Home range and movement patterns of parrotfish in subtropical reefs of the Southwestern Atlantic

Jora O. Batista^1,2, Linda Eggertsen^3,4 , Carlos W. Hackradt², César A. M. M. Cordeiro⁵, Moysés C. Barbosa⁴, Marcos B. Lucena⁴ and Carlos E. L. Ferreira⁴

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Associate Editor: Luiz Osmar

Section Editor: Luiz Osmar

Editor-in-chief: José Birindelli

Abstract​

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Introduction​

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The study of animal home range can be defined as the spatial representation of the area needed for survival and reproduction, and is a classical issue in terrestrial (Seaman, Powell 1996; Bellis et al., 2004; Börger et al., 2008) and marine ecology (Kramer, Chapman, 1999). On reefs, movements of a given fish species are influenced by environmental drivers such as topography, resource availability, habitat quality and general seascape features (Pittman et al., 2007; van Lier et al., 2018; Streit et al., 2021). Interactions between biotic processes (e.g., predation) and habitat characteristics further shape fish behavior and distribution; for example, high reef rugosity is attractive for a majority of species of reef fish while most species will avoid crossing larger areas of bare sand, especially where predators are abundant (Chateau, Wantiez, 2009). At the individual level, size and maturity determine social status and intensity of intra- and inter-specific interactions, thus further shaping spatial movement patterns (Bruggemann et al., 1994; Rooij et al., 1996; Feitosa et al., 2021). The combined characteristics of habitats, species and individuals thus act in conjunction as drivers, governing home range and patterns of movement behavior of fishes (Mumby, Wabnitz, 2002; Lowe et al., 2003).

Parrotfish (Labridae: Scarinae) is an iconic functional group on reef ecosystems, mediating coral-algae interactions and taking part in nutrient cycling, sediment transport and bioerosion (Bonaldo et al., 2014; Morgan, Kench, 2016). Size of territory and home range in parrotfish have been suggested to be influenced by a range of factors such as social structure, fish size, reefscape characteristics (complexity, reef area, coastal morphology), local population density, interspecific competition and local benthic productivity, (Rooij et al., 1996; Howard et al., 2013; Catano et al., 2015; Carlson et al., 2017; Davis et al., 2017). Home range also depends on life stage of parrotfishes (Howard et al., 2013). Most parrotfish species are protogynous hermaphrodites, with an initial phase (IP) as females and then changing sex to terminal phase males (TP) with a distinct coloration (Hoey, Bonaldo, 2018). It is common for terminal phase (TP) individuals of the majority of species to maintain a harem of initial phase (IP) fish, while defending their territory from other TP males (Rooij et al., 1996; Howard et al., 2013). For the species where this behavior occurs, territories are hypothesized to be defended to protect food resources and secure mating success of the TP males (Bruggemann et al., 1994; Rooij et al., 1996). However, different social structures may occur, also in haremic species, such as dominant IP fish defending territories (Buckman, Ogden, 1973), or smaller males managing to fertilize eggs (i.e., sperm competition), resulting in that it is not always advantageous for large females to transform into TP males (Muñoz, Warner, 2003, 2004). As food resources (e.g., patches of high qualityhigh-quality turf or endolithic algae) are not evenly distributed in reef seascapes, maintaining a territory with high-quality resources of food to maximize energy outcomes of foraging is an expected strategy (Bruggemann et al., 1994).

Much of the parrotfish conservation initiatives which have spread worldwide sustain that, based on their important functional roles as herbivores, the higher their biomass, the better to improve reef resilience (Nyström, Folke, 2001; Adam et al., 2011). As different functional herbivores, parrotfishes are roving microphages (Clements et al., 2016), targeting cyanobacteria and other protein-rich autotrophic epilithic or endolithic microorganisms, while spending the majority of their daily activity feeding, often foraging in groups of complex social structures (Bruggemann et al., 1994; Afonso et al., 2008a). Due to their relatively small home ranges and high site fidelity, Marine Protected Areas (MPAs) have been suggested as an effective tool for parrotfish conservation and stock management (Afonso et al., 2008b; La Mesa et al., 2012; Howard et al., 2013; Lippi et al., 2022). The available knowledge on patterns of parrotfish movement behavior is mostly based on tropical species (e.g., Howard et al., 2013; Davis et al., 2017; Lippi et al., 2022), with a few cases from temperate reef systems (e.g., Afonso et al., 2008b). Despite their ecological uniqueness, functional roles, as well as importance as fishery targets, there is still little information on home ranges of parrotfishes, with marginal subtropical reef systems yet to be explored (Cordeiro et al., 2016). This information is critical to support the optimal design of MPAs in order to be effective and meet management objectives (Sale et al., 2005; Chateau, Wantiez, 2009; Di Franco et al., 2018).

The southwestern Atlantic reefs are characterized by low coral cover comparative to Caribbean reefs, and with high dominance of turfs and macroalgae (Aued et al., 2018). Ten species of parrotfish occur in the southwestern Atlantic, of which the genus Sparisoma is the most diverse, with the majority of species being endemic (Moura et al., 2001). They are functionally important mainly as scrapers, with only two species being classified as excavators (Scarus trispinosus and Sparisoma amplum) (Ferreira, Gonçalves, 2006; Francini-Filho et al., 2010). The larger species are important targets for the artisanal and recreational fishery in several regions in Brazil (Roos et al., 2016; Queiroz-Véras et al., 2025). Lack of effective management has unfortunately led to substantially decreases in population of the larger species during the last decades with unknown consequences for reef ecosystem function (Bender et al., 2014; Roos et al., 2020; Eggertsen et al., 2024).

The gray parrotfish Sparisoma axillare and the Agassiz’s parrotfish Sparisoma frondosum are two of the larger and most abundant endemic species, both with a broad distribution from subtropical to tropical reefs (00°25’S to 27°20’S), including the oceanic islands. They occur on both biogenic and rocky reefs with a depth distribution of about 1 to 40 m (Pinheiro et al., 2018). Both species are listed as Vulnerable (VU) by the Brazilian official list of threatened species in the decree MMA N^o 445/2014 (MMA, 2014). While these two species are threatened by intense fishing and protected only by a few no-take zones (2.5% of the Brazilian EEZ; Magris et al., 2020), almost nothing is known about their movement patterns and home range. However, a recent study confirmed high site fidelity for S. axillare on tropical reefs at the Northeastern Brazilian coast (Lippi et al., 2022). Home range sizes may however differ between tropical and subtropical environments due to different metabolic/energetic demands in different temperature regimes (Scott et al., 2017, 2019) and differences in resource distribution.

Despite the two species being similar in size and occurring in the same habitat, behavioral differences exist that may result in distinct home ranges. Terminal phase S. axillare displays territorial behavior such as patrolling of territories while no such behavior has been recorded for S. frondosum TP males (Bonaldo et al., 2006). Most S. axillare TP males are larger than the IP individuals, while this relationship is not so evident for S. frondosum where TPs are similar, or even smaller than IP fish (CELF, unpub. data).

We evaluated short-term home range and movement patterns for S. axillare and S. frondosum using active acoustic telemetry in subtropical rocky reefs on the Brazilian southeastern coast. The primary aim of the study was to establish home range size of the two species, to provide information on spatial patterns linked to social structure and size of parrotfish, and ultimately, giving support as baseline information for parrotfish management and conservation initiatives. We hypothesized that: 1) home range would not differ between the two species; 2) home range would be influenced by seascape variables (depth and coastal morphology) and fish size; and, 3) size of territory would differ between TP and IP individuals of S. axillare but not between TP and IP individuals of S. frondosum due to a more territorial behavior by the former.

Material and methods

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Study area. This study was performed on subtropical reefs in Arraial do Cabo (22°57’S 41°01’W), on the southeastern Brazilian coast, in Rio de Janeiro State. The region consists of an isthmus and four islands (Fig. 1), surrounded by rocky shores and sand beaches. The region was declared a marine extractive reserve in 1997, where only traditional fishers are allowed to exploit marine resources. However, no-take areas are absent and general enforcement is limited. Small-scale upwelling processes often occur in the region because of the prevailing winds (north-easterlies) and coastal morphology, where upwelling water is characterized by temperatures below 20°C (originating from the South Atlantic Central Water mass). It is worth noting that there is no significant freshwater input (i.e., rivers) within a 40 km radius. The region is located in a micro tidal regime with semi-diurnal tides fluctuating between 1 and 0.06–0.025 m in maximum tides (the Brazilian Navy, Castro et al., 2018). The benthic community is dominated by the epilithic algal matrix but also includes a variety of sponges, macroalgae, zoanthids, gorgonians, hydrocorals and some massive stony corals (Ferreira et al., 2001; Cordeiro et al., 2014). The east side where the study was performed is protected from the upwelling by coastal morphology and possesses subtropical characteristics with water temperatures averaging 22–24°C (Ferreira et al., 1998). Narrow rocky reefs line the islands, with sand covering the area between the islands and the isthmus. Movement patterns of S. axillare and S. frondosum were studied at nine sites on the east side of the peninsula (e.g., where subtropical conditions prevail) (Fig. 1). Parrotfishes occur only on the rocky reefs on the east side (Cordeiro et al., 2016), and we therefore expected the fish to have their entire home ranges within the study area. The nine sites were chosen to maximize spatial cover OF the study area with similar depth, benthic composition and extension of the reef habitat (Cordeiro et al., 2014), and where the two study species are abundant (Cordeiro et al., 2016). The identification of the species S. axillare and S. frondosum was based on the descriptions by Moura et al. (2001). Voucher specimens of these species are deposited in the collections mentioned in Moura et al. (2001).

FIGURE 1| Map of the study area, with the capture and tracking sites of Sparisoma axillare and Sparisoma frondosum with active telemetry. 1 = Anequim (coastal complexity – cc = 0.30), 2 = Abobrinha (cc = 0.23), 3 = Pedra Vermelha (cc = 0.43), 4 = Maramutá (cc = 0.41), 5 = Boqueirão (cc = 0.04), 6 = Saco de gato (cc = 0.42), 7 = Praia do Forno (cc = 0.10), 8 = Cardeiros (cc = 0.29), and 9 = Ilha dos Porcos (cc = 0.12). The blue box in the small map (lower right) indicates the limits of the Arraial do Cabo Marine Extractive Reserve (RESEX).

Range test. We performed a detection range test of the acoustic receivers to evaluate signal detection at different gain settings (6–48). At gain 6, detections were obtained up to approximately 150 m from the transmitter, whereas at gains 24 and 48, the maximum detection distance increased to about 220 m. At gains 6 and 24, the highest number of detections and strongest signal strengths occurred at zero distance from the transmitter. At gain 48, the number of detections decreased with increasing distance from the transmitter (Fig. S1), accompanied by a less pronounced decline in signal strength compared to the other gain settings (Fig. S1). These results indicate that the highest detection rates and strongest signal strengths at gains 6 and 24 are achieved at shorter distances from the transmitter, whereas monitoring at gain 48 can be conducted over greater distances. To minimize false detections, signal intensities below 60 dB were excluded from the analyses, as such low values are more likely to represent noise rather than true detections.

Tagging and tracking. Field work was realized in March, November and December 2018, and in August in 2019. Movement patterns of fish were studied using active telemetry tracking. Active tracking allows for higher spatial resolution compared to passive monitoring, and also the possibility to search for fish over large spatial areas, although it is more labour-intense. Fish were captured at night with a net bag and SCUBA, when fish were sleeping. Captured fish were brought to the boat, where total and fork length were measured. Fish were placed in a surgery tray with seawater (Fig. S2). A continuous acoustic tag (V9, 1000 milliseconds) was surgically implanted in the peritoneal cavity through a small incision between the pelvic and anal fins. The area of surgery and the tag was disinfected and the incision was closed with cyanoacrylate adhesive, since the muscle tissue of parrotfish is very soft and sutures may rupture their tissue. After the surgery, fish were placed in a large plastic container with seawater to recover, and then released by a diver at the same spot where it was captured. At each capture site, six individuals were tagged at the same occasion. In total, 39 S. axillare and 25 S. frondosum were captured, tagged and released (Tab. 1). No mortality was observed related to the surgery.

TABLE 1 | Number of tagged fish (N), phase (IP = Initial phase, TP = Terminal phase), total length (TL), mean home range (± SE) and mean trajectory length (± SE) for each species and phase.

Species	N	Phase	Mean TL (mm)	Mean KUD 95% (m2)	Trajectory length (m)
S. axillare	25	IP	389.0 ± 7.5	50600 ± 8900	455 ± 326
S. axillare	14	TP	438.6 ± 13.6	56400 ± 16500	452 ± 287
S. frondosum	18	IP	363.4± 9.0	34000 ± 11100	417 ± 271
S. frondosum	7	TP	372 ± 15.9	62000 ± 45900	526 ± 324

Monitoring was performed from a 4.8 m inflatable boat, using a unidirectional or a multidirectional hydrophone (VEMCO VH110 and VH165, respectively) and an acoustic receiver (VEMCO 100VR). No difference in detection rate was found between the two different types of hydrophones. The tags emit pings on six frequencies (60, 63, 75, 78, 81 and 84 kHz), resulting in a maximum number of six fish that can be tagged at one site at the same time. Each individual fish was tracked for 10 min per day during the lifetime of the tag (on average 10 days ± 0.23 SE), with tracking periods evenly distributed during mornings and afternoons to account for spatial variability due to diurnal patterns. The first active tracking started 24 h after the tagging to allow fish to recover. Due to the micro-tidal nature of tides in the study area, tides were not considered to influence movement patterns of the fish. The hydrophones were handheld from the boat and the tracking was conducted by searching for tagged fish while slowly following the reef at the tagging sites. When detected, an effort was made to follow the fish as close as possible (e.g., stay within the strongest signal possible > 90dB; < 5 m distance). All detections lower than 60dB (> 100 m distance from the hydrophone indicated by the range test performed pre-tagging) were excluded from the data analyses.

Coastal complexity was used as a proxy for reef area because local rocky reefs are narrow stripes following the coastal contour, thus more complex contours are likely to bear larger reef areas. This variable was then calculated similarly to the chain-and-tape method widely applied for substrate complexity (English et al., 1997). In this case, the linear coastal contour (C) of each site was measured in Google Earth Pro software (Google Earth Pro 9.159.0.0 WebAssembly), as well as the straight distance between natural limits of each site (L). Thus, coastal complexity was calculated as 1 – (L/C), with values ranging from 0 to 1, where values close to 1 value indicate larger complexity (see Fig 1).

Statistical analysis. An occurrence index was calculated for all tagged individuals using the number of days when the individual was detected divided by the total number of days the individual was monitored. Differences in fish total length between the two species and between IP and TP individuals were tested with one-way ANOVA. All independent variables were tested for normality and homoscedasticity before analysis and, in case, those criteria were not met (KUD 50 and KUD 95), variables were log- (base 10) or square root-transformed to achieve normality. Functions shapiro.test and levene.test from the stats and car packages were applied for testing normality and homoscedasticity, consequently.

The acoustic telemetry data was used to determine home range using two methods: minimum convex polygons (MCPs) and kernel utilization distributions (KUD). The MCP determines home range based on the peripheral detections of each individual, while kernel distribution calculates relative probability of where the fish will be found during a given time (Worton, 1987). Home range was determined as KUD 95% and core use area as KUD 50% (Afonso et al., 2008a), calculated using least-square cross validation (LSCV) smoothing parameter (Seaman, Powell, 1996). The MCPs and KUDs were calculated in the adehabitatHR package (Calenge, 2006) in R. When MCPs or KUDs overlapped land, area on land was manually excluded from home ranges in QGiS.

To test if home range area was dependent on size or life phase of fish (IP or TP), home range area was modeled separately for each species using linear mixed-effect models (LMMs) with the lme4 package (Bates et al., 2015) in R (v. 4.0.0). We tested a priori differences in species size using ANOVA applied to total length and using species, life phase and the interaction of these as fixed factors. Species were analyzed separately because of ecological and behavioral characteristics of species, as well as previously known size differences (tested here) that could influence measured variables. Fish size, life phase and coastal contour were applied as fixed factors to the models, and site was included as a random factor in the models to account for any non-measured or non-replicable habitat variability. Linear models were also applied to KUD 95 and 50 relationships with fish size, life phase and coastal contour using the function “lmer” from the “lme4” package (Bates et al., 2015). The KUDs were log transformed, and MCP was square-root transformed to comply with model premises and normality was tested with the Shapiro-Wilks test. All models were initially built including a random term, the fixed effects of the independent variables and the interaction between size and life phase. Whenever interactions were non-significant, final models were rerun without the interaction term. P-values from models output were extracted using the “Anova” function from the “car” package (Fox, Weisberg, 2019).

Relative contribution of independent variables was extracted by partitioning the R2 values of each model using the function “partR2” from the “partR2” package (Stoffel et al., 2021), and the random variance relative contribution was extracted from the total residual variance using the functions “VarCorr” and “get_variance_residual” from packages “lme4” and “insight” (Lüdecke et al., 2019), respectively.

Species movement was extrapolated from the total length of individual trajectories during the 10 min monitoring, calculated using the function “as.ltraj” from “adehabitatLT” package (Calenge, 2006). Trajectories were calculated as type II, in which sequential detections are defined as points of the trajectories and the total length is based on the sum of the geographical distances between each sequential detection for each individual per surveyed day. Original coordinates were projected to the metric system (i.e., UTM, EPSG 32724) using the “spTransform” function from the “rgdal” package (Bivand et al., 2022).

We applied generalized linear models (GLM) using Gamma distribution (log link) to model association of daily fish trajectory with fish size, life phase stage and coastal complexity. Models did not converge using sites as a random factor, thus site was not included in the final models. To account for any seasonal variation in daily trajectories, we included a ‘time of the year’ (combination of year and month) random factor in the models. We applied the glmmTMB function from the homonymous package (Brooks et al., 2017).

To investigate if any territorial behavior could be detected, overlap in home range core use (KUD 50) between individual fish was calculated in QGiS (v. 3.4). For S. axillare, this resulted in 10 TP, 15 IP and 26 fish for the IP/TP overlap. For S. frondosum, four TP, 10 IP and 11 IP/TP were tagged at the same occasion and site and used in the overlap calculations.

Results​

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Each of the 64 fish were monitored 2–11 days (6.4 ± 0.35 SE), which resulted in a total of 3860 min (Tab. S3). Total length of fish varied between 374.6 and 405.6 mm for S. axillare and 369.6 and 409.7 mm for S. frondosum (Tabs. 1, S4). Significant size differences (TL) between S. axillare and S. frondosum were observed (ANOVA; F-value 1, 60 = 12.1, p < 0.01), and also between phases (IPs and TPs) (F-value 1, 60 = 11.2, p < 0.01) which were more evident for S. axillare (Tab. 1).

Movement and home range size. Home range for the tagged fish varied with the MCPs ranging between 2,100 and 83,400 m² for S. axillare averaging 30,600 m² ± 3,000 SE and between 1,800 and 77,500 m² for S. frondosum,averaging 31,200 m² ± 4,000 SE(Tab. 1, Figs. S4, S5, S6). Home ranges estimated with kernel distribution (KUD 95%) ranged between 500 and 20,2500 m² for S. axillare and between 1,200 and 358,800 m² for S. frondosum (Tab. 1; Fig. 2; Figs. S4, S5, S6) with an average of 50,500 m² ± 10,000 and 46,100 m² ± 10,000 for the former and latter, respectively. The largest home ranges were recorded for terminal phase (TP) individuals of both species.

FIGURE 2| Examples of home ranges (KUD 95) and core areas (KUD 50) for randomly selected individuals of tagged Sparisoma axillare (blue) and S. frondosum (green) at each site. Darker shades represent core areas for each species, and lighter shades home ranges. The codes represent the individual code for each fish, and the number in the right corner the site number. Note that the scale is not the same for all sites.

The core areas followed the same patterns, with TP individuals using the largest recorded areas, and the IPs the smallest (Tab. S3; Fig. 2), with an average of 10500 m² ± 2000 SE for S. axillare and 10,400 m² ± 3,000 SE for S. frondosum. The largest MCPs for S. axillare were located at site 1, 2, 3 and 4, while the largest home range and core area were located at site 1, 2 and 5 (Fig. 3). Only one individual was tagged at site 7. Regarding S. frondosum, the largest MCPs, home ranges and core areas were all detected at site 3 (Fig. 3). The longest movement detected for S. axillare from the caption site was 715 m (A 623), and for S. frondosum 749 m (F 666). All movements were always recorded along the rocky reef (Fig. 4).

FIGURE 3| Comparative home range size and core use of Sparisoma axillare (A-C) and S. frondosum (D-F). Horizontal lines represent median, boxes 25 and 75% percentiles and the dots are outliers. The numbers on the x-axis represent each site where fish was captured as in Fig. 1.

FIGURE 4| Examples of movement patterns of Sparisoma axillare (blue) and S. frondosum (green) at the studied sites (1–9).

MCP values for both species were not related to any of the tested variables (Tab. 2). Home range of S. axillare (KUD 95) was positively associated with individual fish size (Tab. 2; Fig. 5) and size explained 7.1% of the variance of the linear model (total variance explained by the model was 15.9%). Between-site variation contributed 50.1% of total variation for S. axillare data indicating considerable variability in samples among sites. Contrastingly, home range of S. frondosum (KUD 95) was positively associated with coastal complexity (Tab. 2), which explained 18.1% of total variance of the linear model (total variance explained by the model 20.1%). Random factors (fish size and life phase) showed a low contribution to the total variance (3.1%) of the S. frondosum model. Regarding daily linear distances, S. axillare tended to rove comparatively larger distances at sites with more complex coasts irrespective of individual size or life phase (table x). Daily movements of S. frondosum were not related to any of the tested variables (p > 0.05). Variance associated with the random factor time of the year was < 0.01% for both S. axillare and S. frondosum, indicating an absence of temporal variation during the sampling period.

TABLE 2 | Summary of linear mixed effect models applied for Sparisoma axillare and Sparisoma frondosum probabilistic home range (KUD 95), probabilistic core use (KUD 50) and maximum home range (MCP) variables using acoustic telemetry in Arraial do Cabo (RJ), Brazil. Log – logarithmic transformation of variables (natural base), sqrt – square root transformation of variables, CI – confidence interval of model intercept.

Species	Variable		Fixed Effects				Random Effects
			Intercept	Coastal Complexity	Life phase	Total length
Sparisoma axillare (N = 39)	log(KUD 95)	Estimates	-8.79	1.65	-0.05	0.01	Site	1.04 (51%)
		CI	[-12.49, -5.09]	[-4.04, 7.34]	[-0.99, 0.88]	[0.00, 0.02]	Residual	1.02 (49%)
		t-value	-4.84	0.59	-0.12	2.64	N	9
		p-value	<0.001	0.559	0.908	0.013	Marginal R2 / Conditional R2	0.159 / 0.584
	log(KUD 50)	Estimates	-10.88	1.11	-0.21	0.01	Site	0.74 (37%)
		CI	[-14.82, -6.95]	[-4.04, 6.27]	[-1.24, 0.81]	[0.00, 0.02]	Residual	1.27 (63%)
		t-value	-5.62	0.44	-0.42	2.76	N	9
		p-value	<0.001	0.663	0.678	0.009	Marginal R2 / Conditional R2	0.168 / 0.474
	sqrt(MCP)	Estimates	0.11	0.19	0.00	0.00	Site	0.00 (67%)
		CI	[-0.05, 0.28]	[-0.12, 0.50]	[-0.04, 0.04]	[-0.00, 0.00]	Residual	0.00 (33%)
		t-value	1.42	1.26	0.08	0.12	N	9
		p-value	0.165	0.218	0.940	0.907	Marginal R2 / Conditional R2	0.133 / 0.712
Sparisoma frondosum (N = 25)	log(KUD 95)	Estimates	-7.41	5.38	-0.11	0.01	Site	0.06 (3%)
		CI	[-13.53, -1.28]	[0.47, 10.28]	[-1.45, 1.23]	[-0.01, 0.02]	Residual	2.01 (97%)
		t-value	-2.53	2.29	-0.17	0.68	N	6
		p-value	0.020	0.033	0.865	0.508	Marginal R2 / Conditional R2	0.205 / 0.229
	log(KUD 50)	Estimates	-8.82	5.66	-0.16	0.00	Site	0.06 (3%)
		CI	[-14.93, -2.72]	[0.81, 10.50]	[-1.50, 1.17]	[-0.01, 0.02]	Residual	2.00 (97%)
		t-value	-3.02	2.44	-0.26	0.54	N	6
		p-value	0.007	0.024	0.801	0.595	Marginal R2 / Conditional R2	0.225 / 0.246
	sqrt(MCP)	Estimates	0.06	0.21	0.01	-0.00	Site	0.00 (0.3%)
		CI	[-0.21, 0.33]	[0.01, 0.41]	[-0.05, 0.07]	[0.00, 0.00]	Residual	0.00 (99.7%)
		t-value	0.48	2.18	0.39	-0.41	N	6
		p-value	0.639	0.042	0.700	0.689	Marginal R2 / Conditional R2	0.195 / 0.198

FIGURE 5| Comparative relationship between home range and total length (TL) for Sparisoma axillare (upper panels) and S. frondosum (lower panels) with a fitted line. Shaded areas represent confidence intervals, and each dot the home range of an individual fish.

Site fidelity/habitat utilization. All tagged fish were detected along the rocky reef (i.e., on hard substrate), at the same stretch of coast or island where they were tagged. Occurrence index was 70.1 ± SE 2.6 for S. axillare and 61.5 ± SE 4.7 for S. frondosum, with values ranging from 25 to 100 for the former and 25 to 90.9 for the latter (Tabs. 1, S1). No individual was ever detected in a location where it would have had to cross the open sand areas to reach the other islands or peninsulas on the isthmus.

Overlap in homerange. Sparisoma frondosum showed a lower % of overlap in core home range (KUD 50) of all life stages except terminal phase compared to S. axillare. While overlap in core home range was similar for all life phases of the former, overlap was very low (0.34% of core use areas (KUD 50%) between terminal phase individuals of S. axillare (Tab. 3).

TABLE 3 | Overlap in home range between initial (I) phase, initial and terminal (T) phase and terminal phase fish of the two studied species. Overlap in home range is calculated as % of Kernel Utilization Distribution 50% and shown ±standard error

Species	Phase	KUD 50 (%)
Sparisoma axillare	I/I	5.53±0.037
	I/T	2.91±0.013
	T/T	0.34±0.001
Sparisoma frondosum	I/I	1.54±0.007
	I/T	2.50±0.014
	T/T	19.57±0.131

Discussion​

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The present study provides critical information on home ranges for the endemic and abundant S. axillare and S. frondosum in the Southwestern Atlantic. Our data show that on a short time scale, site fidelity is high for the two studied species. This is consistent with other home range studies of parrotfish in the tropical Atlantic and Pacific (e.g., Howard et al., 2013; Davis et al., 2017; Lippi et al., 2022). No individual was ever detected on the cooler upwelling side of the islands or isthmus in accordance with previous observation studies (Cordeiro et al., 2016), confirming that temperature is an important factor for spatial use. Further, sandy areas seem to function as barriers, maintaining low movement connectivity among adults between reefs on the coast and islands, with fishes moving only along and over the rocky reef substrate. This behavior has been recorded for several species of reef fish (Lowe et al., 2003; Berkström et al., 2020) and has implications for the placement of MPAs and conservation strategies (Di Franco et al., 2018). Visual observations in the area confirm that the two species spend most of their time along the reef and seldom venture out on the sand, as most of the area lacks patches of seagrass, macroalgae beds or rubble habitats that would serve as stepping stones or alternative habitats (Ferreira et al., 2001; Cordeiro et al., 2016; Lucena et al., 2024).

Since fish habitat is restricted to the rather narrow (average 30 m in length from the surface to the sand interface (Ferreira et al., 2001) and shallow (average 10 m deep) rocky substrates, home range of large parrotfishes as S. axillare and S. frondosum comprise mainly longitudinal movements.

Our results support that fish size does not necessarily seem to be a primary factor in driving size of home ranges for parrotfish. In a study by La Mesa et al. (2012), Sparisoma cretense, who inhabit temperate to subtropical reefs, sustained comparatively larger home ranges than the individuals in our study, despite being smaller (max size of tagged individuals 29.8 cm; La Mesa et al., 2012). For the Pacific species Chlorurus microrhinus (max length 70 cm; Welsh, Bellwood, 2012; Howard et al., 2013), considerably smaller home ranges than the fish in our study have been recorded. Although most TP fish in our study had larger home ranges compared to IP fish, home range was not statistically influenced by size or life phase for S. frondosum, as hypothesized, and was only weakly influenced by size for S. axillare. Home and core ranges of S. axillare were one order of magnitude smaller in Arraial do Cabo compared to the same species on tropical coral reefs in the state of Pernambuco (Lippi et al., 2022), despite that most of the fish in our study were larger and included TP individuals. Although we used active instead of passive telemetry, limiting direct comparability with previous studies, our results nevertheless indicate that S. axillare in Arraial do Cabo occupies substantially smaller home ranges than conspecifics on tropical reefs.

Similarly to home range, daily trajectory was positively associated with size for S. axillare,but not for S. frondosum. Longer forays of up to 400 m from tagging sites have been observed for S. rubroviolaceus on reefs in Hawai’i (Howard et al., 2013) and over 500 m for C. microrhinus at the Palmyra atoll (Davis et al., 2017), comparable to the movements in the present study (< 700 m).

Coastal complexity was positively associated with the linear movements of S. axillare and with the home range of S. frondosum. Previous studies have documented differences in parrotfish movements between more open coasts and small bays (Howard et al., 2013). Although our models explained only a modest proportion of the variance, this is consistent with the expectation that home range is shaped by multiple interacting factors beyond those measured here. For instance, in harem-forming species, characteristics such as harem size (i.e., number of IP females) have been suggested as potential drivers of home range (Howard et al., 2013), but these aspects were not quantified in our study. Other unmeasured variables such as local resource availability, fine-scale habitat heterogeneity, interactions with territorial damselfish, and temperature (Francini-Filho et al., 2010; Catano et al., 2015; Davis et al., 2017) may also play a role. Evaluating these factors would require detailed, in-water behavioral observations and is therefore beyond the scope of the present study but would provide valuable avenues for future research.

A possible explanation to the smaller home ranges found in this study compared to home ranges of S. axillare on tropical reefs may be high resource availability due to the localized upwelling. The subtropical reefs of Arraial do Cabo are constantly influenced by upwelling events (Cardozo-Ferreira et al., 2023), resulting in food resources being comparatively N-enriched (Cardozo-Ferrera et al., 2026). Turf biomass is two-fold (70 to 280 gcm^-2; Ferreira et al., 1998) the values reported for the Great Barrier Reef (Klumpp et al., 1987; Russ, 1987; Klumpp, McKinnon, 1992) or for Caribbean reefs (Lobel, 1980; Carpenter, 1985, 1988). Local upwelling certainly contributes to these high values of algae biomass and primary productivity (Lanari, Coutinho, 2014), suggesting that smaller home ranges may be sufficient to meet energetic needs compared to locations where upwelling is absent (e.g., Lippi et al., 2022). Micro components of the diet of S. axillare include beyond detritus also green filamentous algae, cyanobacteria, diatoms and micro-invertebrates (Cardozo-Ferreira et al., 2023), all present in abundance in turf algae. Including nutrient content in studies of home ranges and comparisons of fish movement among geographic regions could be a useful approach in further studies to better explain spatial patterns (see for example Catano et al., 2015).

Core use areas may among other factors also be influenced by competition because parrotfish often use distinct sites for sleeping and migrate daily to their diurnal foraging areas (Davis et al., 2017; Lucena et al., 2024). Consistent with previous studies (e.g., Welsh, Bellwood, 2012; Davis et al., 2017), small core use areas were also confirmed in this study, with most individuals showing one or two core use areas on the reef. The spatial pattern of core utilization has previously been linked to optimal foraging behavior for parrotfish (Welsh, Bellwood, 2012; Yarlett et al., 2020). Parrotfish can be highly selective when feeding, and preferred resources may be patchily distributed or according to habitat and depth, with implications for their distribution on a local scale (Bruggemann et al., 1994; Clements et al., 2016; Carlson et al., 2017). At all study sites, the reef slope is rather steep (> 20 degrees of inclination) with few plateaus, which means that depth is only constant at a very narrow range. Sparisoma axillare prefer shallow areas of the reef (1–5 m) while S. frondosum have a more even distribution, although with higher biomass at the deeper parts of the reef slope (10–15 m) (Cordeiro et al., 2016). The methodology used in our study does unfortunately not allow for the detection of movement patterns at this fine spatial scale.

Social behavior is an important factor influencing distribution patterns of parrotfish (Buckman, Ogden, 1973; Afonso et al., 2008a,b; Feitosa et al., 2021). In our study, IP and TP fish showed considerable overlap of home range areas, while on tropical coral reefs of Pernambuco, on the Brazilian northeastern coast, they show some level of niche segregation (Lippi et al., 2022). The seascape in our study lacks deeper reefs, thus confining all individuals to a max depth of < 15 m. However, TP individuals of S. axillare had significantly larger home ranges than IP fish. This may be an effect of both social behavior and size. Despite that TPs of this species have been shown to display territorial behavior (Bonaldo et al., 2006), large fishes are also more prone to rove large areas. The larger overlap in home range by TP Sparisoma frondosum corroborates previous observations of lack of territorial patrolling (Bonaldo et al., 2006), but a larger sample size is needed to confirm these patterns, as they were highly variable.

In conclusion, short-term home range estimates indicate that the studied species utilize rather small areas (on the order of a few hundred meters – 1,000 m in length), which would fit in manageable no-take zones and possibly be effective to protect their main habitats in Arraial do Cabo. Within the Arraial do Cabo MPA, the restricted-use zone (ZURE) located between our sites 2 and 3 measures approximately 1,000 × 200 m, fitting this description. In this zone, both fishing and tourism is prohibited. Interestingly, this zone coincided with the highest number of parrotfish detections in our study. While our data were not designed to evaluate the effectiveness of specific management measures, this pattern may indicate that even relatively small, well-delimited areas can provide benefits to parrotfish populations when adequately enforced. Since all larger species of parrotfish in the study area have been severely affected by spearfishing, with the largest, green beaked parrotfish (Scarus trispinosus), being considered functional extinct (Bender et al., 2014), these results are potential good news for local efforts on parrotfish conservation. The entire MPA banned parrotfish harvest since 2019 (MMA, 2019), but to our knowledge there has been no systematic assessment of compliance or enforcement effectiveness of this ban. Such evaluations would be crucial to determine whether these strategies are achieving their intended conservation outcomes. Both studied species were restricted to narrow rocky reef habitats, which increases the concerns about habitat conservation, but potentially facilitates the establishment of conservation efforts. Geographical placement would be dependent on the objective of protected areas, i.e., if promoting spill-over would be important.

Our findings indicate that a combination of size, life phase and seascape features primarily shape home range size and movement behavior in the present study. Larger individuals of S. axillare exhibited larger home ranges, which is likely important for the social organization of harem-forming species and should be taken into consideration when designing no-take areas. This study provides the first home range estimations of these two species in Brazilian subtropical reefs, but long-term monitoring would be advisable to understand if individuals relocate over broader temporal scales, as documented for tropical species of parrotfish elsewhere (Chateau, Wantiez, 2009; Davis et al., 2017). Integrating nutritional ecology with detailed feeding observations, as suggested by previous studies (e.g., Carlson et al., 2017), would also improve our understanding of resource partitioning and spatial use within this functional group. Such complementary approaches will be essential for refining management strategies and predicting how parrotfishes will respond to ongoing environmental change. Finally, to further enhance our understanding of spatial use by S. axillare and S. frondosum, future studies should explore the influence of resource availability and agonistic interactions on their movement and home range dynamics.

Acknowledgments​

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We wish to thank Pedro Zaú for support with field work, the ICMBio for research permits and two anonymous reviewers whose comments substantially improved the manuscript.

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Authors

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Jora O. Batista^1,2, Linda Eggertsen^3,4 , Carlos W. Hackradt², César A. M. M. Cordeiro⁵, Moysés C. Barbosa⁴, Marcos B. Lucena⁴ and Carlos E. L. Ferreira⁴

^[1]    Programa de Pós-Graduação em Sistemas Aquáticos Tropicais, Universidade Estadual de Santa Cruz, 45662-900, Ilhéus, BA, Brazil. (JOB) jorabatista@gmail.com.

^[2]    Laboratório de Ecologia e Conservação Marinha, Centro de Ciências Ambientais, Universidade Federal do Sul da Bahia, 45810 000, Porto Seguro, BA, Brazil. (CWH) hackradtcw@ufsb.edu.br.

^[3]    Hawai’i Institute for Marine Biology, University of Hawai’i at Manoa, 46-007 Lilipuna Road, Kane’ohe 96744, HI, USA. (LE) eggertsen.linda@gmail.com (corresponding author).

^[4]    ILaboratório de Ecologia e Conservação de Ambientes Recifais, Universidade Federal Fluminense, Departamento de Biologia Marinha, 24020-141, Niterói, RJ, Brazil. (MCB) moysescb@gmail.com, (MBL) boucasdelucena@hotmail.com, (CELF) carlosferreira@id.uff.br.

^[5]    Laboratório de Ciências Ambientais, Universidade Estadual do Norte Fluminense, Campos dos Goytacazes, 28013-602, Campos, RJ, Brazil. (CAMMC) cammcordeiro@pq.uenf.br,

Authors’ Contribution

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Jora O. Batista: Data curation, Formal analysis, Investigation, Methodology, Project administration, Writing-original draft, Writing-review and editing.

Linda Eggertsen: Formal analysis, Investigation, Methodology, Visualization, Writing-original draft, Writing-review and editing.
Carlos W. Hackradt: Conceptualization, Funding acquisition, Methodology, Project administration, Supervision, Writing-review and editing.

César A. M. M. Cordeiro: Formal analysis, Investigation, Methodology, Project administration, Writing-original draft, Writing-review and editing.
Moysés C. Barbosa: Investigation, Methodology, Writing-review and editing.

Marcos B. Lucena: Investigation, Methodology, Writing-review and editing.

Carlos E. L. Ferreira: Conceptualization, Funding acquisition, Investigation, Methodology, Supervision, Writing-original draft, Writing-review and editing.

Ethical Statement​

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Fish tagging and field work was approved by Collection Licenses of the Brazilian Sistema de Autorização e Informação em Biodiversidade (SISBIO) number 55911–4.

Competing Interests

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The author declares no competing interests.

Data availability statement

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The data supporting the findings of this study are available from the corresponding author, upon reasonable request.

AI statement

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The authors did not use any AI-assisted technologies in the creation of this manuscript or its figures.

Funding

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This research was conducted by a cooperation of Costão Rochoso and Budião Projects, both funded through a partnership with Petrobras (Programa Petrobras Socioambiental). CELF is supported by grants from CNPq (310291/2023–0) and FAPERJ (E-26/201.026/2022). CAMMC is grateful to Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro (E-26/202.310/2019). CWH is supported by research grants from CNPq (300382/2025-1).

Supplementary Material

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Supplementary material S1

Supplementary material S2

Supplementary material S3

Supplementary material S4

Supplementary material S5

Supplementary material S6

How to cite this article

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Batista JO, Eggertsen L, Hackradt CW, Cordeiro CAMM, Barbosa MC, Lucena MB, Ferreira CEL. Home range and movement patterns of parrotfish in subtropical reefs of the Southwestern Atlantic. Neotrop Ichthyol. 2026; 24(1):e250058. https://doi.org/10.1590/1982-0224-2025-0058

Copyright​

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This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Distributed under

Creative Commons CC-BY 4.0

© 2025 The Authors.

Diversity and Distributions Published by SBI

Accepted December 23, 2025

Submitted May 5, 2025

Epub April 26, 2026