Diversidad de especies de Chironomidae (Diptera) de estuarios en un gradiente de uso de suelo en la costa Caribe de Costa Rica.
During the past several decades, agricultural intensification has increased nutrient enrichment and caused widespread eutrophication, accelerating the flow of nutrients to estuaries and other coastal marine ecosystems (ECEs) (Bricker et al., 2008; Howarth et al., 2011; Kennish & Townsend, 2007). Nutrient over-enrichment has been identified as the primary cause of water quality and habitat degradation of ECEs (Boesch et al., 2001; Nixon, 1995; Paerl, Hall, Peierls, & Rossignol, 2014). In tropical regions, land conversion of native rainforest ecosystems to monoculture plantation agriculture has posed significant risks to ECEs (Downing et al., 1999; Kress, Coto, Brenes, Brenner, & Arroyo, 2002; Lovelock, Feller, McKee, Engelbrecht, & Ball, 2004).
On the Caribbean coast of Costa Rica, banana and pineapple plantations are two types of agricultural land uses that impact ECEs (Castillo et al., 2006; Echeverria-Saenz et al., 2012; Grant, Woudneh, & Ross, 2013). Both types of monoculture agriculture are linked to water pollution via modifications in water movement through the construction of canals that channelize runoff, and application of large quantities of agrochemicals through aerial spraying and ground application (Castillo, de la Cruz, & Ruepert, 1997; Castillo, Ruepert, & Solis, 2000; Diepens et al., 2014; Pringle et al., 2016). In addition, the Caribbean coast of Costa Rica is experiencing urbanization by increasing the number of paved and improved gravel roads to accommodate the expansion of pineapple and banana plantations (Fagan, 2014). Kranzfelder, Corcoran, Rampi, Knight, and Ferrington (in review) found that expansion of agricultural plantations was the most pronounced land use change in the Northeastern Caribbean watersheds of Chirripo and Tortuguero, while urbanization was the most evident change in the south-central watersheds of Reventazon and Pacuare.
Biological multimetric indices, such as the Index of Biotic Integrity (IBI), are effective and efficient bioassessment tools that synthesize several community or individual level metrics into a numerical score in order to evaluate the cumulative impact of multiple stressors on the health of ECEs (Barbour, Gerritsen, Snyder, & Stribling, 1999; Herman & Nejadhashemi, 2015; Karr, 1981; Weisberg et al., 1997). Traditional multimetric IBI approaches have focused on periphyton, benthic macroinvertebrate, and fish assemblages (Barbour et al., 1999; Karr, 1981; Karr, Fausch, Angermeier, Yant, & Schlosser, 1986). However, the family Chironomidae (Diptera), commonly referred to as non-biting midges or chironomids, is the most widely distributed, most diverse, and often the most abundant of all families of benthic macroinvertebrates in aquatic ecosystems, including ECEs (Ferrington Jr., 2008). Chironomid communities are considered valuable bioindicators of water quality due to their high species diversity and the varying sensitivity of these species to land use changes, including urbanization, agriculture, and deforestation, and subsequent nutrient enrichment (King & Richardson, 2002; Lunde & Resh, 2012; Nicacio & Juen, 2015; Rosenberg, 1992; Ruse, 2010; Thorne & Williams, 1997). There are nearly 900 species described from the Neotropical Region (Spies, Andersen, Epler, & Watson, 2009; Spies & Reiss, 1996), and up to 20 000 species may exist worldwide (Ferrington Jr., 2008). In addition, chironomids are among the few aquatic insect families that have adapted to live in a wide range of salinities, from 0 to 35 ppt, and can be a major component of the fauna of brackish waters (Canedo-Arguelles et al., 2012; Casas & Vilchez-Quero, 1996; Dimitriadis & Cranston, 2007; James, Cant, & Ryan, 2003; Williams & Hamm, 2002; Williams & Williams, 1998). For example, there are typical marine species, such as Clunio marinus Haliday, 1855 (Palmegn & Lindeberg, 1959) or the flightless genus Pontomyia Edwards, 1926 (Huang & Cheng, 2011). However, three predominately freshwater genera, Chironomus Meigen, Cricotopus v.d. Wulp, and Tanytarsus v.d. Wulp, have species in brackish water and are part of the marine fauna, as well (Colbo, 1996). In one brackish Costa Rican estuary, Laguna del Tortuguero, Kranzfelder and Ferrington Jr. (2016) identified 98 morphospecies with species diversity structured along a salinity gradient.
One low-cost, simple, and efficient method of assessing chironomid communities involves collections of Chironomidae surface-floating pupal exuviae (SFPE) (Ferrington Jr. et al., 1991; Kranzfelder et al., 2015; Wilson & Ruse, 2005), which is the exoskeleton shed by the adult as it emerges on the surface of the water. SFPE accumulate behind obstructions, like fallen trees or along banks, through the action of wind or water current and can be collected to give a comprehensive sample of emerging chironomid communities for biological monitoring purposes (Kranzfelder et al., 2015). Collections of SFPE samples have many advantages over benthic samples of chironomid larvae including: (1) enhanced ease of sample processing and species identification, (2) more accurate estimates of the number of species collected in samples, (3) better collection from a range of microhabitats, including areas that are difficult to sample with other collection methods (e.g. wood, sand, and deep waters), and (4) improved measurement of how water quality characteristics influence biodiversity of aquatic systems, since SFPE collected represent individuals that have successfully completed their life cycle in that aquatic habitat (Bouchard Jr. & Ferrington Jr., 2011).
In Costa Rica, the Biological Monitoring Working Party-Costa Rica (BMWP-CR) biotic index was adopted in the Executive Decree No. 33902-S-MINAE (Ministerio del Ambiente y Energia y la Ministra de Salud, 2007) for assessment of environmental quality of waters (Gutierrez-Fonseca & Lorion, 2014; Maue & Springer, 2008; Rizo-Patron V, Kumar, McCoy Colton, Springer, & Trama, 2013; Stein, Springer, & Kohlmann, 2008). The BMWP-CR index value is based on the presence of macroinvertebrate families and their tolerance scores. According to their protocol, Chironomidae have a score of 2.0 and are categorized as being indicators of poor water quality. However, family identification does not provide sufficient resolution for sensitive and accurate bioassessments (Bailey, Norris, & Reynoldson, 2001; Bouchard Jr. & D. Huggins, 2005; Lenat & Resh, 2001), since tolerance to pollution varies by genus (Ruse, 2002; Wilson & Ruse, 2005), and frequently among species within a genus (King & Richardson, 2002). For example, Cricotopus, Chironomus, and Dicrotendipes are generally tolerant of organic pollution, while Cladotanytarsus, Parametriocnemus, and Paralauterborniella are intolerant of organic pollution (Wilson & Ruse, 2005). To provide better resolution of environmental quality for bioassessment, Chironomidae IBIs have been successfully developed for bioassessment of wadeable groundwater- and surface-water-dominated streams in Minnesota (Bouchard Jr. & Ferrington Jr., 2011) and wadeable perennial streams in the Northern Glaciated Plains ecoregion (Kafle, 2013). Thus, Chironomidae assemblages may provide a useful measure of biotic integrity in estuaries of Costa Rica, which lack an intensive estuarine bioassessment tool to support environmental monitoring and regulatory programs.
The objectives of this research were to (1) characterize the taxonomic composition of Chironomidae, (2) test a Chironomidae Index of Biotic Integrity (CIBI) developed from extrinsic pollution tolerance values for its efficacy in evaluating the surface water quality and physical habitat, and (3) make recommendations for increasing the sensitivity of CIBI to detect differing degrees of stress across a range of ECEs on the Caribbean coast of Costa Rica.
MATERIALS AND METHODS
Sample area: We selected nine estuaries within six different watersheds across a land use gradient located on the Caribbean coast of Costa Rica (Table 1, Fig. 1). These six watersheds were selected to represent a land use gradient from mostly primary and secondary tropical rainforest to largely monoculture plantation agriculture (e.g. banana and/or pineapple). At the coastal city of Limon (09[degrees]57 N & 83[degrees]01 W; elevation 5 masl) (Fig. 1), the mean monthly temperature is 25.9 [degrees]C and the mean total monthly rainfall is 298.3 mm. Mean total monthly precipitation is 436.1 mm over an average of 22 rainy days in July and 317.0 mm over an average of 19 rainy days in January (Instituto Meteorologico Nacional, 2016). See Kranzfelder et al. (in review) for a detailed description of the study area, land uses, and land covers of the six watersheds.
Data collection and sample processing: We collected data biannually for two consecutive years (July 2012, Jan. 2013, July 2013, Jan. 2014). We sampled each of the nine estuaries one day during each sample event. We collected data from three zones in each estuary that represent a range of estuarine conditions: (1) the transition of the river into the estuary (1-10 ppt), (2) the middle of the estuary (10-20 ppt), and (3) near the outlet of the estuary to the ocean (20-30 ppt). We collected Chironomidae SFPE samples along 500-1 000 meter reaches of the left and right descending banks at each sample zone. We used a YSI Pro2030 field dissolved oxygen/conductivity handheld meter to measure the following physiochemical parameters from each zone: water temperature, dissolved oxygen, and salinity. A Secchi disk was used to measure water clarity using standard protocol described by (Shapiro, Lamarra, & Lynch (1975) (Table 1).
Chironomidae SFPE samples were collected from both banks of each zone to account for hourly-to-daily changes in wind and tidal pattern, which may affect the down water drift of chironomid pupal exuviae. We collected 24 SFPE samples per estuary (3 zones x 2 SFPE samples/zone x 4 sample events) and followed methods described by Kranzfelder et al. (2015), except samples were collected for 20 min instead of 10 min, as suggested by Siqueira, De Oliveira Roque, and Trivinho-Strixino (2008) and Kranzfelder and Ferrington Jr. (2016). Briefly, we collected SFPE samples by repeatedly dipping a white plastic tray into areas of known SFPE accumulation (e.g. behind a fallen tree) and pouring then contents from the tray through a 125-[micro]m-aperture US Standard test sieve to retain chironomid SFPE and residue. We transferred the sample to 60-ml jars and preserved with 95% ethanol.
For each estuary, we picked up to 500 specimens per sample, but did not pick whole pupae, SFPE with adults still attached to the pupal exuviae, or broken specimens with less than 3/4 of the specimen complete to avoid species identification problems. In temperate streams, a subsample size of 300 specimens was on average sufficient to identify a large proportion (85%) of the Chironomidae community (Bouchard Jr. & Ferrington Jr., 2011). However, the larger subsample size was chosen to ensure that SFPE samples represent a large proportion of the Chironomidae community from the relatively unknown ecosystem of Costa Rican estuaries. We dehydrated specimens in 95% ethanol before dissecting and slide mounting all specimens in Euparal. Specimens were either slide-mounted individually or in multiples. Slide-mounted specimens were identified under a compound scope to morphospecies or the lowest taxonomic level possible. All contemporary references were checked, but the following were especially useful: Beck and Beck (1966), Boesel (1974), Paggi (1977), Roback (1980), Roback and Coffman (1983), Borkent (1984), Sawedal (1984), Wiederholm (1986), Roback (1986), Soponis (1987), Epler (1988), Langton (1991), Caldwell (1993), Sublette and Sasa (1994), Serrano and Nolte (1996), Wiedenbrug and Fittkau (1997), Epler and Janetzky (1998), Hestenes and Saether (2000), Jacobsen and Perry (2000), Andersen and Mendes (2002), Wiedenbrug and Andersen (2002), Mendes, Marcondes, and Pinho (2003), Ssther (2004), Wiedenbrug and Ospina-Torres (2005), Ekrem (2007), Ferrington Jr., Berg, and Coffman (2008), Jacobsen (2008), Tejerina and Paggi (2009), Wiedenburg, Mendes, Pepinelli, and Trivinho-Strixino (2009), Wiedenburg and Trivinho-Strixino (2009), Oliveira, Mendes, and Silva (2010), Trivinho-Strixino, Da Silva, and Roque (2010), Ferrington Jr. and Saether (2011), Oliveira and Silva (2011), Saether (2011), Wiedenburg and Trivinho-Strixino (2011), Kranzfelder (2012), Ssther and Cranston (2012), Wiedenbrug, Lamas, and Trivinho-Strixino (2012), De Oliveira, Da Silva, and Gessner (2013), Wiedenburg, Lamas, and Trivinho-Strixino (2013), Da Silva, Fonseca-Gessner, and Ekrem (2014), Donato, Siri, Massaferro, and Brooks (2015), Silva and Ekrem (2016), and Tang (2016). Finally, we deposited voucher specimens in the insect collections at the University of Minnesota (UMSP) and University of Costa Rica (UCR). A list of species can be found in Supplemental Table 1 and a count of individuals by species in Supplemental Table 2.
Data analysis: We pooled 24 chironomid SFPE samples for each of the nine estuaries.
First, we estimated theoretical species richness in EstimateS with the abundance-based Chao 1 classic richness estimator (Eq. 1), which is a nonparametric species-richness estimator that takes into account relative abundance and performs well on data that include many rare species (Chao, 1984; Colwell, 2013):
[S.sub.est] = [S.sub.obs] + [a.sup.2]/2b (1)
where [S.sub.obs] = the number of observed species in the sample; a = the number of species represented by only 1 individual; and b = the number of species represented by 2 individuals. Second, based on recommendations by Bouchard Jr. and Ferrington Jr. (2011) for Minnesota streams, we tested a Chironomidae Index of Biotic Integrity (CIBI) using 10 community composition and diversity and biotic index metric values: Total Species Richness, Berger-Parker Index of Dominance (Eq. 2), Shannon's Diversity Index (Eq. 3), and Margalef's Diversity Index (Eq. 4), Hilsenhoff's Biotic Index (Eq. 5), % Tolerant Genera, % Intolerant Genera, % Orthocladiinae, % Chironomini, and% Tanytarsini. Although these benthic metrics were regionally developed, they are effective at measuring a response across a range of human influence over a wide geographic area (Barbour et al., 1999). Metric values are the raw numeric expression of taxonomic or autecological information at either the community of individual level. Metric scores were derived from our metric values by calculating the metric value range (minimum to maximum) and ranking these values from 1 to 10 points based on its response to human disturbance. Metric values that respond negatively to human disturbance will have metric scores positively correlated to metric values, while metric values that respond positively to human disturbance will have metric scores inversely related to metric values (Barbour et al., 1999). The CIBI score was calculated for each estuary by summing the ten metric scores. See Supplemental Table S3 for a detailed description of each of the ten metrics. Using this approach, theoretical CIBI scores for each estuary range from 10 to 100 with lower CIBI scores indicating lower biotic integrity ratings and implying higher human disturbance. Estuary biotic integrity was rated in the following manner, similar to Karr et al. (1986): 1) Very poor: 10-19 CIBI score; 2) Poor: 20-39 CIBI score; 3) Fair: 40-59 CIBI score; 4) Good: 60-79; 5) Excellent: 80-100. Berger-Parker Index of Dominance was calculated as:
DBP = [n.sub.max] IN (2)
where [n.sub.max] = the number of specimens for the most common species and N = the total number of specimens in the sample (Berger & Parker, 1970; Magurran & McGill, 2011). Shannon's Diversity Index was calculated as:
H' = - 2 ([n.sub.i]/N) ln ([n.sub.i]/N) (3)
where [n.sub.i] = the number of specimens for the ith species and N = the total number of specimens in the sample (Magurran & McGill, 2011). Margalef's Diversity Index was calculated as:
DMG = (S-1)/lnN (4)
where S = total number of species in the pooled sample and N = total number of specimens in the sample. Hilsenhoff's Biotic Index was calculated as:
HBI = ([summation] [n.sub.i] [T.sub.i])/N (5)
where [n.sub.i] = the number of specimens for the th genus; [T.sub.i] = the tolerance value for the th genus; and N = the total number of specimens in the sample (Hilsenhoff, 1977; Hilsenhoff, 1987). We derived Holarctic pollution tolerance values for Chironomidae used in the HBI from Barbour et al. (1999) and Ferrington Jr. et al. (2008) (Table S4), because tolerance values are not available for Costa Rica. When available, we used Southeast United States genus values, but for cases where these values were not provided, we used tribe or subfamily values. Percent tolerant genera were calculated as the total genera out of all genera in the pooled samples with a tolerance value above 7. Percent intolerant genera were calculated as the total genera out of all genera with tolerance values below 3.
We identified 228 morphospecies and 70 genera from 17 071 pupal exuviae contained in the samples. Most of the morphospecies (220) and 14 genera are either undescribed or unknown for the pupal life stage. Tanytarsus was the most species-rich genus with 24 morphospecies followed by Polypedilum with 19 morphospecies and Cricotopus with 13 morphospecies (Table S1). The taxa collected included members from five subfamilies/tribes: Pseudochironomini (1.3%), Tanypodinae (3.5 %), Orthocladiinae (26.8%), Tanytarsini (28.4 %), and Chironomini (40.1%). Chironomini had the highest genus richness ranging from 5 to 21 genera per estuary and was followed by Orthocladiinae with 4 to 11 genera per estuary. Pseudochironomi was represented by the lowest genus richness with one genus: Pseudochironomus. The five most abundant genera, Tanytarsus (20.0%), Nanocladius (14.1 %), Polypedilum (12.9%), Cricotopus (9.5 %), and Cladotanytarsus (5.8%), collectively accounted for 62% of all specimens present in the samples (Table S2).
Rio Estrella had the highest species richness, genus richness, and relative abundance (120, 43, and 3 620, respectively). Estero Negro had the lowest species richness, genus richness, and relative abundance (26, 13, 423, respectively) (Fig. 2). Species-accumulation curves reached saturation for Laguna Cuatro, Laguna del Tortuguero, Rio Bananito, and Estero Negro with an average of 80.1% of estimated species collected. However, Laguna Barra del Colorado, Laguna Jalova, Rio Parismina, Rio Pacuare, and Rio Estrella did not reach saturation with an average of 59.7% of estimated species collected (Fig. 3).
Chironomidae tolerance values ranged from 1.7 to 10 ([bar.x] = 6.2) (Table S4). Percent Orthocladiinae ranged from 2.1% to 58.0%, percent Chironomini from 13.4% to 72.6%, percent Tanytarsini from 4.3% to 70.1%, percent tolerant genera from 9.5% to 28.0%, and percent intolerant genera from 0.0% to 9.5%. Total species richness ranged from 26 to 120, Berger-Parker Index of Dominance from 0.136 to 0.636, Shannon's Diversity Index ranged 1.71 to 3.38, Margalef's Diversity Index from 4.13 to 14.52, and Hilsenhoff's Biotic Index from 5.17 to 6.83 (Table 2). Rio Bananito and Rio Estrella scored the highest and Estero Negro the lowest on the following four Chironomidae assemblage metrics: Total Species Richness, Berger-Parker Index of Dominance, Margalef's Diversity Index, and Shannon's Diversity Index. The estuaries ranked in the following order from lowest to highest biotic integrity based on CIBI scores: Estero Negro (15), Laguna Cuatro (44), Laguna Jalova (49), Laguna del Tortuguero (51), Rio Parismina (51), Laguna Barra del Colorado (57), Rio Pacuare (59), Rio Bananito (61), and Rio Estrella (71) (Table 3). The five most abundant genera for the lowest CIBI score (Estero Negro) were as follows: Polypedilum, Tanytarsus, Labrundinia, Dicrotendipes, and Cricotopus. The five most abundant genera for the highest CIBI score (Rio Estrella) were as follows: Tanytarsus, Cricotopus, Polypedilum, Cladotanytarsus, and Cryptochironomus (Table S2).
Typically, chironomids are neglected in biological monitoring of estuarine and coastal ecosystems (ECEs) since species have been reported to typically prefer low salinities and are generally considered freshwater organisms (Pinder, 1986; Williams, 1998). The 228 morphospecies from 70 genera and five subfamilies/tribes demonstrates that rich chironomid communities are not only present in these Costa Rican estuaries, but that species richness was high, similar to results in Kranzfelder and Ferrington Jr. (2016). Putting these results into context, we detected the equivalent of 154% of the reported 148 species of chironomids reported by Watson and Heyn (1992) during their extensive collections from lotic Costa Rican habitats, ranging from small springs to large rivers, and the equivalent of 85% of the reported 266 species of chironomids collected from 13 streams in northwestern Costa Rica (Coffman, de la Rosa, Cumming, & Wilzbach, 1992). Dimitriadis and Cranston (2007) recorded 44 species from 5 735 chironomid larvae collected from an Australian estuary, fewer than the number of species recorded from seven out of the nine estuaries in our study.
Species-accumulation curves reached saturation for four estuaries, including Laguna Cuatro, Laguna del Tortuguero, Rio Bananito, and Estero Negro. Therefore, these results suggest that these estuaries, especially Estero Negro, were sufficiently sampled and our results are based on biotic responses, not differences in sampling effort. However, the species-accumulation curves did not reach saturation for five estuaries, including Laguna Barra del Colorado, Laguna Jalova, Rio Parismina, Rio Pacuare, and Rio Estrella. These five estuaries have moderate to high species richness and abundance. However, it is common for species accumulation curves from sampling to not reach an asymptote in the tropics since species diversity is high and most species are rare. For example, after nearly 30 consecutive years of sampling, an ongoing inventory of a tropical rainforest ant assemblage at La Selva, Costa Rica, has still not reached an asymptote in species richness and tropical biodiversity studies often fall short of revealing the complete species richness for an assemblage (Gotelli & Colwell, 2011). While undetected species remain in these estuaries, we argue that our main results are based on the common species detected. However, we predict that more extensive sampling in these Costa Rican estuaries would further widen the observed species richness gap between the least and most species rich estuaries.
While we collected a high number of species in this study, 125 species were represented by less than 10 specimens, indicating a large number of rare species with low abundances.
This pattern is consistent with other Neotropical studies involving chironomids (Coffman & de la Rosa, 1998; Ferrington Jr., Buzby, & Masteller, 1993; Pringle & Hamazaki, 1998; Souza, Moulton, Silveira, Krsulovic, & Brito, 2007). Coffman and de la Rosa (1998) reported low chironomid densities of less than 5 000 larvae per square meter in Northwestern Costa Rican streams, compared to larval densities ranging up to 50 000 larvae per square meter reported in temperate areas (Lindegaard, 1989). Presence of filter-feeding shrimp or predatory benthic macroinvertebrates and fish could account for the low abundance of chironomids in our study estuaries. Ferrington Jr. et al. (1993) collected 2 451 chironomids from a Puerto Rican stream over the course of one year, and suggested that filter-feeding shrimp could compete with and decrease the number of filter-feeding chironomid genera, like Rheotanytarsus. Pringle and Hamazaki (1998) found that omnivorous fish and shrimp reduced chironomid larval densities in lowland streams of Northeastern Costa Rica.
Our results indicate that the CIBI we have developed can be used to evaluate the surface water quality and physical habitat of Costa Rican estuaries. Individual metrics are derived from an extrinsic model and thus provide a high degree of objectivity to the CIBI. Structed in this manner, the CIBI successfully differentiated between estuaries with very poor biotic integrity (Estero Negro), estuaries with fair biotic integrity (Laguna Cuatro, Laguna Jalova, Laguna del Tortuguero, Rio Parismina, Laguna Barra del Colorado, Rio Pacuare), and estuaries with high biotic integrity (Rio Bananito and Rio Estrella).
These results, based on biotic data, follow the same patterns observed in our study based on coastal watershed land cover data, except Estero Negro (Kranzfelder et al., in review). In that study, we ranked watersheds in the following order from most to least impacted by human land use change, and thus, susceptible to water quality issues: Tortuguero, Reventazon, Chirripo, Pacuare, Bananito, and Estrella (Kranzfelder et al., in review). Estero Negro is in the Bananito watershed; and therefore, based on the land use composition, we would predict high biotic integrity, similar to Rio Bananito and Rio Estrella. However, on average, Estero Negro had low dissolved oxygen (3.0 mg/L) and moderate salinity (9.8 ppt) compared to the estuaries with higher biotic integrity (Table 1). The low dissolved oxygen could be due to high organic matter in the estuary from decomposition of plants or animals and the moderate salinity could indicate a tidally influenced estuary. We believe that for this estuary small-scale environmental factors (i.e., low dissolved oxygen and moderate salinity) had a greater impact on the taxonomic composition of the chironomid community than cumulative coastal watershed land use.
Four metrics showed a large range in scores between the estuary with the highest water quality (Rio Estrella) and estuary with the lowest water quality (Estero Negro): Total Species Richness, Berger-Parker Index of Dominance, Margalef's Diversity Index, and Shannon's Diversity Index. These four metrics were calculated using the species richness and relative abundance of chironomids in the samples. Therefore, Rio Estrella had the highest species richness and relative abundance and scored the highest CIBI score. By contrast, the other six metrics, which are based on the relative abundances of different tribes, relative abundance of tolerant/intolerant genera using literature-based tolerance values, and abundance-weighted average of each genus using tolerance values, did not show as large a range in scores or inconsistent scores between the estuaries with lowest and highest water quality. This was especially evident when we used extrinsic genus-level values for Polypedilum for our initial trials related to the HBI metric. For instance, genus-level tolerance resulted in the lowest HBI score for Rio Estrella, which otherwise had moderate-to-maximum score for the other nine metrics. On closer inspection, we realized that the numerically dominant species of Polypedilum in the estuary differed from numerically dominant species of Polypedilum in the low-ranking estuaries.
In addition to the HBI metric, the remaining tribe- and genus-based metrics did not closely follow the trends of the four species-based metrics. Even though the 70 genera collected have a wide range of generic pollution tolerances from very intolerant (1.7) to very tolerant (10), our results suggest that there are likely to be some additional genera for which species-level differences to pollution should not be generalized by using generic tolerance values. For example, Cladotanytarsus has a tolerance value of 3.7 based on extrinsic data and was very abundant in samples from both Rio Estrella and Laguna Jalova. Recent research in Minnesota on lakes with differing trophic states has revealed that different species of Cladotanytarsus occur in oligotrophic versus hypereutrophic lakes (Ferrington, unpublished data) and should be given different tolerance values. Subsequently, differing intrinsic species tolerances for this genus may need to be developed for use in the estuarine CIBI. For example, Laguna Jalova had a higher number of individuals from this genus than Rio Estrella; and so, Laguna Jalova had an HBI score of 10, while Rio Estrella had an HBI score of only 3. However, the Cladotanytarsus present in Laguna Jalova was a different species than Rio Estrella. Based on the land use in the Tortuguero watershed with 10.4% forest land cover and 28.3% pasture cover, we predict that the Cladotanytarsus sp. present in Laguna Jalova is likely to be highly tolerant of organic enrichment and should have been assigned a higher intrinsic tolerance value than 3.7.
While most species were rare, there were three species-rich and abundant genera, Cricotopus, Polypedilum, and Tanytarsus, which accounted for 42.4% of the total abundance in the samples. Cricotopus and Polypedilum were collected from all nine estuaries and Tanytarsus was collected from all estuaries except Rio Parismina. We recommend that future Costa Rican estuarine biomonitoring studies focus on assessment of individual morphospecies within these three genera and use measurement of specific stressors, like nitrogen and phosphorus, to develop more realistic intrinsic tolerance values of these morphospecies of estuaries of Costa Rica. For example, the species-rich genus Cricotopus has some species within subgenus Cricotopus and subgenus Isocladius that are resistant to many forms of pollution, while there are some species within subgenus Nostococladius that are not resistant to many forms of pollution (Wilson & Ruse, 2005). For genus Polypedilum in the Southeast USA, Polypedilum aviceps has a tolerance value of 4.0, while Polypedilum illinoense has a tolerance value of 9.2 (Barbour et al., 1999) and it can be expected that ranges in tolerance among species in Costa Rica will have similar magnitude.
Thorne and Williams (1997) have argued that genus identifications of benthic macroinvertebrates for bioassessments in tropical countries are rarely possible and propose that use of family-level biotic indices, like the qualitative BMWP-CR (Biological Monitoring Working Party-Costa Rica), is more practical. However, based on our results, we suggest that a multimetric index of biotic integrity focused on Chironomidae at genus-level is useful for evaluating the integrity of ecologically important Costa Rican estuaries. We recommend that future studies evaluate and calibrate regionally accurate genus, and corresponding species-level tolerance values for Cricotopus, Polypedilum, Cladotanytarsus, and Tanytarsus using estuary-specific stressors, such as nutrient enrichment, in order to improve the sensitivity of the CIBI for use in biological monitoring of ECEs in Costa Rica. In addition, morphospecies can be given a described species name by either rearing immature life stages (eggs, larvae, or pupae) to adults with species descriptions (Spies et al., 2009) or using DNA barcodes to link pupal exuviae to described species using approaches refined by Kranzfelder, Ekrem, and Stur (2017).
We would like to thank the Canadian Organization for Tropical Education and Rainforest Conservation, especially staff members Charlotte Foale and Manuel Arias, for assisting us in the research at Cano Palma Biological Station. Also, thanks to Jenna McCullough, Miranda Roberts, Katherine DeGuire, and Katherine Kemmitt for assistance with field and lab work in Costa Rica and the University of Minnesota. We thank Costa Rica's Ministerio del Ambiente y Energia for providing research permits (138-2012-SINAC, 207-2012-SINAC, 74-2013-SINAC, and SINAC-SE-GASP-PIR-001-2014) and export permits (DGVS-6212012, DGVS-027-2013, DGVS-333-2013, and DGVS-011-2014). We also give many thanks for the following funding sources that allowed this work to be completed: National Science Foundation (Grant No. 1114845), University of Minnesota's Global Programs and Strategy Alliance for the Master's, Professional, or Doctoral International Research Grant; University of Minnesota's Bell Museum of Natural History for the Dayton-Wilkie Natural History Fund; University of Minnesota's Graduate School for the Alexander and Lydia Anderson Fellowship and the Doctoral Dissertation Fellowship; and the University of Minnesota's Department of Entomology for the Morris and Elaine Soffer Rockstein Fellowship.
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Petra Kranzfelder (1,2) * & Leonard C. Ferrington, Jr. (1)
(1.) Department of Entomology, University of Minnesota, 1980 Folwell Avenue, 219 Hodson Hall, Saint Paul, MN 55108 USA; email@example.com
(2.) Department of Biology Teaching and Learning, University of Minnesota, 3-133 Molecular Cellular Biology, 420 Washington Avenue SE, Minneapolis, MN 55455 USA; firstname.lastname@example.org
Caption: Fig. 1. Locations of nine estuaries on the Caribbean coast of Costa Rica. Black dot indicates location of sample estuary.
Caption: Fig. 2. Species and genus richness and total abundance of pupal exuviae samples from nine estuaries.
Caption: Fig. 3. Species accumulation curves for (A) Laguna Barra del Colorado; (B) Laguna Cuatro; (C) Laguna del Tortuguero; (D) Laguna Jalova; (E) Rio Parismina; (F) Rio Pacuare; (G) Rio Bananito; (H) Estero Negro; (I) and Rio Estrella. Solid black line indicates mean Chao 1 species-richness estimator and dotted and dashed black lines indicate Chao 1 upper and lower 95% confidence level, respectively.
TABLE 1 Costa Rica study watersheds, estuaries, GPS coordinates, percent forest land cover, and average physiochemical parameters of the water Watershed Estuary Chirripo Laguna Barra del Colorado Tortuguero Laguna Cuatro Tortuguero Laguna del Tortuguero Tortuguero Laguna Jalova Reventazon Rio Parismina Pacuare Rio Pacuare Bananito Rio Bananito Bananito Estero Negro Estrella Rio Estrella Watershed Coordinates % Forest Chirripo 10[degrees]45'54" N & -83[degrees]29'31" W 73.3 Tortuguero 10[degrees]37'58" N & -83[degrees]32'44" W 53.7 Tortuguero 10[degrees]33'30" N & -83[degrees]30'45" W 53.7 Tortuguero 10[degrees]20'51" N & -83[degrees]23'42" W 53.7 Reventazon 10[degrees]18'29" N & -83[degrees]21'19" W 57.9 Pacuare 10[degrees]23'12" N & -83[degrees]17'21" W 75.8 Bananito 9[degrees]52'42" N & -82[degrees]57'43" W 85.4 Bananito 9[degrees]50'45" N & -82[degrees]56'12" W 85.4 Estrella 9[degrees]47'09" N & -82[degrees]53'29" W 91.0 Watershed Water temp. DO Salinity Secchi Chirripo 26.4 6.3 0.1 0.3 Tortuguero 26.4 1.7 7.0 1.1 Tortuguero 26.8 4.7 13.0 0.9 Tortuguero 26.1 5.5 2.0 0.5 Reventazon 25.5 7.1 0.1 0.2 Pacuare 25.4 7.0 0.4 0.5 Bananito 26.4 6.1 3.9 0.6 Bananito 27.5 3.0 9.8 0.5 Estrella 24.7 7.2 3.4 0.3 Coordinates = GPS coordinates in middle zone of estuary sample area, % Forest = percent forest land cover per watershed (data from Kranzfelder et al. (in review)), Water temp. = average water temperature ([degrees]C), DO = average dissolved oxygen (mg/L), Salinity = average salinity (ppt), Secchi = average Secchi depth (m). TABLE 2 Ten community composition metric and diversity and biotic index values for each estuary Estuary % Orth % Chir % Tany % Tol Laguna Barra del Colorado 58.0 34.7 4.3 11.1 Laguna Cuatro 2.3 57.2 38.3 28.0 Laguna del Tortuguero 8.5 13.4 70.1 18.8 Laguna Jalova 10.5 35.1 49.0 22.2 Rio Parismina 46.8 46.5 4.6 9.5 Rio Pacuare 26.5 64.2 8.0 13.2 Rio Bananito 21.9 39.5 33.3 20.9 Estero Negro 2.1 72.6 12.8 23.1 Rio Estrella 27.0 13.4 29.0 14.0 Estuary % Intol richness Berger Shannon Laguna Barra del Colorado 0.0 74 0.38 2.38 Laguna Cuatro 8.0 45 0.24 2.67 Laguna del Tortuguero 9.4 72 0.64 1.89 Laguna Jalova 3.7 55 0.44 2.22 Rio Parismina 9.5 38 0.42 2.00 Rio Pacuare 5.3 84 0.18 3.04 Rio Bananito 2.3 109 0.15 3.38 Estero Negro 0.0 26 0.56 1.71 Rio Estrella 7.0 120 0.14 3.37 Estuary Margalef HBI Laguna Barra del Colorado 9.27 5.74 Laguna Cuatro 6.60 6.33 Laguna del Tortuguero 9.31 6.63 Laguna Jalova 7.52 5.17 Rio Parismina 5.32 5.92 Rio Pacuare 10.85 6.23 Rio Bananito 13.43 6.54 Estero Negro 4.13 6.62 Rio Estrella 14.52 6.83 % Orth = % Orthocladiinae, % Chir = % Chironomini, % Tany = % Tanytarsini, % Tol = % Tolerance, % Intol = % Intolerant, richness = total species richness, Berger = Berger-Parker Index, Shannon = Shannon's Index, Margalef = Margalef's Index, HBI = Hilsenhoff's Biotic Index. TABLE 3 Ten metric scores for each estuary Estuary % Orth % Chir % Tany % Tol Laguna Barra del Colorado 10 7 1 10 Laguna Cuatro 1 3 6 1 Laguna del Tortuguero 1 10 10 6 Laguna Jalova 2 7 7 4 Rio Parismina 8 5 1 10 Rio Pacuare 5 2 1 9 Rio Bananito 4 6 5 4 Estero Negro 1 1 2 3 Rio Estrella 5 6 4 8 Estuary % Intol richness Berger Shannon Laguna Barra del Colorado 1 6 6 4 Laguna Cuatro 8 3 9 6 Laguna del Tortuguero 9 5 1 2 Laguna Jalova 4 4 4 3 Rio Parismina 10 2 5 2 Rio Pacuare 6 7 10 8 Rio Bananito 2 9 10 10 Estero Negro 1 1 2 1 Rio Estrella 7 10 10 10 Estuary Margalef HBI CIBI Laguna Barra del Colorado 5 7 57 Laguna Cuatro 3 4 44 Laguna del Tortuguero 5 2 51 Laguna Jalova 4 10 49 Rio Parismina 2 6 51 Rio Pacuare 7 4 59 Rio Bananito 9 2 61 Estero Negro 1 2 15 Rio Estrella 10 1 71 % Orth = % Orthocladiinae, % Chir = % Chironomini, % Tany = % Tanytarsini, % Tol = % Tolerance, % Intol = % Intolerant, richness = total species richness, Berger = Berger-Parker Index, Shannon = Shannon's Index, Margalef = Margalef's Index, Hilsenhoff = Hilsenhoff's Biotic Index, CIBI = Chironomidae Index of Biotic Integrity score.
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|Title Annotation:||texto en ingles|
|Author:||Kranzfelder, Petra; Ferrington, Leonard C., Jr.|
|Publication:||Revista de Biologia Tropical|
|Date:||Sep 1, 2018|
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