Small-scale estimation of relative abundance for the coastal spotted dolphins (Stenella attenuata) in Costa Rica: the effect of habitat and seasonality
*Dirección para correspondencia Abstract ]]>
The coastal spotted dolphin (Stenella attenuata graffmani) is one of the most common species of dolphin in inshore Pacific waters of Costa Rica. We conducted surveys in protected waters of the Papagayo Gulf, Costa Rica, to determine relative abundance of dolphins in relation to environmental variables. We used Generalized Additive Models to investigate the influence of a particular set of ]]>
Key words: Pantropical Spotted Dolphin; relative abundance; Habitat; Conservation; Pacific Ocean; Central America; Costa Rica, Cetacea.
Resumen
El delfín manchado costero (Stenella attenuata graffmani) es ]]>
Palabras clave: Delfín manchado pantropical, abundancia, hábitat, conservación, Océano Pacifico, América Central, Población. The spotted dolphin Stenella ]]>
is one of the most common dolphin species in the eastern tropical Pacific (ETP) and at same time the most affected by the tuna industry (Dizon et al. 1994, Hall 1998). For management purposes, the species has been classified into four stocks based on morphological and geographical differences: Northerwestern, Southern offshore, Coastal, and Hawaiian (e.g., Perrin 1975, Douglas et al.1984, Schnell et al.1986, Perrin
et al.1987, Dizon et al. 1994, Escorza-Treviño et al. 2005). The Northernwestern offshore stock is the most affected by the tuna fishery because of its association with the yellow-fin tuna (Thunnus albacares), and more efforts have been put in estimating the abundance and trends in abundance of the offshore than the Coastal or other stocks (e.g., Perrin et al.1982, ]]>
et al. 1992, Wade & Gerrodette 1993, Fiedler & Reilly 1994, Wade 1995, Gerrodette & Palacios 1996, Archer et al. 2004, Gerrodette & Forcada 2005, Wade et al. 2007, Cramer et al. 2008, Archer et al. 2010).
The Coastal stock (
S. attenuata graffmani), ranges within a 200 km band of coastal waters from southern México to Ecuador (Dizon et al. 1994). Genetic studies indicate the presence of several resident populations (Escorza-Treviño et al. 2005) along its distribution. Some of the latest abundance estimations include Gerrodette and Forcada (2006) using simulation approaches. They reanalyzed data collected over 21 years and ]]>
et al. 2007). However, estimations for the coastal stock have not been possible due to limitations in survey effort in coastal waters (Gerrodette & Forcada 2006). Abundance estimations of coastal spotted dolphins for the Exclusive Economical Zone of Costa Rica are limited to an old report in which relative abundance was estimated to be around 16, 600 dolphins, approximately 4.3-9.5% of the ]]>
Cetacean abundance is generally estimated with reference to a large regional-scale (e.g. Eastern Tropical Pacific); however for the coastal spotted dolphins, which have small distribution range, high residency times in regionally limited areas, and high annual mortality in fisheries operations, smaller scale studies are likely to provide more ]]>
et al. 2002). The role of local environmental variation in the population dynamics of dolphin resident populations is thus considered important in temperate waters, but very little has been documented in ]]>
et al. 2006, Levin et al. 2006) and dolphin abundance is likely to respond to this environmental variation.
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Generalized Additive Models (GAMs; Hastie & Tibshirani 1990) are useful to study abundance and distribution of highly mobile animals in relation with the environment at different scales, and in a more flexible way than linear analyses. The GAMs help determining whether apparent spatial and temporal trends represent real abundance changes or just natural fluctuations in distribution due to habitat variation (Forney 2000).
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At the Pagayo Gulf, Costa Rica, coastal spotted dolphins appear to be resident year round (May-Collado 2001, 2009, May-Collado & Morales-Ramírez 2005, May-Collado et al. 2005, Martínez-Fernández et al. 2011). The gulf is characterized by two clearly marked seasons, dry and rainy, of which the dry season is characterized by strong northeast trade winds that ]]>
Methods
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Study site: Murciélago Archipelago (N10°51’/W085°54’) is a set of old continental islands located in the northern part of the Papagayo Gulf, Costa Rica (Fig. 1). The area is affected by annual north-south migration of the inter-Tropical Convergent Zone (iTCZ); during the dry season (December-April) the iTCZ moves southward, the northeast trade winds are intensify generating superficial currents and the richest ]]>
et al. 2006, Lavin et al. 2006, Alfaro & Lizano 2001).
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Sampling Methods: A total of 13 strip transects (1000 m x1000 m) were surveyed during the morning (6 a.m. to 12 p.m) and afternoon (1 p.m. to 6 p.m.) from January 1999 to November 2000, using small outboard engine boat. Because of the small range of the area sampled and the constant sighting cues of dolphin groups, we assumed total detection groups up to a distance of approximately 500 m at each side of the transect lines. This distance was the maximum distance at which dolphins were reliably detected from our small boat. We established this maximum distance using a range finder at different distances from a buoy. For every group detected we measured: sea surface temperature (Celsius) using a normal thermometer, concentration of dissolved oxygen (mg O2/L) was obtained using a dissolve oxygen meter (YSi 52), salinity was measured with a refractometer (ATAGO S/Mill-E) and water transparency (m) using a Secchi disk. in addition, thermocline depth was estimated in two stations that were monthly surveyed using a Niskin bottle we sampled the water column every 10 meters from the surface up to 50 m and measure dissolved oxygen, salinity, and temperature for each sample. Station Termo was located between the Archipelago and the mainland (N10º51.976’ - W085º54.283’, 40m deep) and station Catalina was located behind the main island, San Jose, facing offshore waters (N10º50.703’ - W085º55.016’, 50m deep). The thermocline was defined as a rapid change in the water temperature along the water column (Philander 1996, Thurman 1996). The thermocline occurs particularly in tropical and subtropical waters where it plays a significant role on the distribution of many marine organisms (Quesada-Alpizar and Morales-Ramirez 2004).
We also measured cloud cover using a scale of 0-8, sea state and wind speed based by discretely categorizing both as (1) zero with ocean surface flat and little wind, (2) light sea state when ocean surface had a few white caps, (3) moderate sea state, when ocean surface had considerably occurrence of white caps and, (4) strong sea state when wave action generated high waves, and many white caps. The data analyzed in this study was collected when sea state was zero and light. Finally, water depth was estimated from digital maps of the area with contour lines using ArcGiS tools.
Analysis
We used generalized additive models (Hastie & Tibshirani 1990) to evaluate changes in relative abundance corresponding to the total length of the transects surveyed as a function of environmental variables in a descriptive analysis, and second, we analyzed seasonal changes in relative abundance.
In the descriptive analysis, the estimated number of dolphins of each encountered school was the response variable to a set of environmental predictors , where the j sub-script corresponds to the jth survey. Since the response described dolphin counts we assumed a Poisson distribution error in the model, and, because of the sparseness of our data we allowed for over-dispersion by specifying an error structure with variance proportional to the mean. We used a logarithmic function to link response and predictors. Our model had the general structure:
where “off” was an offset variable corresponding to the total length (sum) of the transects of survey j; θ is the intercept of the model to be estimated while fitting the data; and the fj are smoothed functions of the environmental covariates, which we chose to be smoothing splines. Smoothness, measured in degrees of freedom, was selected as a compromise between bias and variance of the fitted model, and described the linearity between and .
To select environmental covariates with high explanatory power of the response we used forward stepwise model fitting, starting with a general model including all possible covariates as linear terms and, at each step, one linear covariate was either replaced by a smoothed function with increasing degrees of freedom, from two to six, or was dropped from the model according to what option provided best fit of the model to the data. At each step, the best options were selected by the model with lowest Akaike information Criteria (AiC, Akaike 1973), a statistic that measures the best compromise between number of parameters, i.e. covariates or degrees of freedom of covariates, and an adequate description of the data.
To investigate seasonality, we modified a trend analysis based on GAMs developed by Fewster et al. (2000). in contrast to Fewster et al. (2000) analysis, we did not sample different sites, and we measured within season variability by replicated surveys of the same group of transects each month. Replicates within one month were considered independent, since were conducted at different times of the day. in our model, each survey was assumed to follow an independent Poisson distribution with mean number of dolphins μit in replicate i and month t. Within the GAM framework, month becomes an additive non-linear predictor:
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where αi is the replicate effect and f(t) was the month effect, estimated with a smoothing spline . With this model, the trend in the logarithm of the expected count over time is the same for each replicate; even absolute counts differed between replicates. An index of abundance over time is then constructed with the ratio of the estimates of over the estimated effect of the first month:
To provide a reasonable account of short term changes in dolphin counts, we selected smoothing splines with 7 degrees of freedom which revealed fine scale changes over time without decreasing unnecessarily the smoothing the output trend curve. To estimate the variance of this index we used a non-parametric bootstrap approach proposed by Fewster et al. (2000), in which replicated surveys in each month were treated as resampling units.
We estimated second derivatives of the smoothed index I(t) to identify major change points over time. At these points the second derivative is different from 0. if the second derivative was positive there was a change upwards, and if it was negative the change was downwards, i.e. a significant increase or decrease in abundance. The significant changes in the second derivatives were assessed with the approximate confidence intervals of the second derivatives, estimated from the bootstrap replicates of the indices.
Results
A total of 7,524 km of survey effort was completed in the two years of study and a high proportion of the surveys (62.5%) were completed with good sighting conditions (see methods). A total of 637 dolphins were seen in 64 groups. Group size ranged from 1 to 50 dolphins, two to five individuals being that most frequently observed. Mean group size was 9.95 (SD=10.28). The best-adjusted model retained three environmental variables: concentration of dissolved oxygen, water transparency and depth, based on the smallest AiC (Table 1, Fig. 2). The number of coastal spotted dolphins increased non-linearly with increasing superficial dissolved oxygen concentration and linearly with the water transparency and depth (Fig. 2). Despite of the variability in the data there is a trend in that coastal spotted dolphins seem to aggregate in relatively larger schools in clearer and deeper waters around the islands (Fig. 2).
The trend analysis confirmed the effect of the month in the dolphin’s counts, showing a gradual decrease in the counts from the dry season to mid-rains (Fig. 3). The wider stan dard error bands show large within-season variability, as estimated by the 95% bootstrap confidence intervals (Fig. 4). The largest variability is observed during the dry season in both years and the lowest during the months of rain. Overall, relative abundance of spotted dolphins varied annually, increasing in numbers at the end of the rainy season and reaching its peak in the dry season, during the months of upwelling. Afterwards, during the early and mid-rainy season there was a dramatic decrease in dolphin abundance. These results indicate that coastal spotted dolphins relative abundance varies annually in response to the environmental seasonality of the habitat. ]]>
Furthermore, differences in the months in which abundance reached its highest and lowest values suggest that dolphin inter-annual variation may be occurring as well. During 2000 the largest variability in abundance was in January, followed by a decrease in the number of dolphins during the early and midrainy season (May to September). This decrease is revealed by significant upturns of the trend line (
Fig. 2) from April to July of 2000, which could be detected given the low variability in dolphin counts during these months. in July 2000 the relative abundance of the dolphins increased again and continued to increase throughout the last months of rains, and during the dry season. During the previous year, the higher number of dolphins was observed in March, and a significant decrease occurred from July to September. The abundance of dolphins started to increase in October.
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Discussion
Generalized Additive Models are an effective way to detect environmental variables that best predict changes in the relative abundance of the coastal spotted dolphin in the waters surrounding the Murciélago islands, Costa Rica. These models may allow for a ]]>
Regional scale seasonal variation in tropical waters plays an ]]>
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The Pacific waters of Costa Rica are part of the tropical waters of the “Central American Bight” (near coastal waters from Guatemala to Ecuador). These waters are considered the most variable within the Tropical Surface Water Province due to the influence of the inter-Tropical Convergence Zone (iTCZ) which annual migration generates dramatic changes in the oceanography of the area (Au & Perryman 1985, Rasmusson & ]]>
in agreement with this suggestion, MayCollado & Morales-Ramírez (2005) reports that seasonal changes in behavioral patterns and abundance of dolphins in the Gulf of ]]>
et al. 1973, Fieldler et al. 1998). These changes in tropical areas might be particularly more intense in local populations, perhaps not as dramatic as in local temperate dolphin populations, but sufficient to have an impact in the abundance and distribution of the dolphins and their prey. ]]>
et al. 2011).
in conclusion, Generalized Additive Models helped us ]]>
et ]]>
2005) subject to seasonal variation. Therefore, designing conservation strategies relies on the knowledge of their occurrence and seasonal use of important areas.
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*Correspondencia a: Laura J. May-Collado.University of Puerto Rico, Department of Biology, San Juan, Puerto Rico, 00931¸lmaycollado@gmail.com. George Mason University, Department of Environmental Science & Policy, MSN 5F2, 4400 University Drive, Fairfax, Virginia, USA 22030 Jaume Forcada. NERC, British Antarctic Survey, High Cross, Madingley Road, Cambridge CB3 0ET, United Kingdom; jfor@bas.ac.uk 1. University of Puerto Rico, Department of Biology, San Juan, Puerto Rico, 00931¸lmaycollado@gmail.com 2. George Mason University, Department of Environmental Science & Policy, MSN 5F2, 4400 University Drive, Fairfax, Virginia, USA 22030 3. NERC, British Antarctic Survey, High Cross, Madingley Road, Cambridge CB3 0ET, United Kingdom; jfor@bas.ac.uk
Received 22-IV-2011. Corrected 01-XII-2011. Accepted 15-II-2012.
of each encountered school was the response variable to a set of environmental predictors 
, where the j sub-script corresponds to the jth survey. Since the response described dolphin counts we assumed a Poisson distribution error in the model, and, because of the sparseness of our data we allowed for over-dispersion by specifying an error structure with variance proportional to the mean. We used a logarithmic function to link response and predictors. Our model had the general structure: 
over 
the estimated effect of the first month: 