Description

Neotropical Ichthyology, 7(4): , 2009 Copyright 2009 Sociedade Brasileira de Ictiologia Statistical distribution models for migratory fish in Jacuí basin, South Brazil Thaís P. Alves and Nelson F.

Information

Category:
## Fashion

Publish on:

Views: 11 | Pages: 12

Extension: PDF | Download: 0

Share

Transcript

Neotropical Ichthyology, 7(4): , 2009 Copyright 2009 Sociedade Brasileira de Ictiologia Statistical distribution models for migratory fish in Jacuí basin, South Brazil Thaís P. Alves and Nelson F. Fontoura The aim of the present study was to identify the distribution patterns of migratory fishes in the Jacuí river basin (Rio Grande do Sul, South Brazil), proposing a statistical model of presumed distribution based on geomorphologic environmental data. Through maps of occurrence probability, we hope to contribute to decisions regarding basin environmental management. The analyzed species were: Salminus brasiliensis (dourado), Leporinus obtusidens (piava), Prochilodus lineatus (grumatã) and Pimelodus pintado (pintado). Samples were made through interviews with fishermen and local inhabitants, covering the main channel and tributaries of the rivers Jacuí, Taquari-Antas, Vacacaí, Vacacaí-Mirim, Pardo, Pardinho, Sinos, and Caí. The sampling program resulted in 204 interviews, being 187 considered as valid in 155 different sampling points. The probability of migratory fish occurrence was adjusted through the LOGIT routine of the Idrisi Andes Software: P = e (b0 + b1. altitude + b2. basin area). (1 + e (b0 + b1. altitude + b2. basin area) ) -1, where P is the occurrence probability of the species (0-1) and b 0, b 1 and b 2 are the equation parameters. Model accuracy, for estimating presence, ranged from 82% to 93%. Pimelodus pintado was cited to occur in 121 points among the 155 sampled (78.06%), Prochilodus lineatus in 72 (46.45%), L. obtusidens in 62 (40.00%) and S. brasiliensis in 58 (37.42%). Equation parameters were estimated (± standard error) as follow: S. brasiliensis: b 0 = ± ; b 1 = ± ; b 2 = ± ; L. obtusidens: b 0 = ± ; b 1 = ± ; b 2 = ± ; Prochilodus lineatus: b 0 = 0; b 1 = ± ; b 2 = ± ; Pimelodus pintado: b 0 = ± ; b 1 = ± ; b 2 = ± O objetivo do presente estudo foi identificar o padrão de distribuição de peixes migradores da bacia hidrográfica do rio Jacuí (Rio Grande do Sul, Sul do Brasil), propondo um modelo matemático de distribuição presumida baseado em parâmetros ambientais geomorfológicos. Através de mapas de probabilidade de ocorrência, espera-se contribuir para a tomada de decisões relacionadas ao gerenciamento desta bacia hidrográfica. As espécies analisadas foram: Salminus brasiliensis (dourado), Leporinus obtusidens (piava), Prochilodus lineatus (grumatã) e Pimelodus pintado (pintado). As amostras foram obtidas a partir de entrevistas com pescadores e moradores locais, percorrendo-se a calha principal dos rios Jacuí, Taquari-Antas, Vacacaí, Vacacaí-Mirim, Pardo, Pardinho, Sinos e Caí. O programa de amostragens resultou em 204 entrevistas, sendo 187 consideradas como válidas em 155 pontos diferenciados. A probabilidade de ocorrência de peixes migradores foi ajustada utilizando-se a rotina LOGIT do software Idrisi Andes: P = e (b0 + b1. altitude + b2. área de bacia). (1 + e (b0 + b1. altitude + b2. área de bacia) ) -1 ; onde P é a probabilidade de ocorrência da espécie (0-1) e b 0, b 1 e b 2 são os parâmetros da equação. Pimelodus pintado foi citado como presente em 121 pontos dentre os 155 amostrados (78.06%), Prochilodus lineatus em 72 (46.45%), L. obtusidens em 62 (40.00%), e S. brasiliensis em 58 pontos (37.42%). A precisão do modelo, para a presença estimada, ficou entre 82% e 93%. Os parâmetros estimados da equação são descritos a seguir: S. brasiliensis: b 0 = ± ; b 1 = ± ; b 2 = ± ; L. obtusidens: b 0 = ± ; b 1 = ± ; b 2 = ± ; Prochilodus lineatus: b 0 = 0; b 1 = ± ; b 2 = ± ; Pimelodus pintado: b 0 = ± ; b 1 = ± ; b 2 = ± Key words: Salminus brasiliensis, Leporinus obtusidens, Prochilodus lineatus, Pimelodus pintado, Generalized Linear Models. Introduction More than 15% of the Neotropical fish fauna is composed by migratory species (Carolsfeld et al., 2004), which may need to cover several kilometers to stimulate gonadal maturation (Godoy, 1987). The watershed area for maintaining most of these species could reach km 2 (Godoy, 1987). Spawning use to happen upstream the adult feeding areas, and stream current carries the eggs and larvae to areas where they will develop. In these sites, juveniles will feed and grow until they become big enough to join the main stock (Pitcher & Hart, 1982). Malabarba (1989) presents a list of species of freshwater fish of Patos Lagoon drainage, which includes the Jacuí River. Following Agostinho & Júlio (1999) and Vazzoler et al. (1997), the migratory species for Jacuí basin are: Salminus brasiliensis Faculdade de Biociências, Pontifícia Universidade Católica do Rio Grande do Sul (PUCRS). Av. Ipiranga, 6681, Caixa Postal 1429, Porto Alegre, RS, Brazil. (TPA), (NFF) 647 648 Statistical distribution models for migratory fish (Cuvier, 1816) (dourado); Prochilodus lineatus (Valenciennes, 1836) (grumatã); Leporinus obtusidens (Valenciennes, 1836) (piava); and Pimelodus pintado Azpelicueta, Lundberg & Loureiro, 2008 (pintado). The spotted catfish of the Patos basin was known until recently as Pimelodus maculatus Lacépede, 1803, a species with wide distribution in neotropics and maybe a species complex. After Azpelicueta, Lundberg & Loureiro (2008) the Patos spotted catfish was reviewed and described as Pimelodus pintado, with few information concerning migratory or reproductive biology but maybe with similar biological patterns as Pimelodus maculatus. Nevertheless, including Pimelodus pintado as a migratory fish species is still controversial. Braun (2005) suggests that the species seems to reproduce in little channels, tributaries of Patos Lagoon. This information is reinforced by the occurrence of Pimelodus pintado in Barros lagoon, which receives water from only small creeks of first order, suggesting no dependence of large scale migration for reproductive purpose. However, considering the importance of this species for artisanal fishery, Pimelodus pintado will be included among the compulsory migrant species of the Jacuí River basin. A river is a complex system, with continuous changing structure from upstream to lower areas. The species richness increases downwards (Reyes-Gavilán et al., 1996) and in higher altitudes both physical and chemical conditions are more stressful, with a lower number of species showing physiological and ecological capacities to survive in such places (Matthews & Styron-Júnior, 1981). As a general rule, the distributional limit of a species is driven by several factors, which combine or interact to create the patterns found in nature (Hall et al., 1992). The altitude, for example, seems to be broadly related to hydrodynamics and morphology of a river. With the combined factors such as water temperature, conductivity, ph, current speed, declivity, and the presence of pools and waterfalls, altitude is capable of creating an environmental gradient that exerts influence on the species richness in each region (Pouilly et al., 2006). Araújo & Tejerina-Garro (2009), found that ph, water velocity, channel width and water temperature strongly affected fish assemblages in upper Paraná tributaries. Using geomorphologic data, Súarez & Petrere-Júnior (2007) suggested altitude as the most important structuring factor to fish community in the Iguatemi River basin. Apart from altitude, the water volume drained to a specific point seems to exert a large influence on the fish distribution patterns (Garutti, 1988). When the altitude decreases, there is an increase in the width and deep of the river, allowing the survival of bigger fish species (Vázquez et al., 1979). Brazil has approximately 4360 dams, not taking into account homemade barriers and/or non-registered ones (Silveira & Cruz, 2005). Currently, 90% of the Brazilian electric power production is generated by hydroelectric turbines (Petrere- Júnior et al., 2002). Hydroelectric plants have been interfering in fish migration all around the world. Knowing the longitudinal distribution of migratory fishes is an important tool to support decisions related to dams planning in a regional scale. In this respect, predicting species occurrence through a modeling approach based on geographical information system (GIS) represents a useful methodological tool (Pearce & Ferrier, 2000b). Generalized linear models (GLM), implemented within a GIS are especially useful as it can be applied with many types of predictors (continuous, binary, qualitative, and ordinal), and the distribution of the species should comprise just presence and absence data (Syartinilia & Tsuyuki, 2008), information available through interviews. These models are then applied to extrapolate the probability of occurrence of species across the entire region of interest (Pearce & Ferrier, 2000b). Although several ecological factors are known to influence fish distribution patterns, access to such data is not easy or even accurate at large areas. In a large regional scale, the use of simple factors derived from GIS, as altitude and basin area, seems to be enough for understanding distributional patterns along the rivers (Matthews et al., 1992; Pouilly et al., 2006). The present study aims to identify the longitudinal historical distribution pattern of long distance migratory fish in the hydrographic basin of the Jacuí River, which already has an installed hydroelectric production of 1.428,674 MW, and a potential, still not installed one, of 271,004 MW. Apart from presenting distribution maps for long distance migratory fish, probabilistic distribution models for each species will also be proposed, using altitude and basin area as main presence predictors. Material and Methods Eight field expeditions were carried out, covering the most important rivers draining to the Jacuí basin: Taquari-Antas, Vacacaí/Vacacaí-Mirim, Pardo/Pardinho, Sinos and Caí, as their main tributaries. The samplings were performed between February and July of Expeditions were carried out making use of the road network and its crossing points with the rivers, resulting in 155 different sampling points (Fig. 1). At each sampling site, local fishermen and oldest residents were searched and interviewed. With the aid of a board containing a picture of interest species, each interviewed person was asked about their knowledge of the fishes, and then, their presence or absence in that specific river segment (present or historical). In case of more than one interview at the same sampling site, only the most frequent information or consensus was registered. The project totalized 204 interviews, 187 registered as useful in 155 different points. Also, a survey in the main Brazilian fish collections was done by using the SIBIP/NEODAT III database (MCP, MNRJ, MZUSP, UFRGS); besides literature information (Petry & Schulz, 2006). This survey was extended through the analysis of Environmental Impact Reports (EIA-RIMA) produced for the licensing power-dams (UHE) in the Jacuí and Taquari- Antas basins (Dona Francisca; 14 de Julho, Castro Alves, Monte Claro). Data analysis and cartographic products were performed T. P. Alves & N. F. Fontoura 649 using Idrisi Andes software (Clarck Labs, 2006) and a digital elevation model (DEM, radar altitude in pixel of 92.6 per 92.6 m) for the Brazilian official reference system (SAD 69; Labgeo, 2006). The hydrographic matrix was obtained through four basic steps: (1) DEM homogenization by applying the Filter Min 3x3 pixels; (2) defining pathways with monotonically decreasing attitudes through Pit Removal tool; (3) by applying the Runoff tool to estimate the upstream catchment area for each pixel, correcting pixel number for the real area (km²) by multiplying pixel values by ; (4) converting each pixel with catchment area smaller then 10 to zero and bigger then 10 into one, and then creating a hydrographic matrix with watershed bigger then 10 km². Hydrographic matrix with fish presence, for each species, was created through Pathway tool, using recorded presence (GPS coordinates) as Target Image and the DEM as Cost Surface. Mask for data analysis was obtained by using also the Pathway tool, with upstream sampled coordinates as Target Image and the DEM as Cost Surface. The occurrence probability for each migratory fish species throughout the hydrographic matrix was estimated using the Fig. 1. Sampling sites (n = 155) covering the main channel and tributaries of the rivers Jacuí, Taquari-Antas, Vacacaí, Vacacaí- Mirim, Pardo, Pardinho, Sinos and Caí. Hydrographic map generated through a digital elevation model (DEM, radar altitude in pixel of 92.6 per 92.6 m) for the Brazilian official reference system (SAD 69; Labgeo, 2006). 650 Statistical distribution models for migratory fish Multinomial Logistic Regression routine (Logisticreg, adjust by maximum likelihood through the Newton-Raphson algorithm): P (X = 1) = e (b0 + b1. altitude + b2. basin area) (b0 + b1. altitude. (1 + e + b2. basin area) ) -1, where P (X = 1) is the occurrence probability of the species and b 0, b 1 and b 2 are the equation parameters. The measured variables, the measurement scale and data source is described in Table 1. Data analysis was performed in two steps. First, the probability model for each species was estimated using data from 175 interviews from 150 different points, including literature, EIA-RIMA and museum data. A residual map was generated showing differences among observed presence (0/1) and predicted presence probability. Sampling points with residuals larger then 0.5 were revisited for new interviews. During this new field trip, additional 29 interviews and five new points were introduced into de original data matrix for new model processing. Adherence between informed distribution and presumed distribution were estimated as the percentage of the river segments with presence/absence informed as correct, according to the presumed distribution model (considered as presumed presence probabilities higher than 0.5 and as presumed absence probabilities lower than 0.5). Considering that the Logisticreg function of the Idrisi Andes software does not estimate the standard errors of the adjusted parameters, 10 independent adjustments were carried out for each species through random selection of 10% of the pixels from de sample matrix. The standard errors were calculated as the standard deviation of the different estimates for each parameter, obtaining the significance thorough the Wald statistic, the ratio between the estimated parameter and its standard deviation, calculating P through the Z distribution (Tabachnick & Fidell, 1996). in 58 (37.42%). Is shown in Fig. 2 the cumulative frequency of informed occurrence as a function of altitude and catchment area. Results were similar for all the species but Pimelodus pintado. As identified, Prochilodus lineatus, L. obtusidens and S. brasiliensis have an altitudinal threshold of about m whereas Pimelodus pintado was recorded in altitudes exceeding 500 m (one informed occurrence at 680 m). Occurrences, concerning basin area, were recorded for all species in river segments with catchments over 20 km². The estimated parameters for the probability model for each species are shown in Table 2. Table 3 presents the adherence between informed distribution and presumed distribution. For each analyzed species, Figs. 3 to 6 show (a) hydrographic map of the informed occurrence through the interviews, as well as the registered occurrence points in literature and museums; (b) probability map of the presumed occurrence through the adjusted logistic model to the whole hydrographic basin with a catchment area larger than 10 km 2 ; Table 1. Measured variables, measurement scale and data source for probability distribution models adjustment for migratory fishes at Jacuí basin (Brazil). Variable Measurement Scale Data Origin Presence in catalogues Presence (per specie Binary: Records in literature of migratory fish) present (1); absent (0) Interviews Altitude Metric (natural logarithm Digital Elevation Model of the altitude in meters) Catchment area Metric (natural logarithm of the basin area in km 2 Digital Elevation Model ) Results Search in available information (museums, literature, EIA- RIMA) resulted in just five occurrences for S. brasiliensis, 13 for Prochilodus lineatus, 16 for L. obtusidens and 26 for Pimelodus pintado. Pimelodus pintado was present in 121 points among the 155 sampled (78.06%), Prochilodus lineatus in 72 (46.45%), L. obtusidens in 62 (40.00%) and S. brasiliensis Fig. 2. Inverse cumulative frequency of informed occurrence of migratory fish of Jacuí basin according to an upstream gradient of altitude (m) and basin catchment (km²). T. P. Alves & N. F. Fontoura 651 Table 2. Average, Standard Deviation, Wald and significance of the coefficients (Intercept, b 0 ; altitude, b 1 ; and basin area, b 2 ) estimated from ten independent adjustments of the logistic regression performed using the software Idrisi Andes to each of the four migratory fish species in Jacuí basin (Brazil). S.D. = Standard Deviation. Regression Coeficients Species Intercept Altitude (m) Basin Area (km 2 ) Average S.D S. brasiliensis Wald p Average S.D Prochilodus lineatus Wald p Average S.D L. obtusidens Wald p Average S.D Pimelodus pintado Wald p Table 3. Mean accuracy percentage (adherence) of the presumed occurrence model for each one of the four migratory fish species in Jacuí basin (Brazil). S. brasiliensis P. lineatus L. obtusidens P. pintado Presence adherence (%) Absence adherence (%) and (c) distribution map of residuals, highlighting points in which the difference between informed occurrence and estimated probability was higher than 0.3 (33%). Considering the effects of dams as barriers to fish migratory movements, Fig. 7 presents the cumulative distribution of large and small power dams of the Jacuí basin (n = 24, data from National Electric Power Agency - ANEL) according to an altitudinal gradient. As could be identified, around 50% of registered dams are present above the altitudinal threshold of m. Figure 8 presents the geographical distribution of power dams (yellow spots) in the Jacuí basin according to altitudinal categories of under 300 m (red river segments) and over 300 m (green river segments). Inspecting the spatial distribution, it becomes clear that most of the constructed power dams are located above or near the altitudinal threshold of m of upper migratory fish distribution, suggesting that most operating power dams presents little effect as barriers for upstream movements for migratory fish. Discussion In the last twenty years, the use of multivariate statistic in the modeling of species distribution has increased through the application of a large variety of techniques. Particularly, the regression models have been broadly used to predict distribution, abundance and habitat preference of species (Brito et al., 1999). However, when this information is joined with Geographic Information Systems (GIS), it is possible to incorporate ecological factors directly obtained from remote images to prediction models. Also, indirect gradients constituted by variables that do not show direct physiological action, as altitude, declivity and topographic position, are easier to be obtained than environmental parameters of direct gradient, such temperature or ph, which often introduce spatial uncertainties due to the lack of data and interpolation bias (Guisan et al., 1999). Although in the present work we have opted to model distribution based just in two parameters of indirect gradient, altitude and catchment area, the predicted distribution, when confronted with the informed distribution, always produced an adherence ratio higher than 80%. The combined effect of both variables suggest that altitude is the main factor limiting migratory fish distribution for areas comprising the Serra Geral (altitudinal areas) whereas catchment a

Related Search

Species Distribution ModelsEmergent funding models for journalismVirtual 3D City Models for Urban PlanningModels for the AeneidMathematical Models for Social SciencesGlobal Digital Elevation Models for HydrologyBusiness Models for JournalismStatistical Analysis Techniques for GeographyHealthcare Models for ElderlyStatistical Modeling for Finances

Similar documents

We Need Your Support

Thank you for visiting our website and your interest in our free products and services. We are nonprofit website to share and download documents. To the running of this website, we need your help to support us.

Thanks to everyone for your continued support.

No, Thanks