Ichthyoplankton dynamics in a highly urbanized estuary

Spatio-temporal distribution and composition of ichthyoplankton assemblages were studied in the Golden Horn estuary (Istanbul) over a 10-month period. Environmental parameters were considered to determine the environmental status in different parts (upper, middle and lower) of the estuary. The ichthyoplankton composition of the Golden Horn estuary consisted of 23 species and was dominated by Mullus sp., Diplodus spp. and Liza sp. The largest densities of fish eggs and larvae were found in September 2009 with 786.4 ind. 100 m(-3) and 355.9 ind. 100 m(-3), respectively. As supported by the multivariate analysis, most species showed a seasonal pattern, with the presence of higher densities during summer and winter. Moreover, the spatial pattern showed that ichthyoplankton distribution and diversity was relatively high in the lower part of the Golden Horn and gradually decreased through the upper parts. Canonical correspondence analysis revealed that spatial changes in depth and water clarity were the main factors forcing larval assemblage distribution and leading to a decrease in density and diversity of fish larvae through the upper part of the estuary. For the seasonal changes, sea surface salinity and chlorophyll a were the main factors in shaping the structure of the larval assemblage and increasing sea surface temperature lead to an increase in the density and diversity of fish larvae

An overall indicator for the good environmental status of marine waters based on commercially exploited species

An indicator is presented to assess and monitor the good environmental status of national marine waters based on the status of commercially exploited marine fishes and invertebrates, including fully-assessed as well as data-limited stocks. The overall-indicator consists of one number per year. It summarizes the following sub-indicators: the stock size relative to the size that can produce the maximum sustainable fishing yield; the mortality caused by fishing relative to the natural rate of mortality; the mean length in the catch relative to the length where 90% of the females reach sexual maturity; and the abundance in national waters relative to mean abundance in the time series. For the example of German marine waters, the overall-indicator shows that only 3 of 19 stocks (Baltic Sea dab, North Sea plaice and North Sea sprat) were above the limit reference point for the overall indicator in 2011. North Sea herring was close to reaching the threshold, but most other stocks were still far below. Apparently fishing mortality was too high to allow recovery of more stocks to levels capable of producing the maximum sustainable yield. The chosen indicators and reference points may prove useful to other scientists tasked with assessing the environmental status of their national waters. (C) 2014 Published by Elsevier Ltd

Attributes2Classname: A discriminative model for attribute-based unsupervised zero-shot learning

We propose a novel approach for unsupervised zero-shot learning (ZSL) of classes based on their names. Most existing unsupervised ZSL methods aim to learn a model for directly comparing image features and class names. However, this proves to be a difficult task due to dominance of non-visual semantics in underlying vector-space embeddings of class names. To address this issue, we discriminatively learn a word representation such that the similarities between class and combination of attribute names fall in line with the visual similarity. Contrary to the traditional zero-shot learning approaches that are built upon attribute presence, our approach bypasses the laborious attribute-class relation annotations for unseen classes. In addition, our proposed approach renders text-only training possible, hence, the training can be augmented without the need to collect additional image data. The experimental results show that our method yields state-of-the-art results for unsupervised ZSL in three benchmark datasets.Comment: To appear at IEEE Int. Conference on Computer Vision (ICCV) 201

Zero-Shot Object Detection by Hybrid Region Embedding

Object detection is considered as one of the most challenging problems in computer vision, since it requires correct prediction of both classes and locations of objects in images. In this study, we define a more difficult scenario, namely zero-shot object detection (ZSD) where no visual training data is available for some of the target object classes. We present a novel approach to tackle this ZSD problem, where a convex combination of embeddings are used in conjunction with a detection framework. For evaluation of ZSD methods, we propose a simple dataset constructed from Fashion-MNIST images and also a custom zero-shot split for the Pascal VOC detection challenge. The experimental results suggest that our method yields promising results for ZSD

An overall indicator for the good environmental status of marine waters based on commercially exploited species

An indicator is presented to assess and monitor the good environmental status of national marine waters based on the status of commercially exploited marine fishes and invertebrates, including fully-assessed as well as data-limited stocks. The overall-indicator consists of one number per year. It summarizes the following sub-indicators: the stock size relative to the size that can produce the maximum sustainable fishing yield; the mortality caused by fishing relative to the natural rate of mortality; the mean length in the catch relative to the length where 90% of the females reach sexual maturity; and the abundance in national waters relative to mean abundance in the time series. For the example of German marine waters, the overall-indicator shows that only 3 of 19 stocks (Baltic Sea dab, North Sea plaice and North Sea sprat) were above the limit reference point for the overall indicator in 2011. North Sea herring was close to reaching the threshold, but most other stocks were still far below. Apparently fishing mortality was too high to allow recovery of more stocks to levels capable of producing the maximum sustainable yield. The chosen indicators and reference points may prove useful to other scientists tasked with assessing the environmental status of their national waters

Revisiting safe biological limits in fisheries

The appropriateness of three official fisheries management reference points used in the north-east Atlantic was investigated: (i) the smallest stock size that is still within safe biological limits (SSBpa), (ii) the maximum sustainable rate of exploitation (F-msy) and (iii) the age at first capture. As for (i), in 45% of the examined stocks, the official value for SSBpa was below the consensus estimates determined from three different methods. With respect to (ii), the official estimates of F-msy exceeded natural mortality M in 76% of the stocks, although M is widely regarded as natural upper limit for F-msy. And regarding (iii), the age at first capture was below the age at maturity in 74% of the stocks. No official estimates of the stock size (SSBmsy) that can produce the maximum sustainable yield (MSY) are available for the north-east Atlantic. An analysis of stocks from other areas confirmed that twice SSBpa provides a reasonable preliminary estimate. Comparing stock sizes in 2013 against this proxy showed that 88% were below the level that can produce MSY. Also, 52% of the stocks were outside of safe biological limits, and 12% were severely depleted. Fishing mortality in 2013 exceeded natural mortality in 73% of the stocks, including those that were severely depleted. These results point to the urgent need to re-assess fisheries reference points in the north-east Atlantic and to implement the regulations of the new European Common Fisheries Policy regarding sustainable fishing pressure, healthy stock sizes and adult age/size at first capture

Revisiting Safe Biological Limits in Fisheries

The appropriateness of three official fisheries management reference points used in the north-east Atlantic was investigated: (i) the smallest stock size that is still within safe biological limits (SSBpa), (ii) the maximum sustainable rate of exploitation (Fmsy) and (iii) the age at first capture. As for (i), in 45% of the examined stocks, the official value for SSBpa was below the consensus estimates determined from three different methods. With respect to (ii), the official estimates of Fmsy exceeded natural mortality M in 76% of the stocks, although M is widely regarded as natural upper limit for Fmsy. And regarding (iii), the age at first capture was below the age at maturity in 74% of the stocks. No official estimates of the stock size (SSBmsy) that can produce the maximum sustainable yield (MSY) are available for the north-east Atlantic. An analysis of stocks from other areas confirmed that twice SSBpa provides a reasonable preliminary estimate. Comparing stock sizes in 2013 against this proxy showed that 88% were below the level that can produce MSY. Also, 52% of the stocks were outside of safe biological limits, and 12% were severely depleted. Fishing mortality in 2013 exceeded natural mortality in 73% of the stocks, including those that were severely depleted. These results point to the urgent need to re-assess fisheries reference points in the north-east Atlantic and to implement the regulations of the new European Common Fisheries Policy regarding sustainable fishing pressure, healthy stock sizes and adult age/size at first capture

User Guide for CMSY++

CMSY++ is an advanced state-space Bayesian method for stock assessment that estimates fisheries reference points (MSY, Fmsy, Bmsy) as well as status or relative stock size (B/Bmsy) and fishing pressure or exploitation (F/Fmsy) from catch and (optionally) abundance data, a prior for resilience or productivity (r), and broad priors for the ratio of biomass to unfished biomass (B/k) at the beginning, an intermediate year, and the end of the time series. For the purpose of this User Guide, the whole package is referred to as CMSY++ whereas the part of the method that deals with catch-only data is referred to as CMSY (catch MSY), and the part of the method that requires additional abundance data is referred to as BSM (Bayesian Schaefer Model). Both methods are based on a modified Schaefer surplus production model (see paper cited above for more details). The main advantage of BSM, compared to other implementations of surplus production models, is the focus on informative priors and the acceptance of short and incomplete (i.e., fragmented, with missing years) abundance data. This document provides a simple step-by-step guide for researchers who want to apply CMSY++ to their own data

Image Captioning with Unseen Objects

Image caption generation is a long standing and challenging problem at the intersection of computer vision and natural language processing. A number of recently proposed approaches utilize a fully supervised object recognition model within the captioning approach. Such models, however, tend to generate sentences which only consist of objects predicted by the recognition models, excluding instances of the classes without labelled training examples. In this paper, we propose a new challenging scenario that targets the image captioning problem in a fully zero-shot learning setting, where the goal is to be able to generate captions of test images containing objects that are not seen during training. The proposed approach jointly uses a novel zero-shot object detection model and a template-based sentence generator. Our experiments show promising results on the COCO dataset.Comment: To appear in British Machine Vision Conference (BMVC) 201