Habitat Preference and Local Climate Influencing Mantella Baroni Activity and Abundance

doi:10.21203/rs.3.rs-2948114/v1

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Habitat Preference and Local Climate Influencing Mantella Baroni Activity and Abundance

https://doi.org/10.21203/rs.3.rs-2948114/v1

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published 19 Oct, 2023

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Amphibian habitat preferences have ecological, evolutionary, and conservation implications, but for most species we have no information about habitat use, especially at the microhabitat level. Relatedly, considering ongoing widespread amphibian population declines, baseline estimates of abundance are increasingly important for informing conservation, yet for most species we have no knowledge of their population sizes. We explored the habitat preferences and estimated abundance for the poison frog Mantella baroni in Vohimana Reserve, eastern Madagascar. We recorded microhabitat and environmental variables in 155 quadrats in forest valleys, on slopes, and on ridgelines, surveying both during the rainy season and at the onset of winter. Using occupancy models, we identified leaf litter depth and the number of small trees in a quadrat as the most important habitat features. Deeper litter and fewer small trees increased the probability of quadrat use. Mean 24-hr temperature influenced activity, with higher detection at warmer temperatures. Capture-recapture models estimated the two M. baroni populations in the reserve at 27 (95% CI: 22–49) and 197 (95% CI: 168–246) adult individuals. Our results provide the first information about habitat use for M. baroni, showing frogs select relatively open areas with deep litter near valley streams, likely due to the species eco-physiological requirements and reproductive behavior. Considering M. baroni is harvested commercially for the international pet trade, we note current export numbers could impact local populations if collection is not spread across multiple sites and we point to how our results can inform ex situ conservation efforts.

amphibians

conservation

frogs

Madagascar

microhabitat

occupancy

Habitat preferences influence the abundance and distribution of species and help shape their evolutionary history through habitat selection, which is linked to the eco-ethological needs of individuals (Jaenike and Holt 1991; Morrison et al. 2006). Animals select habitat based on biotic and abiotic environmental conditions at different spatial and temporal scales (Mayor et al. 2009), with preferences of ectotherms like amphibians heavily affected by their physiology (Lemenager et al. 2022; Ruthsatz et al. 2022). Amphibian habitat preferences vary temporally across months and seasons but also spatially from the landscape to microhabitat level. Thus, studying habitat preference variation across temporal and spatial scales can provide valuable evolutionary and ecological insights, for example how species have evolved to fill a particular ecological niche (e.g., Díaz and Valencia 1985; Ficetola et al. 2018), or the ability of amphibians to adapt to environmental change (e.g., Egea-Serrano et al. 2006; Youngquist and Boone 2014; Valdez et al. 2021)

Understanding amphibian habitat requirements is also essential for meeting conservation needs (Semlitsch 2002; Cushman 2006). Without baseline knowledge of the habitat preferences of a species, actions like habitat creation and restoration remain guesswork, and new protected areas may be established without encompassing critical habitat components for all species (Semlitsch and Bodie 2003). Amphibians are also increasingly the target of ex-situ conservation efforts, for which knowledge of a particular species’ habitat and environmental preferences is often a prerequisite to sustaining captive assurance populations (Michaels et al. 2014). Additionally, demographic processes like recruitment and survival, which determine population persistence, can be partly explained by variation in the habitat characteristics affecting species presence (Heard et al. 2008). Accordingly, our ability to evaluate the risk of extirpation or extinction partially depends on how well we understand species-specific habitat requirements (Hoffmann and Mitchell 2021). Despite the conservation importance of such data, we are only beginning to learn the habitat preferences of amphibians, especially at the microhabitat level and for key periods of activity such as breeding season.

Madagascar is a biodiversity hotspot and among the countries with the greatest amphibian species richness and endemism (Myers et al. 2000; Tapley et al. 2018). Some of the most iconic frogs on the island are the 16–17 species of Malagasy poison frogs in the genus Mantella (Vences et al. 1999, 2022). They are a well-known example of convergent evolution with Dendrobatids and a symbol of Madagascar’s biodiversity, with M. baroni even appearing on the country’s currency (Fischer et al. 2019; Andreone et al. 2021). Alarmingly, 10 Mantella species (62.5%) are currently assessed as threatened with extinction by the IUCN Red List (1 Critically Endangered, 5 Endangered, 4 Vulnerable; IUCN 2023). The main threat Mantella species face is habitat destruction from subsistence agriculture, cattle grazing, logging, charcoal production, spread of Eucalyptus, fires, and expanding human settlements. However, some species have also been overexploited for the international pet trade and trade in all species is now monitored by the Convention on International Trade in Endangered Species (CITES; Andreone et al. 2006; Rabemananjara et al. 2007).

Despite their threatened status, we have little information about habitat requirements of Mantella species. Vences et al. (1999) first defined species groups, which can be broadly divided into those associated with streams, seasonally flooded swamps, or for one specialized species (M. laevigata), bamboo stands. Most species are also characterized by undergoing an extended period of inactivity or dormancy during Madagascar’s winter, and in captivity temperature and humidity play a large role in their daily and seasonal activity patterns (Woodhead et al. 2007; Edmonds et al. 2020; Edwards et al. 2022). However, aside from important work on M. aurantiaca (Randrianavelona et al. 2010; Edwards et al. 2019), we have no information about habitat preferences for other species at the microhabitat level or across seasons. Therefore, in the context of species conservation, it is essential to obtain more data about the habitat requirements of other Mantella species.

Despite being widespread throughout Madagascar’s central-eastern forests, no studies have examined the habitat preferences or seasonal activity of Mantella baroni. Though M. baroni is assessed as Least Concern by the IUCN Red List and is widespread, common species are especially important to study and monitor because they heavily influence ecosystems and can be prone to rapid declines without detection, especially when specialized on environmental conditions undergoing change (Whitfield et al. 2007; Gaston 2008; Lindenmayer et al. 2011). Additionally, M. baroni is one of six Mantella species still heavily exploited for the international pet trade, with thousands of wild frogs exported for the trade annually (CITES 2022). As such, establishing baseline estimates of population size is important for monitoring M. baroni populations and informing wildlife trade regulations.

We set out to (1) explore the microhabitat characteristics explaining M. baroni presence, (2) determine the environmental variables influencing detection and seasonal activity, and (3) estimate population sizes to provide baseline data for their conservation. Our results provide the first characterization of habitat requirements for one of Madagascar’s most iconic frogs and a species lacking crucial ecological field data.

Study site and data collection

The study was carried out in Vohimana Reserve (18.9289 S, 48.4996 E), a 2500 ha protected area in eastern Madagascar. We surveyed the two sites (A and B) in the reserve where M. baroni is known to occur (Figs. 1–2). The sites were located along different rivers at opposite ends of the reserve ~ 1.6 km apart at ~ 900 m asl. We surveyed each site in the rainy season and at the start of the austral winter. Site A was surveyed during 23–27 February and 21–25 April and Site B was surveyed during 1–5 March 26–30 April (Site B), providing 10-days of surveys for each population.

We initially based our survey methods on Edwards et al. (2019) who studied M. aurantiaca by establishing three 100 m long transects per site: one in the valley near the river, one 30 m above along the slope, and one on top of the hill along the ridgeline. All transects ran parallel to a river. Along each transect we flagged 1 m² quadrats spaced at least 4 m apart, resulting in 20 quadrats along each transect. During pilot surveys, no M. baroni were observed in quadrats on the slope or ridgeline, so we added 10 additional quadrats in the valley at Site A and 15 additional quadrats in the valley at Site B, resulting in a total of 70 quadrats at Site A and 75 at Site B. Altogether our dataset consisted of 1550 quadrat checks over 20-days: 700 from Site A and 850 from Site B.

During surveys, two people walked transects and visited all quadrats twice per day, once in the morning during 6–11h and once in the afternoon 13:30–16h. When a frog was found, we photographed its ventral side allowing for individual identification (Fig. 3). Microhabitat variables relating to vegetation, canopy, and litter cover were recorded within each quadrat (Table 1). We chose microhabitat variables based on Edwards et al. (2019) study of M. aurantiaca, however we did not include tree roots, moss, or damaged trees in our study as they did because these microhabitat variables were not large habitat components at our study sties. We hypothesized temperature, humidity, and barometric pressure would be associated with frog activity levels, and we recorded these environmental variables with a data logger (Extech RHT35) at 30-sec intervals (Table 1). For analysis, we categorized temperature and humidity as daytime (5:45–18:29), or nighttime (18:30–5:44) because M. baroni activity might be influenced differently depending on conditions experienced during the day or night in much the same way as M. aurantiaca (Edwards et al. 2022). We also computed the 24-mean for temperature and pressure, but not for humidity as there was little variation left in the humidity data after averaging by day.

Table 1

Microhabitat and environmental variables used in occupancy models for Mantella baroni at Vohimana Reserve, Madagascar. We incorporated microhabitat variables in models as covariates of the probability of quadrat use and environmental variables for detection probability. Additionally, we included the effects of site (A or B) and transect topography (valley, slope, or ridgeline) in all models.
Microhabitat variables (unit)	Environmental variables (unit)
Canopy cover (estimated %)	Mean 24-hr temperature (°C)
Canopy height (estimated m)	Mean day temperature (°C)
Leaf litter cover (estimated %)	Mean night temperature (°C)
Leaf litter depth (measured cm)	Difference of day-night temperature (°C)
Dead trees (count)	Mean day humidity (%)
Large trees > 1.5 m high (count)	Mean night humidity (%)
Small trees < 1.5 m high (count)	Barometric pressure (hPa)

Occupancy Models

We explored microhabitat characteristics and activity patterns using occupancy models. Occupancy models are more robust than other common microhabitat analyses such as significance tests and generalized linear models because they account for imperfect detection (Gorosito et al. 2016). Traditionally, occupancy models are used to assess species presence-absence across a landscape. In our case, we assumed occupancy (\(\psi\)) to be the probability a quadrat was being used by a frog and detection (\(p\)) the product of both the probability of presence in a quadrat and the probability of detection. We used an information theoretic framework to assess models and compared them by Akaike Information Criterion (AIC; Burnham and Anderson 2002).

First, we developed a global model with site, topography (valley, slope, or crest transect), and all microhabitat variables from Table 1 as occupancy covariates, and daytime temperature and daytime humidity as detection covariates. The variance inflation factors for covariates in the global model were all < 2.7. We assessed the fit of the global model with the MacKenzie and Baily goodness-of-fit test (MacKenzie and Bailey 2004), which showed no evidence of lack of fit using 5000 bootstrapped replications (p = 0.938). We then used a stepwise approach to develop a reasonably sized candidate model set with biologically relevant microhabitat and environmental covariates. First, we compared models of detection with each environmental covariate and all occupancy covariates. Then, we used the environmental covariate from the most parsimonious detection model as the detection covariate in subsequent models exploring the effect of microhabitat on the probability of quadrat use. Models with \(\varDelta\)AIC < 2 were averaged for predictions. Analyses were conducted in R 4.2.0 using package unmarked (Fiske and Chandler 2011; R Core Team 2022).

Capture-recapture Models

We estimated population size (\(\widehat{N}\)) using photographic capture-recapture. We used capture-recapture methods rather than estimating from counts (e.g., N-mixture modeling) because we had individual capture histories available from photographs, and capture-recapture has less risk of bias when there is large variation in animal activity levels (Couturier et al. 2013). We treated Site A and Site B as separate populations and tested each for closure before analysis (Stanley and Burnham 1999). While Site B appeared closed, there was evidence Site A may have been open to emigration during sampling. Thus, for Site A we estimated abundance with the POPAN formulation of the Jolly-Seber model which allows for immigration and emigration (Schwarz and Arnason 1996). For Site B, there was no evidence of immigration or emigration, so we used the closed models of Otis et al. (1978) for more precise population size estimates. For both sites, we assumed no deaths during the 5-day sampling periods. Goodness-of-fit tests did not reveal evidence of unequal capture probability between individuals. Analyses were conducted in Program MARK using the RMark package interface (White and Burnham 1999; Laake 2013).

Captures and population sizes

Over both sites we captured 143 M. baroni 198 times. All individuals were adults. At Site A, we estimated an adult population size of 27 frogs (95% CI: 22–49, SE = 5.87), where we captured 20 individuals 30 times. At site B we estimated an adult population size of 197 frogs (95% CI: 168–246, SE = 19.4), where we captured 123 individuals 168 times. The most parsimonious open population model for Site A had detection probability constant whereas for Site B the most parsimonious closed population model had detection probability varying by time (Table 2).

Table 2

Open (Site A) and closed (Site B) capture-recapture models used to estimate population sizes of Mantella baroni in Vohimana Reserve and ranked by adjusted Akaike Information Criterion (AICc). For Site A, we used a POPAN model, where phi = apparent survival, p = capture probability, pent = population entry probability, and N = population size. For Site B, we compared the models of Otis et al. (1978) where t = time, h = individual heterogeneity, and b = behavior.
\(phi\)\(p\)\(pent\)\(N\)\(t\)\(h\)\(b\)
Site A	Model	K	AIC_c	\(\varDelta\)AIC_c	Weight
	\(phi\left(1\right),p\left(1\right),pent\left(1\right),N\left(1\right)\)	4	64.30	0.00	0.75
	\(phi\left(t\right),p\left(1\right),pent\left(1\right),N\left(1\right)\)	7	66.55	2.25	0.24
	\(phi\left(1\right),p\left(t\right),pent\left(1\right),N\left(1\right)\)	8	74.19	9.89	0.01
	\(phi\left(t\right),p\left(t\right),pent\left(1\right),N\left(1\right)\)	11	83.47	19.17	0.00
Site B	Model	K	AIC_c	\(\varDelta\)AIC_c	Weight
	\(M\left(t\right)\) time dependent \(p\)	6	-370.04	0.00	0.81
	\(M\left(th\right)\) time and individual dependent \(p\)	8	-367.18	2.85	0.19
	\(M(.)\) constant \(p\)	3	-294.27	75.77	0.00
	\(M\left(bh\right)\) individual and behavioral \(p\)	4	-292.57	77.47	0.00

Climatic data description and indicators of activity

Mantella baroni exhibited highly seasonal activity. We did not observe any frogs during surveys at the end of April; all frogs were observed only during the rainy season in late February and early March. The best environmental predictor of detection was mean 24-hr temperature (Table 3). As the mean 24-hr temperature increased, so did the probability of detecting M. baroni (Fig. 4). In the rainy season, mean temperature was higher and daytime humidity lower than during the onset of winter (Fig. 5). Specifically, in the rainy season we recorded a mean daytime temperature of 22.4°C (range = 16.6–28.3°C) and nighttime temperature of 20.1°C (range = 16.8–23.1°C), compared to the onset of winter when mean daytime temperature was 18.9°C (range = 13.3–24.7°C) and mean nighttime temperature was 17.7°C (range = 13.7–20.8°C).

Table 3

Occupancy models evaluating microhabitat of Mantella baroni and ranked by Akaike Information Criterion (AIC). We first compared covariates of detection probability (bottom), and then used the detection covariate from the most parsimonious detection model for occupancy (top). ψ = occupancy probability, p = detection probability, CC = canopy cover, CH = canopy height, LC = litter cover, LD = litter depth, TD = dead trees, TL = large trees, TS = small trees, Topo = topography (valley, slope, ridgeline), K = number of model parameters, LL = log-likelihood.
\(\psi\)\(p\)
Models for microhabitat use	K	AIC	\(\varDelta\)AIC	Weight	LL
\(\psi\)(Site + Topo + CC + LD + TS)	9	363.85	0.00	0.44	-172.92
\(\psi\)(Site + Topo + CC + CH + LD + LC + TD + TL + TS)	13	365.00	1.15	0.25	-169.50
\(\psi\)(Site + Topo + LD + TD + TS)	9	365.70	1.85	0.17	-173.85
\(\psi\)(Site + Topo + LD + TS)	8	366.82	2.97	0.10	-175.41
\(\psi\)(Site + Topo + LD + LC + TD + TL + TS)	11	368.67	4.82	0.04	-173.36
\(\psi\)(Site + Topo + TS)	7	379.89	16.04	0.00	-182.94
\(\psi\)(Site + Topo + TD + TL + TS)	9	381.32	17.47	0.00	-181.66
\(\psi\)(Site + Topo + LD)	7	395.22	31.37	0.00	-190.61
\(\psi\)(Site + Topo + LD + LC)	8	397.21	33.36	0.00	-190.60
\(\psi\)(Site + Topo + CC)	7	405.22	41.38	0.00	-195.61
\(\psi\)(Site + Topo + CC + CH)	8	407.16	43.31	0.00	-195.58
\(\psi\)(Site + Topo + TD)	7	407.87	44.03	0.00	-196.93
\(\psi\)(Site + Topo)	6	408.39	44.54	0.00	-198.19
\(\psi\)(1)	3	469.30	105.45	0.00	-231.65
Models for detection	K	AIC	\(\varDelta\)AIC	Weight	LL
\(p\)(temp 24-hr mean)	13	365.00	0.00	0.88	-169.50
\(p\)(temp daytime)	13	369.66	4.66	0.09	-171.83
\(p\)(temp daytime + humidity daytime)	14	371.48	6.48	0.03	-171.74
\(p\)(temp night)	13	381.29	16.30	0.00	-177.65
\(p\)(temp night + humidity night)	14	381.95	16.95	0.00	-176.98
\(p\)(barometric pressure)	13	403.09	38.09	0.00	-188.54
\(p\)(humidity daytime)	13	415.80	50.80	0.00	-194.90
\(p\)(difference of temp daytime and night)	13	443.89	78.90	0.00	-208.95
\(p\)(1)	2	544.91	179.91	0.00	-270.45

Microhabitat preference

The number of small trees in a quadrat and leaf litter depth were the most important microhabitat variables explaining M. baroni quadrat use (Tables 3–4). Canopy cover and number of dead trees also slightly improved models but were not particularly meaningful on their own (Table 3). The most parsimonious model had canopy cover, leaf litter depth, and number of small trees as explanatory variables, however the global model and a model with leaf litter depth, small trees, and dead trees also had support. Averaging all three models showed the probability of frog occupancy increased with leaf litter depth but decreased with the number of small trees and percent canopy cover (Figs. 6–8). On average across both sites, quadrats with M. baroni present had 2.96 (SD = 1.74) small trees, 8.70 cm (SD = 6.78) deep litter, 47.20% (SD = 14.25) canopy cover, and 0.65 (SD = 0.78) dead trees. Average quadrats without M. baroni had 6.86 (SD = 3.35) small trees, 7.82 cm (SD = 6.15) deep litter, 57.79% (SD = 16.34) canopy cover, and 0.41 (SD = 0.59) dead trees.

Table 4

Model-averaged parameter estimates, unconditional standard errors (SE), and 95% confidence intervals (Lower, Upper) from occupancy models in Table 3 with ∆AIC < 2.
\(\varDelta\)
Parameter	Estimate	SE	Lower	Upper
Canopy Cover	-0.06	0.03	-0.11	0.00
Canopy Height	0.04	0.13	-0.22	0.29
Litter Depth	0.38	0.15	0.09	0.67
Litter Cover	-0.03	0.03	-0.08	0.02
Dead Trees	1.7	1.13	-0.53	3.92
Large Trees	0.47	1.48	-2.43	3.36
Small Trees	-0.73	0.25	-1.23	-0.23
24-hr Mean Temperature	1.45	0.21	1.05	1.85

Our objective was to characterize the microhabitat preferences of an iconic poison frog in Madagascar and determine the environmental variables influencing their seasonal activity. Our results show M. baroni activity is associated with warm temperatures and frogs prefer relatively open areas of forest with deep leaf litter in valleys near streams. Their increased activity during warmer months likely relates to breeding season, however eggs and tadpoles of M. baroni have never been observed in the field and are only described from captivity (Jovanovic et al. 2009a). Considering temperature regulates the physiology of amphibian reproductive cycles (Delgado et al. 1992; Saidapur and Hoque 1995), frogs should maximize their reproductive success by timing breeding activity to coincide with warmer temperatures. As such, the increased detection of M. baroni at warmer temperatures can be explained by their seasonal reproductive behavior, similar to M. aurantiaca (Edmonds et al. 2020). However, other environmental factors can influence amphibian breeding phenology, especially photoperiod and rainfall (Saenz et al. 2006; Borah et al. 2019), which we did not measure in our study. Additionally, temperature could serve as a proxy measurement for unidentified causal stimuli regulating amphibian behavior because temperature often covaries with other environmental conditions (Canavero and Arim 2009). Still, mean 24-hr temperature was the best indicator of M. baroni activity compared to the other environmental variables, which aligns with previous work showing temperature driving seasonal frog activity in Madagascar and daily activity patterns of captive Mantella species (Heinermann et al. 2015; Dubos et al. 2020; Edwards et al. 2022).

We offer several explanations for why litter depth and number of small trees were the most important microhabitat variables influencing M. baroni presence. First, leaf litter offers amphibians microhabitat with lower temperatures, increased moisture, and overall greater heterogeneity (Burrow and Maerz 2022), and in other tropical regions deeper litter is associated with higher frog species richness and abundance (Fauth et al. 1989; van Sluys et al. 2007). At the same time, open areas in tropical forests with a sparser understory reach higher temperatures (Fetcher et al. 1985; Tymen et al. 2017). Thus, we believe M. baroni is primarily selecting habitat to meet eco-physiological requirements, choosing relatively open areas with deep litter because the sites offer large thermal and moisture gradients to select from. Second, M. baroni might favor deep leaf litter for egg deposition. Aside from M. laevigata, Mantella species deposit masses of 20– 193 eggs on land near water (Vences et al. 1999; Tessa et al. 2009; Edmonds et al. 2015), and in captivity choose dark secluded locations, so deeper litter might provide better egg deposition sites. A third possible factor is that deep leaf litter can decrease predation risk (Folt and Guyer 2021). Although Mantella species display aposematic coloration to warn predators of their poisonous skin alkaloids, there are records of predation by skinks and beetles (Heying 2001; Jovanovic et al. 2009b; Garcia et al. 2018). Finally, the seasonal availability of arthropod prey appears to play a role in M. laevigata habitat use (Moskowitz et al. 2018), so areas of the forest with deeper leaf litter and fewer small trees could support greater arthropod abundance. Future research should examine the costs, benefits, and tradeoffs of these four factors affecting Mantella habitat preferences.

We based our study on the work of Edwards et al. (2019) and found some notable similarities and differences between M. aurantiaca and M. baroni. At the microhabitat level both species prefer relatively open areas of forest, with measures of leaf litter and woody structures influencing quadrat use. For M. aurantiaca, percent litter cover best explained frog presence rather than litter depth as in M. baroni, and number of tree roots in a quadrat was important rather than number of small trees. However, the greatest difference between the species relates to seasonal habitat use. Whereas M. aurantiaca moves down from forested hills to ephemeral breeding ponds in the rainy season (Randrianavelona et al. 2010; Edwards et al. 2019), we only detected M. baroni in valleys along streams, likely reflecting different breeding strategies for the two species. Considering M. baroni is widely distributed and sympatric or syntopic with several other Mantella species (M. cowanii, M. madagascariensis, and M. pulchra; Andreone 1992; Schaefer et al. 2002; Chiari et al. 2005), future research could explore competitive shifts in microhabitat use within and outside areas of sympatry. M. baroni should be less specialized in its habitat requirements than the other Mantella species with which it occurs, and in areas of sympatry might rely on slightly different microhabitat features than at sites where it is the only Mantella species present. Our results can be used for such a comparison because M. baroni occurs together with M. pulchra in Vohimana Reserve.

The population sizes of M. baroni differed between sites, with Site A supporting < 15% the number of frogs at Site B. Amphibian populations naturally fluctuate in size and may be in decline much of the time following short periods of high recruitment that sustain populations (Meyer et al. 1998). As such, long-term data are needed to determine if Site A supports a smaller population than Site B as our results suggest, or if the difference between Sites A and B can be attributed to demographic and/or environmental stochasticity. Species with fast life histories, which are short-lived and highly fecund, experience greater population growth rate variation than species with slow life histories (Sæther et al. 2013). For M. baroni, skeletochronology suggests the lifespan of wild frogs is typically 1–3 years (Guarino et al. 2008; Jovanovic et al. 2010) and gravid females have been found with 53–64 eggs (Tessa et al. 2009). Thus, due to the life history traits of M. baroni, population growth likely varies considerably and our estimates from a single year are not adequate to fully compare population sizes. Alternatively, or additionally, population size at Site B may be larger because it could be a source for A, in which case further studies on habitat use and resource availability are needed to examine factors influencing frog abundance.

Population size estimates of M. baroni in the literature are limited to the work of Rabemananjara et al. (2008) who used batch-marking and the Schnabel estimation method to rapidly assess the size of three populations exploited for the pet trade. They found population sizes of 49–108 frogs at three sites (Fanjavala, Ampasimpotsy, and Kidonavo), which is within the range of our estimates at Vohimana. Importantly, Rabemananjara et al. noted their estimates could be male-biased if outgoing territorial males were captured more often than females during the breeding season, and our results have the same limitation since we were unable to accurately record sex. Population size estimates would be biased low if females were not captured at the same rate as males. Still, taken together with the results of Rabemananjara et al., our estimates show that although M. baroni is widespread, population sizes are not large. Such observations have implications for the international pet trade. Records from the CITES Trade Database show 54,211 wild M. baroni exported during 2001–2021, averaging 2,802 individuals/year during the most recent decade (CITES 2022). Though collection for the pet trade provides economic benefits for biodiverse developing countries like Madagascar (Robinson et al. 2018a, b), exporting such large quantities relative to population sizes may not be sustainable. Andreone et al. (2021) conducted a population viability analysis for nine Mantella species, and although there was large uncertainty in the inputs of their model, their results show extinction probability increases when > 20% of the population is harvested. Additionally, considering the short lifespan and highly seasonal behavior of M. baroni, if frogs are collected early in the year before reproducing, the impact on populations may be greater than if collected after. Therefore, collection of M. baroni for the pet trade could easily cause local extirpation if exported frogs are sourced from very few sites, especially if harvested early in the season before reproducing.

A final implication of our results relates to ex situ conservation. Mantella baroni is closely related to M. cowanii, one of Madagascar’s most threatened amphibian species and the focus of a national conservation action plan (Andreone et al. 2020). In the action plan, one of the priorities is establishing an ex situ survival assurance population to safeguard against extinction. While there is a lack of information about the captive requirements of M. cowanii, some zoological institutions maintain M. baroni (Ziegler et al. 2022). As such, M. baroni could serve as a surrogate for developing captive breeding protocols for the highly threatened M. cowanii. Zoological institutions and breeding programs can use the microhabitat temperature and humidity measurements we collected to refine M. baroni captive breeding protocols. Our findings point towards a benefit of providing relatively large thermal and moisture gradients for captive frogs to select from, and varying temperature seasonally to replicate natural breeding conditions.

Acknowledgements

This work was supported by Parc Zoologique de Paris. We are extremely grateful to Olivier Behra and the NGO L’Homme et l’Environnement for help with coordination and logistical support at Vohimana Reserve, to local guides Martial, Jérôme, Rakoto, Gagah and Régis for helping in the field, and to Ethan Kessler for providing guidance on data analysis.

Authors’ contributions:

Conceptualization: Olivier Marquis and Tantely Rasoarimanana

Methodology: Olivier Marquis and Tantely Rasoarimanana

Investigation: Tantely Rasoarimanana

Data curation: Tantely Rasoarimanana and Olivier Marquis

Formal analysis: Devin Edmonds

Writing – original draft: Devin Edmonds

Writing – review and editing: All authors

Funding: This study was fully funded by Paris Zoo. The Illinois State Toll Highway Authority funded Devin Edmonds during writing and analysis.

Ethics approval: This study adheres to the welfare standards of Paris Zoo.

Consent to participate: Not applicable.

Consent for publication: The authors grant Evolutionary Ecology permission to publish this manuscript if it is accepted for publication.

Availability of data and material: The data generated during the current study are available in the Illinois Databank at https://doi.org/10.13012/B2IDB-2234820_V1. We provide the following private link for reviewers: https://databank.illinois.edu/datasets/IDB-2234820?code=UBJL9vH-bf_O3QXiPAgsGSGdfKyOY86Wao3rYRuWBP8 The dataset will be made publicly available after review if the article is accepted for publication.

Code availability: Available on request.

Competing interests: The authors have no financial or proprietary interests in any materials discussed in this article.

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Habitat Preference and Local Climate Influencing Mantella Baroni Activity and Abundance

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Materials and Methods

Study site and data collection

Occupancy Models

Capture-recapture Models

Results

Captures and population sizes

Climatic data description and indicators of activity

Microhabitat preference

Discussion

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