BIOTROPICA 49(1): 117–129 2017
10.1111/btp.12348
When invasion may not be harmful: niche relations in a lizard assemblage Gisele R. Winck1,2, Marlon Almeida-Santos, Thiago A. Dorigo, Felipe B. Telles, and Carlos F. D. Rocha ~o Departamento de Ecologia, Instituto de Biologia Roberto de A. Gomes, Universidade do Estado do Rio de Janeiro – UERJ, Rua Sa ~ , CEP 20550-013, Rio de Janeiro, RJ, Brazil Francisco Xavier 524, PHLC, sala 220, Bairro Maracana
ABSTRACT Some studies have suggested that non-native species invasions may threaten local diversity by creating homogenized environments. However, many studies have been based on limited or anecdotal data, and/or have failed to consider the influence of habitat modification together with possible influences of non-native species on native ones. Hemidactylus mabouia (Squamata, Gekkonidae) likely invaded natural environments in Brazil hundreds of years ago. Yet, little is known about whether it affects native lizard fauna. We tested whether H. mabouia negatively influences native lizard species richness and abundance on a regional scale and locally through niche overlap. We analyzed species abundance and richness of nine lizard assemblages, in five of which H. mabouia occurred. We evaluated niche overlap of species in a lizard assemblage with high H. mabouia abundance through null models. Niche axes included spatial use, temporal activity and diet. Although species abundance did not differ among sites with and without the invasive species, the presence of H. mabouia seems constrained to the richer assemblages sampled. We observed significantly higher niche overlap in spatial (ɸobs = 0.63; ɸexp = 0.37; Pobs ≥ Pexp = 0.0002) and trophic axes (ɸobs = 0.46; ɸexp = 0.17; Pobs ≥ Pexp < 0.001), but not in activity. When we considered all axes (three-dimensional niche), there was no overlapping among the lizard species. Our findings did not support the hypothesis that this non-native species negatively influences other sympatric lizard species. Abstract in Portuguese is available with online material. Key words: biological invasions; Brazil; coastal environments; exotic species; Gekkonidae; Hemidactylus mabouia; invasive species; niche overlap.
UNDERSTANDING
THE ECOLOGICAL NICHE OF POPULATIONS AND SPE-
CIES IS ONE OF THE MAIN GOALS OF ECOLOGISTS WORLDWIDE.
Characterizing species’ niches is an important approach for evaluating the coexistence of sympatric species since the degree of niche overlap among species may indicate the extent of competition for shared resources (Pianka 1986). Theoretically, species coexist when the overlap of their realized niche is relaxed in at least one niche dimension (Hutchinson 1957, Chase & Leibold 2003). Evaluating the interactions among sympatric exotic and native species aids our understanding of the consequences of historical and current species introductions from one biogeographic realm to another, whether accidental or intentional. This kind of information is vital for determining conservation needs for native species, ecological communities, or invaded ecosystems. While there may be a link between exotic species invasions and the extinction of native species in ecological communities (e.g., Mooney & Cleland 2001, Seabloom et al. 2003), data supporting the hypothesis that invasion may be the primary cause of extinctions is still lacking (Gurevitch & Padilla 2004, Didham et al. 2005). A comprehensive discussion of the invasion process and its stages are beyond the scope of this study, and this discussion can be found elsewhere Received 19 March 2015; revision accepted 11 March 2016. 1
Corresponding author; e-mail:
[email protected]
2
Current address: Departamento de Ecologia, Instituto de Biologia, Universidade Federal do Rio de Janeiro - UFRJ, Av. Carlos Chagas Filho 373, sala A2-84, Cidade Universitaria, Ilha do Fund~ao CEP 21941-902, Rio de Janeiro, RJ, Brazil ª 2017 The Association for Tropical Biology and Conservation
(e.g., Richardson et al. 2000, Shea & Chesson 2002, Colautti & MacIsaac 2004, Davies 2005, Jeschke 2014). Important local conservation decisions are based upon the extent to which the invader interacts with other species. If we consider the arrival of a new organism as a stochastic process, its establishment should be inhibited by its interactions with native species, especially if both sides have similar environmental requirements (Tilman 2004). This environmental similarity among species should be a repelling factor to the invader species (Fargione et al. 2004, Tilman 2004). When environmental resources are available, it would be expected that the establishment of an invader and its relative abundance would be affected (positively or negatively; see Fargione et al. 2004) by interactions to native species. Therefore, only the presence of an invading species in the assemblage would not necessarily affect species coexistence. However, species interactions may be more intricate since a successful invasion may lead to competitive displacement after the invader becomes established (Mooney & Cleland 2001), and hybridization could also occur (Huxel 1999). In such cases, comparing niche attributes among phylogenetically proximate species or species from similar guilds should help determine the influence of the newly established species. The introduction of non-native species may threaten biodiversity (Rodrıguez 2001), and biotic homogenization is the main consequence of this process (Rahel 2000, Leprieur et al. 2008). In fact, this process may induce important changes on ecological and evolutionary levels such as modifications in food-web 117
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structure and hybridization that lead to a decline in species fitness (see Olden et al. 2004). Characteristics of the invading species and the invaded environment may influence the success of nonnative species in new ranges (e.g., Lodge 1993, Tsutsui et al. 2000, Cully et al. 2003, Lake & Leishman 2004, Zheng et al. 2015). The gecko Hemidactylus mabouia (Squamata, Gekkonidae) is the most widely distributed non-indigenous vertebrate species in Brazil (Rocha et al. 2011). Hemidactylus mabouia is a well-established species that is able to colonize new areas (Vanzolini et al. 1980, Meshaka 2000, Rocha et al. 2011), and it has been observed near urban environments in several Brazilian provinces (Rocha et al. 2011). It is known to interact with local communities in parasitehost (Anjos and Rocha 2005, 2008a,b), microhabitat (Rocha et al. 2002) and prey–predator relationships (e.g., Rocha & Vrcibradic 1998, Bernarde et al. 2000). Besides the biological characteristics of H. mabouia that favor its insertion into natural assemblages (e.g., fast reproduction and microhabitat use plasticity) (Bonfiglio et al. 2006, Anjos & Rocha 2008a, Rocha et al. 2011), the increasing number of records supports the invasion capacity of this species (Anjos & Rocha 2008b, Rocha et al. 2011). The invasion of H. mabouia in Brazilian natural environments probably dates from the country’s colonization period (started in the 1500s), and this gecko is currently found mainly in sandy coastal plains of the Atlantic rain forest biome (Rocha et al. 2011). We believe that this area is suitable for testing whether the nonindigenous species may drive competitive displacement of natives due to three main reasons: (1) the vegetation structure facilitates the detection of lizards (lower complexity than Atlantic rain forest strictu sensu); (2) lizard assemblages in sandy coastal environments typically have low richness (up to nine species); and (3) H. mabouia is not present in all sandy coastal environment remnants, which allows comparisons of abundance and richness among study sites. We evaluated the probable influence of H. mabouia on native lizard species by comparing three niche axes (time of activity, microhabitat use and prey type) from a coastal lizard assemblage and examined how the invader may influence the local species assemblage. We hypothesized that the invader would negatively impact the local assemblage if the three-dimensional niche of this species is identical to that of any native species. We also compared species abundances and richness among nine assemblages to assess the potential influence on these metrics by H. mabouia.
METHODS There is a lack of consensus in invasion ecology terminology. In the past decade, some studies have tried to provide an operational terminology framework (e.g., Richardson et al. 2000, Colautti & MacIsaac 2004). We assigned ‘invasive’ as a synonym for ‘nonindigenous’ species (NIS), since we were not able to properly identify in which stage of invasion the species could be placed (Colautti & MacIsaac 2004). Also, this conceptual approach does not imply any a priori effects of the NIS in the colonized environments, as used by the International Union for Conservation of Nature (i.e., an ‘invasive’ necessarily causes damages).
STUDY AREA.—The study took place in areas within the Atlantic rain forest domain, which includes a sandy coastal plain ecosystem (restinga habitat). We recorded data from nine lizard assemblages in the state of Rio de Janeiro to evaluate differences in abundance and richness among the following locations (datum WGS84, Fig. S1): Praia do Sul (PS; 23°100 37.20″S, 44°170 60.00″ W); Lopes Mendes (LM; 23°100 1.20″S, 44° 70 55.20″W); Grumari (GR; 23° 20 49.20″S, 43°310 30.00″W) Marica (MA; 22°570 43.20″S, 42°510 36.00″W); Jacarepia (JA; 22°550 58.80″S, 42°270 3.60″W); Per o (PE; 22°510 57.60″S, 41°580 58.80″W); Itapebussus (IT; 22°290 13.20″S, 41°530 38.40″W); Jurubatiba (JU; 22°150 28.80″S, 41°360 54.00″W); and Grussaı (GR). This ecosystem has predominantly herbaceous and shrubby vegetation and sandy soil, and it is subject to high salinity (Suguio & Tessler 1984). In general, different vegetation zones transition into others with increasing distance from the sea. We performed the second part of the study (niche overlap analyses) only at Grussaı, S~ao Jo~ao da Barra municipality, state of Rio de Janeiro (21°430 48.00″S, 41° 10 48.00″ W; WGS84), due to logistical constraints. According to the K€ oppen–Geiger climate classification (Peel et al. 2007), the climate in the area corresponds to Af (tropical sub-humid). The average annual rainfall is 800–1200 mm, and the highest rates of precipitation were recorded during the warmer months (December to March). The study site comprises 302 ha, and the vegetation is organized into four zones (or mesohabitats), which spread out parallel to the sea. The first zone extends from the high tide line to approximately 300 m inland from the beach, and the vegetation is mainly herbaceous. The second zone, which is formed by low bushes, starts at approximately 300 m from the seashore and extends for approximately 280 m. The third is composed of higher bushes (up to 2 m high) and extends from 580 to 980 m from the shore. We considered the latter two zones as one due to structural similarities and the lack of variation in lizard abundance and richness (G. R. Winck, pers. obs.). The fourth zone is composed of a continuous forest, and extends to approximately 3000 m from the beach toward the interior. Tree height rarely exceeds 8 m and the understory is dominated by bromeliads. We sampled the first 650 m of this zone to reduce possible differences in results due to area size. Each sampling zone was approximately 1200 m wide. SAMPLING AMONG SITES.—We evaluated the abundance and richness of lizard species among all areas (N = 9) with timeconstrained transects (1 h) at least 100 m apart in each vegetation zone (Fig. S2A). Each transect was performed by two researchers walking at the same pace at least 50 m apart. This approach prevented individual lizards from being recorded twice and allowed us to sample a larger area. We gathered data at each site for 2 d each during wet (October–April) and dry seasons (May–September), from 2008 to 2011, for a total of 756 sampling hours and 1422 sightings. This data acquisition was performed at all sites (including Grussaı), and the data were used only to test for differences in abundance and richness among areas, with and without H. mabouia.
Niche Relations Among Lizards
SAMPLING AT GRUSSAI.—We sampled from December 2010 to January 2011 in Grussaı to evaluate species niche overlap analyses. We performed transects in all vegetation zones between 0600 and 2000 h. Daily activity refers to the number of individuals recorded per species per hour; data were gathered by seven researchers performing transects simultaneously in different places. We performed six transects of 15 min per hour over a 10-d period each month (Ntotal = 420 h of sampling). Transects were at least 100 m apart to avoid multiple records of the same individual (Fig. S2B). We used air pressure rifles to randomly collect lizards inside the study area for stomach content analysis. Lizard taxonomy followed Pyron et al. (2013). We recorded the following data: species, vegetation zone, type of substrate (soil: sand or leaf litter; vertical: bromeliads, cactus, stem, tree trunk or debris), position in vegetation (edge or inside bushes, and edge or interior of the forest), and degree of exposure to sunlight (sun, filtered sunlight, or shade). At the beginning of each counting period, we measured in all four habitat types the air temperature (in °C) in the shade (1 m above the ground) and leaf litter (secondary layer) and sand temperature (5 cm deep). We analyzed diet with a stereoscopic microscope, and we identified all but the following consumed items to the taxonomic level of order: ants (Formicidae), homopterans, gastropods, chilopods and diplopods (identified to class). Unidentified items (due to digestion) were considered as arthropod remains. We also measured prey size (length and width; accuracy of 0.01 mm). We estimated prey volume (in mm³) for each item using the formula of the ovoidspheroid: V = (4/3)p(L/2)(w/2)2, where ‘L’ corresponds to length and ‘W’ to width (Dunham 1983). DATA ANALYSES.—We tested for possible differences in species abundance and richness among the nine assemblages with a permutational analysis of variance (PERMANOVA) (Anderson 2001, McArdle & Anderson 2001) by group (with and without H. mabouia; 10,000 iterations). We selected this statistical analysis due to the low number of sampling sites. We used the Jaccard Index as the similarity measure in the richness presence/absence matrix and Pearson’s correlation in the abundance analysis. We tested the detectability and abundance estimate for each species following Royle and Nichols (2003) by comparing the observed distributions to null models and using detection radius (i.e., distance between lizard and observer) as a covariate. We performed these analyses using the ‘occuRN’ function of the ‘unmarked’ package (Fiske et al. 2015) in the software R (R-project.org). In Grussaı, we tested whether temperature affected the number of individuals of each species with a permutational analysis of variance (PERMANOVA; 10,000 iterations). We also tested whether species differed in habitat use with a PERMANOVA (10,000 iterations). We chose PERMANOVA because our data did not meet the assumption of normal distribution for ANOVA. We used multiple linear regressions to test the thermal relationships between the different lizard species and the environmental heat sources. Therefore, we tested whether the number of individuals of each species was related to the temperature of each associated substrate (leaf litter or sand) and air temperature. Data
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on temperature included the measured temperatures of the microhabitat used by each individual throughout the day. Results from the multiple linear regressions are presented as two R² values for clarity. Analyses were performed with the software MULTIV 2.4b2 (Pillar 2006) and Statistica 7. We determined the number, volume (in mm³), and frequency of occurrence of each prey type consumed by each lizard. We estimated the relative importance index (Ix) for each prey category (Gadsden & Palacios-Orona 1997). This index represents the arithmetic mean of the proportions of the number, volume, and frequency of occurrence of each prey item. We estimated the species niche breadth through Simpson’s dominance index (D) (Krebs 1989), which estimates the probability of any two items being different when taken at random from an infinitely large community (Magurran 2004). Body size is an important metric to understand the diversity of assemblages and species diet. We present a compilation of data based on available literature for body size of the species comprising the lizard assemblage from Grussaı (Tropidurus torquatus: Kiefer et al. 2008, Fialho et al. 2000, Hemidactylus mabouia: Anjos & Rocha 2008a, Gymnodactylus darwinii: Carvalho & Araujo 2007, Ameiva ameiva: Rocha 2008, Cnemidophorus littoralis: Menezes et al. 2006, Tupinambis merianae: Winck et al. 2011, Mabuya agilis: Vrcibradic & Rocha 2002, M. macrorhyncha: Vrcibradic & Rocha 2005). To examine niche overlap (temporal, spatial, and trophic) among species of the assemblage, we calculated the index introduced by MacArthur and Levins (1967) modified by Pianka (1973): pffiffiffiffiffiffiffiffiffiffiffiffi /jk ¼ Rpij pik = Rpij pik . In this method, ‘/jk’ is the niche overlap for species ‘j’ and ‘k’ (ranging from zero, where there is no overlap, to one, where the niche overlap is total), and ‘pi’ is the proportion of the resource in class ‘i’ used by the species (‘j’ and ‘k’). Variance in the niche overlap index is represented by ‘r’. We tested whether the probability of overlap was the same if the data were randomly distributed by a null model (10,000 iterations) (Pianka 1986) using the randomization algorithm three (RA3). It reorganizes the observed utilizations of species (it is not replaced by a uniform distribution, option ‘Retained’), which effectively retains the niche breadth of each species (resource state ‘Equiprobable’) (Gotelli & Graves 1996). The resources that were not used in nature by a particular species (zeroes) are randomized (option ‘Reshuffled’), under the assumption that the species of the community could use the resource in the absence of interspecific competition (Gotelli & Graves 1996). As other studies have shown (Winemiller & Pianka 1990), the algorithm RA3 detects patterns of non-random niche overlap, and it tends to produce pseudo-communities more similar to the observed community (Pianka 1986). For temporal analysis, we used a matrix with the relative frequency of species (the proportion of individuals of each species at each hour of day). The spatial data matrix corresponded to the frequency of use data for the different microhabitat variables, comprised of 15 variables and seven species. We determined diet overlap with a matrix containing the relative importance index of each category of prey per species (Gainsbury & Colli 2003). In this case (diet), the RA3 algorithm permits the assumption that prey categories not found in gut content analysis could still be resources for the lizard species. This is
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richness among sites (no groups assigned) (PERMANOVA; SS = 3.42; P = 0.9). However, there was a clear difference in richness between assemblages with versus without H. mabouia (PERMANOVA; SS = 3.42, P = 0.01) (Table 1). Estimates of abundance and detection for each species are given in Table 1. At Grussaı, we recorded eight lizard species during observations of daily activity and meso/microhabitats used: Tropidurus torquatus (Wied, 1820) (Tropiduridae) (N = 294), Hemidactylus mabouia (Moreau de Jonnes, 1818) (Gekkonidae) (N = 35), Gymnodactylus darwinii (Phyllodactylidae) (N = 2), Ameiva ameiva (Linnaeus, 1758) (Teiidae) (N = 30), Cnemidophorus littoralis Rocha, Ara ujo, Vrcibradic and Costa, 2000 (Teiidae) (N = 28), Tupinambis merianae (Teiidae) (N = 9), Mabuya agilis (Raddi, 1823) (Scincidae) (N = 61), and Mabuya macrorhyncha Hoge, 1947 (Scincidae) (N = 29). G. darwinii was not included in niche analyses due to its low sampling. Body size (snout-vent length) varies among species: T. torquatus (females: 57 3.4 mm; males: 72 9.45 mm); H. mabouia (females: 56.6 5.2 mm; males: 56.7 5 mm); G. darwinii (38.21 12.76 mm); A. ameiva (females: 96,5 23,1 mm; males: 124,2 17,8 mm); C. littoralis (females: 60.7 3.7 mm; males: 64.4 7.1 mm); T. merianae (females: 377 23 mm; males: 416 3.5 mm); M. agilis (females: 70.2 4.5 mm; males: 68.5 3.1 mm); M. macrorhyncha (females and males: 67.1 6.8 mm).
important for our analysis since we had three lizard species with lower numbers of individuals (N < 10): C. littoralis (a threatened species), A. ameiva, and M. macrorhyncha. Following the methodology used by Gainsbury and Colli (2003), we excluded categories that had a rate of importance less than 5 percent for all species in the assemblage from the analysis. All niche overlap analyses were performed in EcoSim (Gotelli & Entsminger 2004). We also calculated the unified index for niche overlap (Geange et al. 2011), which estimates the pairwise niche overlap regarding all niche axes: NOij ¼ ð1=T ÞRTt¼1 NOi;j;t ; where ‘T ’ is the number of niche axes and NOi,j,t is the result of the pairwise niche overlap estimated by Pianka’s formulae. NOij is zero when both distributions are completely non-overlapping and one when they match precisely. We generated a distance matrix containing the unified pairwise niche overlap using dij = 1NOij to perform an ordination (non-metric multidimensional scaling, NMDS) (Geange et al. 2011). We used this approach to reduce the dimensionality of the data and graphically displayed the niche relations among all species in two dimensions: (1) all axes, excluding G. darwinii and T. merianae due to the lack of consumed prey data (trophic niche); (2) two axes, temporal, and spatial, including T. merianae.
RESULTS SPECIES ABUNDANCE AND RICHNESS.—Species abundance did not differ among sites with and without H. mabouia (PERMANOVA; SS = 5.88, P = 0.23). There was also no significant difference in
TEMPORAL ACTIVITY.—Tropidurus torquatus, M. agilis, and M. macrorhyncha were less active when environmental temperatures
TABLE 1. Species abundances among sampled sites. Data were collected in sandy coastal environments in Rio de Janeiro state, Brazil. PS, Praia do Sul; LM, Lopes Mendes; GR, Grumari; MA, Marica; JA, Jacarepia; PE, Pero ; JU, Jurubatiba; IT, Itapebussus; GS, Grussaı; EstAb, estimated mean abundance for each species by sample (transection); Det, estimated mean detection by species. Sites Taxa
PS
LM
GR
MA
JA
PE
JU
IT
GS
Total
EstAb (P)
Det (P)
0
0
51
55
47
36
0
0
0
189
2.6 (0.002)
0.67 (0.006)
0
33
49
57
61
3
166
19
107
495
2.52 (0.01)
0.94 (0.05)
0
0
17
3
0
1
1
0
23
45
0.94 (0.02)
0.46 (0.05)
2
1
3
0
0
0
1
0
3
10
0.65 (
