Biodiversity and Conservation (2007) 16:679–692 DOI 10.1007/s10531-005-3742-4
Springer 2007
-1
Floodplain lake fish assemblages in the Amazon River: directions in conservation biology C. GRANADO-LORENCIO1,*, J. LOBO´N CERVIA´2 and C.R.M. ARAUJO LIMA3 1
Departamento de Biologı´a Vegetal y Ecologı´a, Facultad de Biologı´a, Universidad de Sevilla, Avda. Reina Mercedes s/n, Apdo. 1095, 41080 Sevilla, Spain; 2Museo Nacional de Ciencias Naturales, Madrid, Spain; 3Instituto Nacional de Pesquisas da Amazoˆnia-INPA, Manaus, Brasil; *Author for correspondence (e-mail:
[email protected]; phone: +34-954-557067; fax: +34-954-626308) Received 30 March 2005; accepted in revised form 22 September 2005
Key words: Amazon river, Assemblages, Conservation, Fish, Floodplain lakes, Nestedness Abstract. The Neotropical region is renowned for its high biodiversity, and the Amazon River basin contains the highest number of fish species of any river system in the world. In recent years, habitat fragmentation and exploitation of biotic resources have threatened biological integrity and provoked to need for sustainable management and conservation of the Amazon River system. We studied 36 floodplain lakes along 2000 km of the Amazon River. The fish assemblages associated with flood forests are moderately diverse, with low species dominance and reduced populations. To detect nestedness of fish assemblage composition in floodplain lakes, a nested subset analysis was performed on species presence–absence. The incidence matrix (species · lakes) was maximally packed using the Nestedness Temperature Calculator software. The results of ranking lakes and species allow us to establish targets for conservation. Such strategy for sustainable management should be focused on maintaining the Amazonian biodiversity.
Introduction Recognition of the processes that determine the distribution of species in nature is one of the most important challenges in community ecology (Tilman 1999). Various recent papers consider that community assembly is governed by regional or local factors (Hubbell 2001; Wootton 2001). Studies have shown that both situations are equally possible Chase 2003). Biotic nestedness (Atmar and Patterson 1995) is considered a property of communities, not of the species at individual level (Wright et al. 1998). In recent years, scientific production has revolved around improving the measurement method and its empirical application to communities of different historical-evolutionary characteristics, attempting to describe patterns and causes of great interest to conservation biology (Hecnar et al. 2002). The philosophy of this novel macroecological approach (sensu Brown 1995) is to measure the degree (biogeographic temperature or index of nestedness) to which a certain association of species in a fragmented space (sub-region) is a subset of a more diverse region (communities with a common biogeographical
680 history and subjected to a process of natural fragmentation) (Patterson 1990). In a nested distribution, species assemblages in species-poor sites comprise subsets of the species present at richer sites. Among the applied contributions of this approach to conservation planning are detection of regional biodiversity patterns, identification of the species most sensitive to habitat loss, presence of indicative species, identification of geographical areas most impacted by human actions, and recognition of the species in greatest danger of extinction (Kadmon 1995; Hecnar and M’Closkey 1997; Margules and Pressey 2000; Fleishman and Mac Nally 2002; Wethered and Lawes 2005). The deterioration of certain areas of the Amazon, the extinction process observed in different taxonomics groups in recent years (Laurance et al. 2001, Barlow et al. 2002, Laurance et al. 2002) makes it necessary to generate programs of conservation of the species and habitats, built on a scientific basis and able to strengthen the measures to be taken.
Methods Thirty-six flood lakes were studied. Lakes were selected using radar images (www.Nasda.org.jp), based on their surface area (between 20 and 2000 ha) and connection to the Amazon River (straight-line distance) during high water (Figure 1 and Table 1). The field study was conducted in July 2000. We measured profiles of dissolved oxygen concentration (ppm), temperature (C) and conductivity (lS/cm) in open water and flooded forest, every 4 h, during 24 h (Table 2). Captures were obtained only inside the flooded-forest habitat (structural guild sensu Austen et al. 1994), using a set of 12 gill nets 25 m long, 2.5 m high, and with 12 mesh sizes (1.5; 2; 2.5; 3; 3.5; 4; 4.5; 5; 5.5; 6; 7 and 8 cm between opposite knots), during 24 h. We revisited every 4 h to prevent the effect of piscivorous and other predatory aquatic fauna. To reduce seasonal variation of fish assemblages, we used three independent boats and teams, each sampling one sub-Region (Tefe´, Coarı´ and Maue´s). All the samples were taken to the Instituto Nacional de Pesquisas da Amazoˆnia (INPA, Manaus, Brazil). We defined a lake as the local scale, and defined a region as the 36 lakes studied. The local species richness was the number of species found within a single lake, and regional richness was the number of species found within the 36-lakes region. The nestedness index (N0) or biogeographic temperature, proposed by Patterson and Atmar (1986), was used to sort the ‘incidence matrix’ (a ranking of species’ occurrences among habitat sites of a system, with species in columns and lakes in rows) into a state of ‘maximum packing’, which means that the species and sites are arranged such that there is minimum ‘unexpectedness’ in the distribution of species among sites (Maron et al. 2004). However, the nestedness calculator is susceptible to detecting nestedness as an artefact
681
Figure 1. Location of sampling lakes and area in the Amazonas basin.
of passive sampling, overestimating its statistical significance (Fisher and Lindenmayer 2002). The first row is the lake with highest richness, and successive rows decrease in richness. These matrices are a reflection of the relative ‘‘hospitality’’ (the most species-rich lake is the most hospitable) of sites to the species under study and the prevalence of environmental conditions needed to support each species (sic Patterson and Atmar 2000). Those authors developed the Nested Calculator software ‘‘RANDOM1’’ to generate expected values and confidence intervals for the index by 1000 Monte Carlo simulations (to determine whether the data matrix is significantly more-nested than a population of randomised matrices) (Lomolino 1996). A perfectly nested matrix would have a value of T close to 0 (maximum order), and one at random, of 100 (disorder).
Latitude
225¢21.0¢¢ 236¢40.0¢¢ 202¢22.0¢¢ 159¢00.0¢¢ 207¢12.0¢¢ 243¢24.9¢¢ 219¢33.0¢¢ 242¢39.6¢¢ 306¢08.5¢¢ 312¢07.8¢¢
353¢29.4¢¢ 351¢16.2¢¢ 349¢19.2¢¢ 345¢03.4¢¢
Name
Tefe´ sub-Region Tarara´ Campina Curimata´ Miriti Sacambu´ Tamaniqua´ Malvado Uara´ Tracaja´ Alvaraes
Coari sub-Region Nameless Ipixuma Cipotuba Anori
S S S S
S S S S S S S S S S
6235¢11.4¢¢ 6352¢39.6¢¢ 6220¢54.0¢¢ 6141¢03.6¢¢
6630¢10.0¢¢ 6620¢55.0¢¢ 6620¢10.0¢¢ 6602¢38.0¢¢ 6609¢03.0¢¢ 6543¢24.9¢¢ 6621¢03.0¢¢ 6536¢15.6¢¢ 6446¢08.8¢¢ 6450¢00.8¢¢
Longitude
W W W W
W W W W W W W W W W 108 479 272 594
167 233 1889 426 833 219 65 154 151 145
Flood area (ha)
28 345 157 236
121 17 311 40 158 342 55 120 35 77
Dry area (ha)
0.1 1.8 1.8 7.6
0.4 1.8 2.1 2.9 3.3 3.7 8.8 4.7 1.3 2.7
Connexion to river length (km)
Table 1. Name, geographic area, hydrological characteristics, total catches and richness of lakes included in this study.
220 270 470 222
221 133 363 151 442 408 116 134 121 434
Total catch
51 39 68 40
48 29 35 40 36 79 27 41 32 46
Richness
682
355¢28.8¢¢ 355¢21.0¢¢ 336¢25.2¢¢ 338¢35.4¢¢ 336¢30.0¢¢ 345¢05.4¢¢ 354¢18.6¢¢ 353¢29.4¢¢ 354¢21.0¢¢
248¢02.8¢¢ 221¢55.7¢¢ 226¢52.0¢¢ 254¢02.0¢¢ 230¢81.9¢¢ 230¢81.9¢¢ 313¢55.5¢¢ 252¢37.0¢¢ 318¢35.9¢¢ 310¢30.9¢¢ 307¢30.6¢¢ 246¢35.9¢¢ 303¢42.9¢¢
Urucurı´ Grande Saˆo Tome´ Grande Rasgado Jacare´ Acuru Comprido Apaura´ Ajura Luis
Maue´s sub-Region Estasio Carar-ac¸u´ Terra Preta Apunuma Piloto Arari Coro´-coro´ Moana´ Tapereba´ Garc¸as Paracuuba Arroz Buiuc¸u´
S S S S S S S S S S S S S
S S S S S S S S S 5713¢24.5¢¢ 5735¢43.4¢¢ 5738¢13.4¢¢ 5805¢53.6¢¢ 5711¢44.2¢¢ 5711¢44.2¢¢ 5841¢28.7¢¢ 5731¢53.0¢¢ 5840¢01.0¢¢ 5742¢13.0¢¢ 5916¢26.1¢¢ 5759¢39.5¢¢ 5744¢47.0¢¢
6200¢16.8¢¢ 6128¢36.6¢¢ 6109¢30.0¢¢ 6050¢06.0¢¢ 6341¢48.0¢¢ 6332¢31.8¢¢ 6225¢57.6¢¢ 6235¢11.4¢¢ 6248¢09.0¢¢ W W W W W W W W W W W W W
W W W W W W W W W 392 77 1444 327 920 1088 10 1392 755 408 47 673 1333
683 733 142 783 315 559 1031 171 137 607 388 296 138 176 825 14 1156 199 371 13 33 828
674 194 83 103 0 96 737 153 93 1.6 5.0 1.5 0.4 0.8 0.5 4.6 1.4 2.6 4.2 8.6 2.8 2.3
3.2 2.7 1.3 6.1 4.3 4.9 0.7 2.0 1.1 224 62 145 193 226 292 93 177 218 105 88 198 235
343 524 292 168 346 110 218 227 437 54 20 29 44 49 57 15 52 38 35 23 40 51
39 39 35 44 51 15 36 47 37
683
684 Table 2. Lakes ordination in the ‘maximum packing’ matrix. Rows
Lake
Average oxygen concentration (ppm)
Average conductivity (lS)
Average temperature (C)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
Tamaniqua´ Cipotuba Estasio Arari Buiuc¸u´ Nameless Piloto Acuru Tarara´ Moana´ Alvara˜es Jacare´ Apunuma˜ Ajura Uara´ Garc¸as Miriti Urucurı´ -Grande Rasgado Sa˜o Tome´ Grande Arroz Anori Apaura´ Paracuuba Sacambu´ Ipixuna Campina Carar-ac¸u´ Tapereba´ Terra Preta Luis Curimata˜ Tracaja´ Malvado Coro´-coro´ Comprido
2.27 2.27 1.64 1.91 3.12 2.68 2.79 1.32 2.86 4.19 3.56 1.23 2.94 1.67 1.93 4.53 1.48 3.14 2.51 0.68 1.83 0.53 3.80 2.35 1.36 2.13 0.22 3.16 1.61 1.15 0.30 2.46 1.91 2.68 0.67 0.49
71 93 239 47 113 96 56 29 120 206 78 39 31 87 25 92 70 90 84 90 38 78 93 12 48 75 91 21 25 75 99 88 71 124 25 68
26.3 28.0 27.6 27.7 28.6 27.5 28.4 26.9 25.8 28.8 27.5 27.7 27.8 27.7 26.9 29.1 26.8 28.3 27.2 27.3 27.3 26.6 27.6 27.7 26.7 26.2 25.6 26.2 28.2 27.5 27.0 27.3 26.5 26.8 27.8 26.7
A perfectly nested matrix is defined as one where every site contains a proper subset of the species in all of the sites above it. The most species-rich lake is placed in the top row of the matrix (rows are ranked by decreasing species richness). The more-widespread species are located in the left columns of the matrix. The more-restricted species occupy the upper-right area of the matrix. A correlation analysis was performed using the Spearman Rank Coefficient between the position of the lake in the ‘‘maximum-packing’’ matrix and the
685 independent factors: surface area, connection to the river, and average oxygen concentration, water temperature, and conductivity. Results In the 36 studied lakes, we captured a total of 195 species, belonging mainly to the orders Characiformes and Siluriformes. The represented families were Cichlidae, Doradidae, Pimelodidae, Characidae, Serrasalmidae, Curimatidae, Anostomidae, Loricaridae, Clupeidae, and Erythrinidae. The genus with the highest number of species was Serrasalmus (9), followed by Hemiodus (7), Ageneiosus and Leporinus (5). There was no relationship between local richness and lakes size (r = 0.16). The species matrix presented a temperature of 28.46. The ranking of the matrix shows Tamaniqua´ as the most-hospitable (-rich) lake, followed by Cipotuba, Estasio, and Arari. The least-hospitable were Comprido and Coro´coro´ (Table 2). The species with largest geographical distribution were Triportheus albus, Pygocentrus nattereri, Parauchenipterus galeatus, Triportheus elongates, and Mylossoma duriventre. These species occupying the fewest places, in increasing order from the fewest, are: Rhabdolichops caviceps, Loricariichthys nudirostris, Pachipops trifilis, and Thoracocharax securis (Table 3). The correlations between the position of the lake in the matrix (row) and the minimum size of the lake (dry period), maximum size (food area), connection to the river, local richness, and average oxygen concentration, conductivity, and temperature show different significant values. The highest correlations are obtained with the local richness (r = 0.8620; p £ 0.001); and the lowest with the distance to the river (r = 0.338; p £ 0.05). No significant relationships were obtained with the Shannon–Wiener Diversity Index, Evenness Index, size of the lake, or average oxygen concentration, conductivity, and temperature. The local richness showed a significant negative relationship with the distance to the river (r = 0.427; p £ 0.01). Discussion The functioning of floodplain lakes is affected by the hydrological pattern of the river (Flood Pulse Concept, Junk 1980; Junk et al. 1989). The connection with the river (present in all the studied lakes) is a temporary homogeneity element of fish assemblages, and especially of the migratory species. In a geographical approach, the effect of migratory fish species was of similar intensity in all the lakes. Other factors that might also affect the absence of certain relationships observed (e.g. richness vs. lake size) are the volume of water remaining in the lake during drought, massive death caused by oxygen deficiency, and the moment of colonisation of the lakes by the species of the region, among others.
686 Table 3. Species position in the ‘‘maximum packing’’ matrix. Columns 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50
Triportheus flavus Pygocentrus nattereri Parauchenipterus galeatus Triportheus elongatus Mylossoma duriventre Schizodon fasciatum Potamorhina altamazonica Acestrorhynchus falcirostris Raphiodon vulpinus Serrasalmus rhombeus Potamorhina latior Brycon cephalus Rhytiodus microlepis Serrasalmus elongatus Cichla monoculus Colossoma macropomum Triportheus albus Chalceus erythrurus Pellona castelnaeana Serrasalmus sp2 (robertsoni) Plagioscion squamosissimus Osteoglossum bicirrhosum Semaprochilodus insignis Anodus elongatus Hemiodus microlepis Psectrogaster amazonica Laemolyta proximus Hoplias malabaricus Ageneiosus ucayalensis Pimelodus blochii Hypophthalmus edentatus Rhytiodus argenteofuscus Plagioscion montei Callichthys callichthys Auchenipterus ambyiacus Psectrogaster rutiloides Prochilodus nigricans Leporinus friderici Hydrolycus scomberoides Ageneiosus brevifilis Roeboides myersi Brycon melanopterus Liposarcus pardalis Heros sp Hypophthalmus marginatus Mylossoma aureum Myleus rubripinnis Curimata knerii Chaetobranchus semifasciatum Leporinus fasciatus
687 Table 3. Continued Columns 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100
Astronotus ocellatus Potamorhina pristigaster Astronotus crassipinnis Dekeyseria amazonica Semaprochilodus taeniurus Pristobrycon serrulatus Nemadoras humeralis Mesonauta insignis Acestrorhynchus microlepis Tatia intermedia Acarichthys heckelii Serrasalmus spilopleura Leporinus trifasciatus Hoplosternum littoralis Hypophthalmus fimbriatus Loricariichthys acutus Pseudoplatystoma tigrinum Dianema longibarbis Anodus orinocensis Acestrorhynchus falcatus Hemiodus immaculatus Sternopygus macrurus Glyptoperichthys gibbiceps Cynodon gibbus Auchenipterichthys thoracatus Piaractus brachypomus Pterodoras lentiginosus Pseudorinelepis genibarbis Pseudoplatystoma fasciatum Hemiodus argenteus Megaladoras uranoscopus Laemolyta taeniatus Ancistrus dolichopterus Hemiodus sp1 (microlepisL) Trachelyopterichthys taeniatus Serrasalmus eigenmanni Anadoras weddellii Ageneiosus brevis Lycengraulis grossidens Satanoperca jurupari Metynnis hypsauchen Centromochlus heckelii Metynnis argenteus Dianema urostriatum Hemiodus unimaculatus Hoplerythrinus unitaeniatus Bryconops giacopinii Agamyxis pectinifrons Geophagus proximus Apteronotus hasemani
688 Table 3. Continued Columns 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150
Chaetobranchus flavescens Pirinampus pirinampu Tatia sp Loricariichthys maculatus Uaru amphiacanthoides Curimata inornata Gymnotus carapo Ageneiosus sp Hypselecara temporalis Boulengerella maculata Oxydoras niger Acaronia nassa Nemadoras hemipeltis Serrasalmus altispinis Metynnis maculatus Bryconops alburnoides Apteronotus albifrons Calophysus macropterus Cichla temensis Metynnis luna Cyphocharax abramoides Hypostomus hoplonites Leporinus amazonanum Auchenipterus britskii Curimatella alburna Sternopygus obtusirostris Megalechis thoracatum Hypostomus sp1 Paulicea luetkeni Stichonodon insignis Geophagus altifrons Hemiodus ternetzi Pterophyllum scalare Rhamphichthys sp Crenicichla reticulata Crenicichla sp Hypoptopoma gulare Sternarchella schotti Curimata vittata Pseudanos trimaculatus Curimatella meyeri Auchenipterus nuchalis Ilisha amazonica Satanoperca acuticeps Pristigaster cayana Curimata ocellata Triportheus culter Pygopristis denticulatus Astyanax sp Cichlasoma amazonarum
689 Table 3. Continued Columns 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195
Leporinus affinis Tetragonopterus chalceus Boulengerella lucia Arapaima gigas Pseudanos gracilis Thoracocharax stellatus Serrasalmus compressus Catoprion mento Charax sp Satanoperca daemon Agoniates anchovia Serrasalmus manueli Rhamphichthys marmoratus Satanoperca lilith Aequidens sp Hypostomus sp2 Amblydoras affinis Dekeyseria scaphirhyncha Pimellodina flavipinnis Eigenmannia sp Crenicichla joana Acestrorhynchus heterolepis Sternarchella orthos Auchenipterus sp. Leiarius pictus Serrasalmus sp1 Stethaprion erythrops Hemiodus atranalis Moenkhausia grandisquamis Pellona flavipinnis Ageneiosus atronasus Symphysodon aequifasciata Sorubim lima Eigenmannia limbata Crenicichla lugubris Ctenobrycon hauxwellianus Argonectes longiceps Opsodoras cf stuebelii Rhabdolichops caviceps Eigenmannia virescens Colomesus asellus Pristobrycon sp. Thoracocharax securis Pachypops trifilis Loricariichthys nudirostris
690 The results obtained in our study characterise the fish assemblages associated with flooded forests as moderately diverse compared with those blackwatercreeks, with low species dominance and reduced populations. The mean richness of the flooded-forest lakes studied was less than that observed in fluvial tracts of the Amazon basin (between 80 and 256 species) (Araujo Lima et al. 1998) and greater than in isolated lagoons (between 7 and 26) (Castello´ et al. 1987). Our results are similar to those from other studies carried out in flood lakes. Meschiatti et al. (2000) found 31 species in two small lakes (3 and 6.9 ha), in the basin of the Parana´, less than the richness in the main channel (84 species). Rodrı´ guez and Lewis (1990), studying 20 lakes, in the basin of the Orinoco River, gave a mean richness of 35 (between 8 and 65). In both these studies, and in others (Galacatos et al. 1996; Vono and Barbosa 2001), the Characiformes and Siluriformes are dominant. Lakes that present a greater richness are located in quite homogeneous areas: Tamaniqua´ (79 species, Tefe´ Sub-region), Cipotuba (68, Coarı´ Subregion) and Estasio (54, Maue´s Sub-region). These lakes have 62% of the species of the overall study. Furthermore, within the variety of lakes with different degree of isolation from the main channel and sizes, there does not seem to be a great deal of differentiation in their faunas. The particular position in the ‘‘maximum-packing’’ matrix allows us to recognise a species pool of critical extinction processes that needs a greater effort for protection. As the rare-species usually inhabit the richest lakes, protection measures should go towards protecting a few of these lakes, thereby preserving the greatest possible biodiversity. Lake assemblages follow a nested distribution pattern. The nestedness approach could be a useful methodology for fish floodplain lake conservation, identifying locations and species as good or poor candidates for conservation efforts (sic Maron et al. 2004). In this sense, it can be considered a simple tool for decision-making. The conservation effort could target the richest lakes. There should be a degree of flexibility in this strategy to allow for local factors (fishing, tourism, etc.) and because the lakes are so well-nested, the conservation biology strategy should have leeway to accommodate the diverse uses. Acknowledgements The study is part of the Brazilian Rainforest Conservation Programme supported by the Agencia Espan˜ola de Cooperacio´n Internacional (AECI-Brasil) and the Instituto Nacional de Pesquisas da Amazoˆnia (INPA-Brazil). References Araujo Lima C.A.R.M., Piedade M.T. and Barbosa F. 1998. Water as a major resource of the Amazon. In: Davies de Freitas M.L. (coord). Amazonia. Heaven of a New World. Ed. Campus, Rio de Janeiro, Brasil, pp. 55–70.
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