Fourth international Symposium in GIS/Spatial Analyses in fishery and Sciences August 25-29, 2008, Universidade Santa Úrsula, Rio de janeiro, Brazil

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Federal University of Para State Coastal Study

landscape variables: Geoinformatic contribution to

Professor, Federal University of Alagoas State Fourth

1 Federal University of Para State Coastal Study Institute Fishery Engineer Faculty Spatial scales and landscape variables: Geoinformatic contribution to Central Amazonian lake fisheries management Keid Nolan Silva SOUSA, Prof. Dr. Integrated Laboratory of Fishery Bioecology - LAIPE Nídia Noemí FABRÉ, Profa. Dra Vandick da Silva BATISTA, Prof. Dr. Professor, Federal University of Alagoas State Fourth international Symposium in GIS/Spatial Analyses in fishery and Sciences August 25-29, 2008, Universidade Santa Úrsula, Rio de janeiro, Brazil

2 Why are these questions Why are these questions important in the context of the continental Amazon fishery?

Principal rivers; Fishery dynamic fleet;

Geographical complexity with fishery Manaus

White waters high abundance Black waters high

With growing importance the commercial fishery

3 Principal rivers; Fishery dynamic fleet; Historic aspects 1. Geographical complexity with fishery Manaus port centered on the port of Manaus 2. White waters high abundance Black waters high diversity 3. With growing importance the commercial fishery was monitored since 1974, we analyze ten years of this data around Manaus ± tons/year ± fishermen > people consuming fish ±1000km distance A variety of aquatic ecosystems are used for commercial fishery

4 This question is related with another: What is the variety of the aquatic environment available for the commercial fishery? Negro river Manaus city Solimões-Amazon river Lake systems with different geological origins Channel systems rivers, tributaries, t i lateral l floodplain channels Shape complexity

5 1. Aquatic ecosystems complexity of the commercial fishery

6 The relationships between fisheries lakes and their landscapes are not known in the Amazon lake ecosystems exploited by the commercial fisheries. In order to contribute to lake management fisheries in Central Amazonia and delimit ecosystem and territorial management units for fishing lakes in this region, we conducted a geoinformatic analysis using morphometric attributes of the landscape, lake variables and their contribution to fishing yield at different geographical scales. Point views of the study Effect of scales analysis

7 Micro scale - Fishery lake on local point view the lake is the analysis unit - <1: Effect of landscape composition on fish yield Meso scale Fishery lake on large ecosystems point view the landscape ground area is the analysis unit Effect of type of landscape ground areas on fish yield Varzea areas Terra firme areas

8 Database analysis Fishing data Landing data control Geographical data Analogical maps and JERS-1 images Fisheries geoinformation GIS and analysis of a Geoinformation Statistical Analysis

9 Fishery data Capture (tons) Fishing effort (fishermen * fishing day) Yield = Capture / (fishermen * fishing day) Geographical data Localization of fishery areas and locales geographical coordinates Measurement of the aquatic landscape attributes (lakes, rivers and ground areas) km, m, km 2, m 2 Fisheries geoinformation and statistical analysis Spatial fishery distribution based on local name register obtained from landing data control General linear model (GLM) and multivariate analysis (correspondence and principal component analysis) Macro zoning and mapping territorial management units for lake commercial fisheries

10 Micro scale - Fishery lake local point view the lake is the analysis unit - <1: Effect of landscape composition on fish yield

11 Image processing in SPRING Software for extracting landscape data SEGMENTATION THEMATIC MAPPING AND EXTRACTION OF LAKE LANDSCAPE ATRIBUTTES CLASSIFICATION NOT SUPERVISIONED ISOSEG ALGORITHM

12 Lake landscape attributes measured: , ,8 Perimeter lake (km) and Perimeter Development -Distance (km) of the landing Superficial i open port (Manaus) water (km²) Flooded forest area (km²) Perpendicular distance to the principal river (km) ,21 km FONTES DE DADOS: Banco de dados da Estatística pesqueira do estado do Amazonas; Imagem Jers-1-SAR; Maio Julho 1996 (copyright by NASDAMITI) DADOS CARTOGRÁFICOS Sistema de projeção UTM/WGS84 Datum SAD69 Coordenadas Geográficas Legenda

13 JERS 1 Image from rainy season (may July 1996) , ,09 10,93 km , ,58 ELABORAÇÃO Keid Nolan Silva Sousa FONTES DE DADOS: Banco de dados da Estatística pesqueira do estado do Amazonas; Imagem Jers-1-SAR; Maio Julho 1996 (copyright by NASDAMITI) DADOS CARTOGRÁFICOS Sistema de projeção UTM/WGS84 Datum SAD69 Legenda

14 JERS 1 - Image from dry season (September ) , ,09 10,93 km , ,58 ELABORAÇÃO Keid Nolan Silva Sousa FONTES DE DADOS: Banco de dados da Estatística pesqueira do estado do Amazonas; Imagem Jers-1-SAR; Maio Julho 1996 (copyright by NASDAMITI) DADOS CARTOGRÁFICOS Sistema de projeção UTM/WGS84 Datum SAD69 Legenda

15 General linear model - GLM R=0,93; R 2 =0,87; n=20 significance level p<0,05 Fishing yield = -35,99 + fishing effort *0,14 + water open areas *0,06 + flooded forest*0,08 perimeter*0,11 + River connection*0,27 + Perimeter Development*2,84 The principal lakes explored between 1974 to 2001 have similarities considering landscape attributes t Fator 2 (26.75% ): (-) Esforço; Areas Abertas; Distância de Manaus; Conex xão Principal component analysis Janauaca Cheia Rei Arari Anori Beruri Anamã Aiapua Surara Beruri Anamã Anori Janauaca Surara Rei AiapuaArari Coari Coari Badajos Badajos Seca Aiapua Arari Anori Janauaca Beruri Surara Rei Anamã Fator 1 (34.97%):( - ) Cota do rio; Área Alagada; Perimetro; Desenvolvimento perimetral

16 The landscape attributes that affect the fishing yield show that the lakes shown here in white have strong similarities. This indicates the possibility for these lakes to be used as spatial management units at the local scale. Baixo Solimões Médio Solimões Baixo Purus

17 Meso scale Fishery lake on the large ecosystems point of view the landscape ground area is the analysis unit Effect of type landscape ground areas on fish yield Varzea areas Terra firme areas

18 General linear model - GLM R 2 =0.88; n=22, p<0,05 Fishing yield = -15,15 + 8,62*log fishing effort - 0,09*Number of lake exploited+ 0,01*total number of lake + ground area unit factor Non flooded areas (Terra firme) Floodplain areas (Varzea)

19 Spatial fishery distribution based on local name register obtained from landing data control

20 Macro zoning and mapping territorial management units for lake commercial fisheries based on the distance of Manaus port and catch and ground areas Unidade geomorfológica AREAS DE VÁRZEA AREAS DE TERRA FIRME BAIXO SOLIMÕES ALTO AMAZONAS Produção pesqueira (toneladas) , km 100km Lake zone km High Amazonas and lower Solimões Distância de Manaus - Km FONTES DE DADOS: Banco de dados da Estatística pesqueira do estado do Amazonas; Imagem Jers-1-SAR; Maio Julho 1996 (copyright by NASDAMITI) DADOS CARTOGRÁFICOS Sistema de projeção UTM/WGS84 Datum SAD69 QUILÔMETROS

21 Lake zone 2 500km 400km 300km 200km Unidade geomorfológica AREASDEVÁRZEA MÉDIO SOLIMÕES AREAS DE TERRA FIRME Produção pesqueira Catch (toneladas) ( tons) Lake zone 2- ( km) Solimões river Purus lower ,4 12 Distância de Manaus - Km ELABORAÇÃO Keid Nolan Silva Sousa BAIXO PURUS FONTES DE DADOS: Banco de dados da Estatística pesqueira do estado do Amazonas; Imagem Jers-1-SAR; Maio Julho 1996 (copyright by NASDAMITI) DADOS CARTOGRÁFICOS Sistema de projeção UTM/WGS84 Datum SAD69 QUILÔMETROS

22 Territorial management unit A 5 4 A B B 0,22-0, ,22-0,58 Quilômetros FONTES DE DADOS: Banco de dados da Estatística pesqueira do estado do Amazonas; Imagem Jers-1-SAR; Maio Julho 1996 (copyright by NASDAMITI); Toneladaspescador*dias Toneladaspescador*dias pescando pescando 0,003-0,09 0,095-0,22 0,095-0,22 DADOS CARTOGRÁFICOS Sistema de projeção UTM/WGS84 Datum SAD69 Unidade Médio Solimões Unidade Baixo Solimões Manaus km Manaus km Manaus km 0,22-0,58 Manaus km Manaus km

23 C D Quilômetros FONTES DE DADOS: Banco de dados da Estatística pesqueira do estado do Amazonas; Imagem Jers-1-SAR; Maio Julho 1996 (copyright by NASDAMITI); DADOS CARTOGRÁFICOS Sistema de projeção UTM/WGS84 Datum SAD69 Toneladaspescador*dias pescando 0,003-0,09 0,095-0, ,22-0,58 Unidade Alto Amazonas Unidade Baixo Purus Manaus km Manaus km Manaus km Manaus km Manaus km

24 Based on the fishery geoinformation analysis we have demonstrated two contributions At the local scale (lake unit analysis) we have listed landscape variables (management indicators). The principal i lakes exploited in the Central Amazon region were organized based on the landscape variables and management units proposed for future experiments. At the meso scale we have listed other variables with effects on the fishery yield and indicate other management units (lake zone fishing) based on these variables (ecosystems ground areas, and lake numbers for river basin). The spatial distribution of the fishery and the combination of the different river basin attributes allow macro zoning of the Amazonian lakes exploited for Manaus commercial fishery fleet.

Institute - INPA Federal University of Amazonas State - UFAM Integrated Program

25 Acknowledgments Nídia Noemí FABRÉ, Profa. Dra Vandick da Silva BATISTA, Prof. Dr. Professor, Federal University of Alagoas State National Amazon Research Institute - INPA Federal University of Amazonas State - UFAM Integrated Program of Aquatic and Floodplain Resources - PYRÁ Thank you! Contact: nolanks@yahoo.com.br

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