Z., Rocha, R., L´opez-Baucells, A., Sampaio, E.M., Palmeirim, J.M., Bobrowiec, P.E.D., Meyer, C.F.J., 2018. Functional recovery of Amazonian bat assemblages following secondary forest succession. Biol. Conserv 218, 192–199. https://doi.org/10.1016/j.biocon.2017.12.036.
Farneda, F.Z., Meyer, C.F.J., Grelle, C.E.V., 2020b. Effects of land-use change on functional and taxonomic diversity of Neotropical bats. Biotropica 52, 120–128. https://doi.org/10.1111/btp.12736.
Farneda, F.Z., Grelle, C.E.V., Rocha, R., Ferreira, D.F., Lo´pez-Baucells, A., Meyer, C.F.J., 2020a. Predicting biodiversity loss in island and countryside ecosystems through the lens of taxonomic and functional biogeography. Ecography 43 (1), 97–106. https://doi.org/10.1111/ecog.04507.
Farneda, F.Z., Rocha, R., Aninta, S.G., Lo´pez-Baucells, A., Sampaio, E.M., Palmeirim, J.M., Bobrowiec, P.E.D., Dambros, C.S., Meyer, C.F.J., 2022. Bat phylogenetic responses to regenerating Amazonian forests. J. Appl. Ecol. 59, 1986–1996. https://doi.org/10.1111/1365-2664.14041.
Farneda, F.Z., Ota´lora-Ardila, A., Meyer, C.F.J., L´opez-Ar´evalo, H.F., G´omez-Posada, C., Polanía, J., 2024. Multiple dimensions of phyllostomid bat biodiversity across ecosystems of the Orinoco Llanos. Anim. Conserv. https://doi.org/10.1111/acv.12941.
Fearnside, P.M., 2017. Dams with big reservoirs: Brazil’s hydroelectric plants threaten its Paris climate commitments. 〈https://www.theglobalist.com/dams-climate- changeglobal-warming-brazil-paris-agreement/〉 Accessed 19 January 2021.
Finer, M., Jenkins, C.N., 2012. Proliferation of hydroelectric dams in the Andean Amazon and implications for Andes-Amazon connectivity. PLoS ONE 7, e35126. https://doi.org/10.1371/journal.pone.0035126.
Garbino, G.S., Tavares, V.C., 2018. Roosting ecology of Stenodermatinae bats (Phyllostomidae): evolution of foliage roosting and correlated phenotypes. Mammal. Rev. 48, 75–89. https://doi.org/10.1111/mam.12114.
Gorresen, P.M., Willig, M.R., 2004. Landscape responses of bats to habitat fragmentation in Atlantic forest of Paraguay. J. Mammal. 85 (4), 688–697. https://doi.org/ 10.1644/BWG-125.
Hartig, F., 2021. DHARMa: Residual diagnostics for hierarchical (multi-level/mixed) regression models. R package version 0.4.6, 〈https://CRAN.R-project.org/ package=DHARMa〉.
Hazard, Q.C., Froidevaux, J.S., Yoh, N., Moore, J., Senawi, J., Gibson, L., Palmeirim, A.F., 2023. Foraging guild modulates insectivorous bat responses to habitat loss and insular fragmentation in peninsular Malaysia. Biol. Conserv 281, e110017. https://doi.org/10.1016/j.biocon.2023.110017.
Jackson, H.B., Fahrig, L., 2015. Are ecologists conducting research at the optimal scale? Glob. Ecol. Biogeogr. 24 (1), 52–63. https://doi.org/10.1111/geb.12233.
Jones, I.L., Saldanha, A.B., Benchimol, M., Palmeirim, A.F., Storck-Tonon, D., Peres, C.A., 2021. Using relict species–area relationships to estimate the conservation value of reservoir islands to improve environmental impact assessments of dams. In: Matthews, T.J., Triantis, K.A., Whittaker, R.J. (Eds.), The species-area relationship: theory and application. Cambridge University Press, Cambridge, pp. 417–437.
Kamilar, J.M., Cooper, N., 2013. Phylogenetic signal in primate behaviour, ecology and life history. Philos. Trans. R. Soc. B: Biol. Sci. 368 (1618), e20120341. https:// doi.org/10.1098/rstb.2012.0341.
Kuebbing, S.E., Maynard, D.S., Bradford, M.A., 2018. Linking functional diversity and ecosystem processes: a framework for using functional diversity metrics to predict the ecosystem impact of functionally unique species. J. Ecol. 106 (2), 687–698. https://doi.org/10.1111/1365-2745.12835.
Laurance, W.F., Lovejoy, T.E., Vasconcelos, H.L., Bruna, E.M., Didham, R.K., Stouffer, P.C., Gascon, G., Bierregaard, R.O., Laurance, S.G., Sampaio, E., 2002. Ecosystem decay of Amazonian forest fragments: a 22-year investigation. Conserv Biol. 16 (3), 605–618. https://doi.org/10.1046/j.1523-1739.2002.01025.x.
Laurance, W.F., Camargo, J.L., Fearnside, P.M., Lovejoy, T.E., Williamson, G.B., Mesquita, R.C., Meyer, C.F.J., Bobrowiec, P.E.D., Laurance, S.G., 2018. An Amazonian rainforest and its fragments as a laboratory of global change. Biol. Rev. 93 (1), 223–247. https://doi.org/10.1111/brv.12343.
Lees, A.C., Peres, C.A., 2009. Gap-crossing movements predict species occupancy in Amazonian forest fragments. Oikos 118 (2), 280–290. https://doi.org/10.1111/ j.1600-0706.2008.16842.x.
Li, D., 2018. HillR: taxonomic, functional, and phylogenetic diversity and similarity through Hill numbers. JOSS 3 (31), e1041. https://doi.org/10.21105/joss.01041.
L´opez-Baucells, A., Rocha, R., Bobrowiec, P.E.D., Bernard, E., Palmeirim, J.M., Meyer, C.F.J., 2016. Field guide to Amazonian Bats. National Institute of Amazonian Research, Manaus.
L´opez-Baucells, A., Rowley, S., Rocha, R., Bobrowiec, P.E.D., Palmeirim, J.M., Farneda, F.Z., Meyer, C.F.J., 2022. Interplay between local and landscape-scale effects on the taxonomic, functional and phylogenetic diversity of aerial insectivorous Neotropical bats. Land. Ecol. 37, 2861–2875. https://doi.org/10.1007/s10980- 022-01493-x.
Losos, J.B., 2008. Phylogenetic niche conservatism, phylogenetic signal and the relationship between phylogenetic relatedness and ecological similarity among species. Ecol. Lett. 11 (10), 995–1003. https://doi.org/10.1111/j.1461-0248.2008.01229.x.
Luther, D.A., Cooper, W.J., Wolfe, J.D., Bierregaard Jr, R.O., Gonzalez, A., Lovejoy, T.E., 2020. Tropical forest fragmentation and isolation: Is community decay a random process? Glob. Ecol. Conserv 23, e01168. https://doi.org/10.1016/j.gecco.2020.e01168.
Mendenhall, C.D., Karp, D.S., Meyer, C.F.J., Hadly, E.A., Daily, G.C., 2014. Predicting biodiversity change and averting collapse in agricultural landscapes. Nature 509 (7499), 213–217. https://doi.org/10.1038/nature13139.
Meyer, C.F., Fründ, J., Lizano, W.P., Kalko, E.K., 2008. Ecological correlates of vulnerability to fragmentation in Neotropical bats. J. Appl. Ecol. 45 (1), 381–391. https://doi.org/10.1111/j.1365-2699.2008.01916.x.
Michalski, F., Norris, D., 2023. Large-scale land-use changes and the amazonian mammal biota. In: Spironello, W.R., Barnett, A.A., Lynch, J.W., Bobrowiec, P.E.D., Boyle, S.A. (Eds.), Amazonian mammals: current knowledge and conservation priorities. Springer, Cham, pp. 323–333.
Moir, M., Richards, L.R., Rambau, R.V., Cherry, M.I., 2021. Functional diversity and trait filtering of insectivorous bats relate to forest biogeography and fragmentation in South Africa. J. Biogeogr. 48 (5), 1170–1182. https://doi.org/10.1111/jbi.14069.
Moran, E.F., Lopez, M.C., Moore, N., Müller, N., Hyndman, D.W., 2018. Sustainable hydropower in the 21st century. PNAS 115 (47), 11891–11898. https://doi.org/ 10.1073/pnas.1809426115.
Naimi, B., Hamm, N.A., Groen, T.A., Skidmore, A.K., Toxopeus, A.G., 2014. Where is positional uncertainty a problem for species distribution modelling? Ecography 37 (2), 191–203. https://doi.org/10.1111/j.1600-0587.2013.00205.x.
Newbold, T., Hudson, L.N., Phillips, H.R., Hill, S.L., Contu, S., Lysenko, I., Blandon, A., Butchart, S.H.M., Booth, H.L., Day, J., De Palma, A., Harrison, M.L.K., Kirkpatrick, L., Pynegar, E., Robinson, A., Simpson, J., Mace, G.M., Scharlemann, J.P.W., Purvis, A., 2014. A global model of the response of tropical and subtropical forest biodiversity to anthropogenic pressures. Proc. R. Soc. Lond. B Biol. Sci. 281 (1792), e20141371. https://doi.org/10.1098/rspb.2014.1371.
Nunes, M.H., Vaz, M.C., Camargo, J.L.C., Laurance, W.F., de Andrade, A., Vicentini, A., Laurance, S., Raumonen, P., Jackson, T., Zuquim, G., Wu, J., Pe˜nuelas, J., Chave, J., Maeda, E.E., 2023. Edge effects on tree architecture exacerbate biomass loss of fragmented Amazonian forests. Nat. Commun. 14 (1), e8129. https:// doi.org/10.1038/s41467-023-44004-5.
Nún˜ez, S.F., L´opez-Baucells, A., Rocha, R., Farneda, F.Z., Bobrowiec, P.E.D., Palmeirim, J.M., Meyer, C.F.J., 2019. Echolocation and stratum preference: key trait correlates of vulnerability of insectivorous bats to tropical forest fragmentation. Front Ecol. Evol. 7, 373. https://doi.org/10.3389/fevo.2019.00373.
Oksanen, J., Blanchet, F.G., Friendly, M., Kindt, R., Legendre, P., McGlinn, D., Minchin, P.R., O’Hara, R.B., Simpson, G.L., Solymos, P., Stevens, M.H.H., Szoecs, E., Wagner, H., 2020. vegan: Community Ecology Package. R package version 2.5-7. 〈https://CRAN.R-project.org/package=vegan〉.
Palmeirim, A.F., Farneda, F.Z., Vieira, M.V., Peres, C.A., 2021. Forest area predicts all dimensions of small mammal and lizard diversity in Amazonian insular forest fragments. Land Ecol. 36 (12), 3401–3418. https://doi.org/10.1007/s10980-021-01311-w.
Palmeirim, A.F., Farneda, F.Z., Vieira, M.V., Peres, C.A., 2022. Emergent properties of species-habitat networks in an insular forest landscape. Sci. Adv. 8 (34). https:// doi.org/10.1126/sciadv.abm0397.
Palmeirim, A.F., Benchimol, M., Storck-Tonon, D., Bueno, A.S., Jones, I.L., Klein, G., Peres, C.A., 2024. Biodiversity responses to insular fragmentation in Amazonia: two decades of research in the Balbina Hydroelectric Reservoir. Zool. Res. https://doi.org/10.24272/j.issn.2097-3772.2023.007.
Petchey, O.L., Evans, K.L., Fishburn, I.S., Gaston, K.J., 2007. Low functional diversity and no redundancy in British avian assemblages. J. Anim. Ecol. 977–985. 〈https://www.jstor.org/stable/4539206〉.
Porensky, L.M., Young, T.P., 2013. Edge-effect interactions in fragmented and patchy landscapes. Conserv Biol. 27 (3), 509–519. https://doi.org/10.1111/ cobi.12042.
Prevedello, J.A., Vieira, M.V., 2010. Does the type of matrix matter? A quantitative review of the evidence. Biodiv Conserv 19 (5), 1205–1223. https://doi.org/ 10.1007/s10531-009-9750-z.
Rabelo, R.M., Bicca-Marques, J.C., Aragon, S., Nelson, B.W., 2017. Are fluvial islands “real” islands for arboreal mammals? uncovering the effect of patch size under the species–area relationship. J. Biogeogr. 44 (8), 1802–1812. https://doi.org/10.1111/jbi.13034.
Ramos Pereira, M.J., Fonseca, C., Aguiar, L.M.S., 2018. Loss of multiple dimensions of bat diversity under land-use intensification in the Brazilian Cerrado. Hystrix 29, 25–32. https://doi.org/10.4404/hystrix-00020-2017.
Ricotta, C., de Bello, F., Moretti, M., Caccianiga, M., Cerabolini, B.E.L., Pavoine, S., 2016. Measuring the functional redundancy of biological communities: a quantitative guide. Methods Ecol. Evol. 7, 1386–1395. https://doi.org/10.1111/2041-210X.12604.
Rossoni, D.M., Costa, B.M., Giannini, N.P., Marroig, G., 2019. A multiple peak adaptive landscape based on feeding strategies and roosting ecology shaped the evolution of cranial covariance structure and morphological differentiation in phyllostomid bats. Evolution 73, 961–981. https://doi.org/10.1111/evo.13715.
Ruffell, J., Banks-Leite, C., Didham, R.K., 2016. Accounting for the causal basis of collinearity when measuring the effects of habitat loss versus habitat fragmentation. Oikos 125 (1), 117–125. https://doi.org/10.1111/oik.01948.
Santo-Silva, E.E., Benchimol, M., Peres, C.A., 2021. Phylogenetic homogenization of Amazonian tree assemblages in forest islands after 26 years of isolation. Appl. Veg. Sci. 24 (3). https://doi.org/10.1111/avsc.12601.
Shi, J.J., Rabosky, D.L., 2015. Speciation dynamics during the global radiation of extant bats. Evolution 69 (6), 1528–1545. https://doi.org/10.1111/evo.12681.
SIGEL/ANEEL, 2022. Agˆencia Nacional de Energia El´etrica, Superintendˆencia de Gest˜ao T´ecnica da Informaç˜ao. 〈https://sigel.aneel.gov.br/〉.
Smith, C.C., Barlow, J., Healey, J.R., de Sousa Miranda, L., Young, P.J., Schwartz, N.B., 2023. Amazonian secondary forests are greatly reducing fragmentation and edge exposure in old-growth forests. Environ. Res Lett. 18 (12), e124016. https://doi.org/10.1088/1748-9326/ad039e.
Srivastava, D.S., Cadotte, M.W., MacDonald, A.A.M., Marushia, R.G., Mirotchnick, N., 2012. Phylogenetic diversity and the functioning of ecosystems. Ecol. Lett. 15, 637–648. https://doi.org/10.1111/j.1461-0248.2012.01795.x.
Storck-Tonon, D., da Silva, R.J., Sawaris, L., Vaz-de-Mello, F.Z., da Silva, D.J., Peres, C.A., 2020. Habitat patch size and isolation drive the near-complete collapse of Amazonian dung beetle assemblages in a 30-year-old forest archipelago. Biodivers. Conserv 29 (7), 2419–2438. https://doi.org/10.1007/s10531-020-01982-y.
Tuneu-Corral, C., Puig-Montserrat, X., Riba-Bertolín, D., Russo, D., Rebelo, H., Cabeza, M., L´opez-Baucells, A., 2023. Pest suppression by bats and management strategies to favour it: A global review. Biol Rev 98 (5), 1564–1582. https://doi.org/10.1111/brv.12967.
Webb, C.O., Ackerly, D.D., McPeek, M.A., Donoghue, M.J., 2002. Phylogenies and community ecology. Annu Rev. Ecol. Evol. Syst. 33, 475–505. https://doi.org/ 10.1146/annurev.ecolsys.33.010802.150448.
Weiss, K.C., Ray, C.A., 2019. Unifying functional trait approaches to understand the assemblage of ecological communities: synthesizing taxonomic divides. Ecography 42 (12), 2012–2020. https://doi.org/10.1111/ecog.04387.
Wiens, J.J., Graham, C.H., 2005. Niche conservatism: integrating evolution, ecology, and conservation biology. Annu Rev. Ecol. Evol. Syst. 36 (1), 519–539. https:// doi.org/10.1146/annurev.ecolsys.36.102803.095431.
Wiens, J.J., Graham, C.H., Moen, D.S., Smith, S.A., Reeder, T.W., 2006. Evolutionary and ecological causes of the latitudinal diversity gradient in hylid frogs: treefrog trees unearth the roots of high tropical diversity. Am. Nat. 168, 579–596. https://doi.org/10.1086/507882.
Wilson, D.E., Mittermeier, R.A., 2019. Handbook of the Mammals of the World. Vol. 9. Bats. Lynx Edicions, Barcelona.
Yoh, N., Clarke, J.A., Lo´pez-Baucells, A., Mas, M., Bobrowiec, P.E.D., Rocha, R., Meyer, C.F.J., 2022. Edge effects and vertical stratification of aerial insectivorous bats across the interface of primary-secondary Amazonian rainforest. PLoS One 17 (9), e0274637. https://doi.org/10.1371/journal.pone.0274637.
Zarfl, C., Lumsdon, A.E., Berlekamp, J., Tydecks, L., Tockner, K., 2015. A global boom in hydropower dam construction. Aquat. Sci. 77, 161–170. https://doi.org/ 10.1007/s00027-014-0377-0.