scholarly journals The fish community of the Sorocaba River Basin in different habitats (State of São Paulo, Brazil)

2009 ◽  
Vol 69 (4) ◽  
pp. 1015-1025 ◽  
Author(s):  
WS. Smith ◽  
M. Petrere Jr. ◽  
W. Barrella
Keyword(s):  
River Basin ◽  
Fish Assemblage ◽  
Standard Length ◽  
Fish Community ◽  
Species Abundance ◽  
Sao Paulo ◽  
Abundance Distribution ◽  
Abiotic Variables ◽  
Species Abundance Models ◽  
Dry And Rainy Seasons

A fish assemblage study was accomplished in different habitats of the Sorocaba River Basin. Fish were caught with gillnets, were weighed (weight total - g) and measured (standard length - mm). Several abiotic variables of selected sampling sites were measured in order to characterise their habitats in order to attempt establishing correlations with fish community traits. Fish numbers per species were adjusted to the lognormal and logseries species/abundance models The fish community totaled 38 species, distributed in 28 genera, 14 families and 4 orders. Diversity was calculated both in number and in weight and both presented higher values in better preserved sites. We did not detect any statistical differences between dry and rainy seasons. We also concluded that the abundance distribution was not influenced by abiotic variables.

scholarly journals Towards combining occurrence and abundance distribution models of Great Bustard for conservation: A global research template from Bohai Bay?

2017 ◽  
Author(s):  
Chunrong Mi ◽  
Falk Huettmann ◽  
Rui Sun ◽  
Yumin Guo
Keyword(s):  
Species Abundance ◽  
Distribution Patterns ◽  
Bohai Bay ◽  
Abundance Distribution ◽  
Great Bustard ◽  
Distribution Models ◽  
A Priority ◽  
Species Abundance Models ◽  
Combine Information ◽  
Protection Index

Species distribution models (SDMs) have become important and essential tools in conservation and management. However, SDMs built with count data, commonly referred to as species abundance models (SAMs), are still less used so far. SDMs are increasingly used now in conservation decisions, whereas SAMs are still not widely employed. Species occurrence and abundance do not frequently display similar patterns, often they are not even well correlated. This leads to an insufficient or misleading conservation. How to combine information from SDMs and SAMs all together for unified conservation remains a challenge. In this study, we put forward for the first time a priority protection index (PI). The PI combines the prediction results of occurrence and abundance models. We used the best-available presence and count records for an endangered farmland species, Great Bustard (Otis tarda dybowskii) in Bohai Bay, China, as a case study. We then applied the advanced Random Forest algorithm (Salford Systems Ltd. implementation), a powerful machine learning method, with eleven predictor variables to forecast the spatial occurrence as well as the abundance distribution. The results show that the occurrence model had a decent performance (ROC: 0.77) and the abundance model had a RMSE 26.54. It is of note that environmental variables influenced bustard occurrence and abundance differently. We found that occurrence and abundance models display different spatial distribution patterns. Still, combining occurrence and abundance indices to produce a priority protection index (PI) used for conservation could guide the protection of the areas with high occurrence and high abundance (e.g. in Strategic Conservation Planning). Due to the widespread use of SDMs and the rel. easy subsequent employment of SAMs these findings have a wide relevance and applicability, worldwide. We promote and strongly encourage to further test, apply and update the priority protection index (PI) elsewhere in order to explore the generality of these findings and methods readily available now for researchers.

scholarly journals Towards combining occurrence and abundance distribution models of Great Bustard for conservation: A global research template from Bohai Bay?

2017 ◽  
Author(s):  
Chunrong Mi ◽  
Falk Huettmann ◽  
Rui Sun ◽  
Yumin Guo
Keyword(s):  
Species Abundance ◽  
Distribution Patterns ◽  
Bohai Bay ◽  
Abundance Distribution ◽  
Great Bustard ◽  
Distribution Models ◽  
A Priority ◽  
Species Abundance Models ◽  
Combine Information ◽  
Protection Index

Species distribution models (SDMs) have become important and essential tools in conservation and management. However, SDMs built with count data, commonly referred to as species abundance models (SAMs), are still less used so far. SDMs are increasingly used now in conservation decisions, whereas SAMs are still not widely employed. Species occurrence and abundance do not frequently display similar patterns, often they are not even well correlated. This leads to an insufficient or misleading conservation. How to combine information from SDMs and SAMs all together for unified conservation remains a challenge. In this study, we put forward for the first time a priority protection index (PI). The PI combines the prediction results of occurrence and abundance models. We used the best-available presence and count records for an endangered farmland species, Great Bustard (Otis tarda dybowskii) in Bohai Bay, China, as a case study. We then applied the advanced Random Forest algorithm (Salford Systems Ltd. implementation), a powerful machine learning method, with eleven predictor variables to forecast the spatial occurrence as well as the abundance distribution. The results show that the occurrence model had a decent performance (ROC: 0.77) and the abundance model had a RMSE 26.54. It is of note that environmental variables influenced bustard occurrence and abundance differently. We found that occurrence and abundance models display different spatial distribution patterns. Still, combining occurrence and abundance indices to produce a priority protection index (PI) used for conservation could guide the protection of the areas with high occurrence and high abundance (e.g. in Strategic Conservation Planning). Due to the widespread use of SDMs and the rel. easy subsequent employment of SAMs these findings have a wide relevance and applicability, worldwide. We promote and strongly encourage to further test, apply and update the priority protection index (PI) elsewhere in order to explore the generality of these findings and methods readily available now for researchers.

scholarly journals How selection structures species abundance distributions

2012 ◽  
Vol 279 (1743) ◽  
pp. 3722-3726 ◽  
Author(s):  
Anne E. Magurran ◽  
Peter A. Henderson
Keyword(s):  
Habitat Structure ◽  
Fish Community ◽  
Rank Order ◽  
Species Abundance ◽  
Estuarine Fish ◽  
Ecological Communities ◽  
Abundance Distribution ◽  
Species Abundance Distributions ◽  
Characteristic Species ◽  
Safety In Numbers

How do species divide resources to produce the characteristic species abundance distributions seen in nature? One way to resolve this problem is to examine how the biomass (or capacity) of the spatial guilds that combine to produce an abundance distribution is allocated among species. Here we argue that selection on body size varies across guilds occupying spatially distinct habitats. Using an exceptionally well-characterized estuarine fish community, we show that biomass is concentrated in large bodied species in guilds where habitat structure provides protection from predators, but not in those guilds associated with open habitats and where safety in numbers is a mechanism for reducing predation risk. We further demonstrate that while there is temporal turnover in the abundances and identities of species that comprise these guilds, guild rank order is conserved across our 30-year time series. These results demonstrate that ecological communities are not randomly assembled but can be decomposed into guilds where capacity is predictably allocated among species.

Arsenic in terrestrial invertebrates from riparian areas of the Piracicaba River Basin, São Paulo State, Brazil

2017 ◽  
Vol 314 (3) ◽  
pp. 1675-1681
Author(s):  
Elvis J. França ◽  
Elisabete A. De Nadai Fernandes ◽  
Felipe Y. Fonseca ◽  
Marcelo R. L. Magalhães ◽  
Mariana L. O. Santos
Keyword(s):  
River Basin ◽  
Sao Paulo ◽  
São Paulo ◽  
Riparian Areas ◽  
Terrestrial Invertebrates ◽  
Paulo State ◽  
São Paulo State ◽  
Piracicaba River

scholarly journals Temporal variation of the phytoplankton community at short sampling intervals in the Mundaú reservoir, Northeastern Brazil

2008 ◽  
Vol 22 (4) ◽  
pp. 970-982 ◽  
Author(s):  
Ênio Wocyli Dantas ◽  
Ariadne do Nascimento Moura ◽  
Maria do Carmo Bittencourt-Oliveira ◽  
João Dias de Toledo Arruda Neto ◽  
Airlton de Deus C. Cavalcanti
Keyword(s):  
Phytoplankton Community ◽  
Abiotic Factors ◽  
Distribution Patterns ◽  
Northeastern Brazil ◽  
Cylindrospermopsis Raciborskii ◽  
Algae Biomass ◽  
Dissolved Phosphorus ◽  
Sampling Intervals ◽  
Abiotic Variables ◽  
Dry And Rainy Seasons

The aim of this study was to determine how abiotic factors drive the phytoplankton community in a water supply reservoir within short sampling intervals. Samples were collected at the subsurface (0.1 m) and bottom of limnetic (8 m) and littoral (2 m) zones in both the dry and rainy seasons. The following abiotic variables were analyzed: water temperature, dissolved oxygen, electrical conductivity, total dissolved solids, turbidity, pH, total nitrogen, nitrite, nitrate, total phosphorus, total dissolved phosphorus and orthophosphate. Phytoplankton biomass was determined from biovolume values. The role abiotic variables play in the dynamics of phytoplankton species was determined by means of Canonical Correspondence Analysis. Algae biomass ranged from 1.17×10(4) to 9.21×10(4) µg.L-1; cyanobacteria had biomass values ranging from 1.07×10(4) to 8.21×10(4) µg.L-1. High availability of phosphorous, nitrogen limitation, alkaline pH and thermal stability all favored cyanobacteria blooms, particularly during the dry season. Temperature, pH, total phosphorous and turbidity were key factors in characterizing the phytoplankton community between sampling times and stations. Of the species studied, Cylindrospermopsis raciborskii populations were dominant in the phytoplankton in both the dry and rainy seasons. We conclude that the phytoplankton was strongly influenced by abiotic variables, particularly in relation to seasonal distribution patterns.

Trophic structure of a fish community along environmental gradients of a subtropical river (Paraitinga River, Upper Tietê River Basin, Brazil)

Hydrobiologia ◽  
2007 ◽  
Vol 598 (1) ◽  
pp. 373-387 ◽  
Author(s):  
Katharina Eichbaum Esteves ◽  
Ana Valéria Pinto Lobo ◽  
Marcos Daniel Renó Faria
Keyword(s):  
River Basin ◽  
Fish Community ◽  
Trophic Structure ◽  
Environmental Gradients ◽  
Tietê River
Sign in / Sign up

Export Citation Format

Share Document