RGB and multispectral UAV image fusion for Gramineae weed detection in rice fields

In this paper, a new method to fuse low resolution multispectral and high resolution RGB images is introduced, in order to detect Gramineae weed in rice fields with plants at 50 days after emergence (DAE).The images are taken from a fixed-wing unmanned aerial vehicle (UAV) at 60 and 70 m altitude. The proposed method combines the texture information given by a high resolution red–green–blue (RGB) image and the reflectance information given by a low resolution multispectral (MS) image, to obtain a fused RGB-MS image with better weed discrimination features. After analyzing the normalized difference vegetation index (NDVI) and normalized green red difference index (NGRDI) for weed detection, it was found that NGRDI presents better features. The fusion method consists of decomposing the RGB image using the intensity, hue and saturation (IHS) transformation, then, a second order Haar wavelet transformation is applied to the intensity layer (I) and the NGRDI image. From this transformation, the low–low (LL) coefficients of the NGRDI image are replaced by the LL coefficients of the I layer. Finally, the fused image is obtained by transforming the new wavelet coefficients to RGB space. To test the method, a one hectare experimental plot with rice plants at 50 DAE with Gramineae weeds was selected. Additionally, to compare the performance of the method, two indices were used, specifically, the M/M_GT index which is the percentage of detected weed area, and the MP index which indicates the precision of weed detection. These indices were evaluated in four validation zones using three Neural Networks (NN) detection systems based on three types of images; namely, RGB, RGB + NGRDI, and fused RGB-NGRDI. The best weed detection performance was obtained by the NN with the fused image, with M/M_GT index between 80 and 108% and MP between 70 and 85%.     

Changing how we approach fisheries: A first attempt at an operational framework for ecosystem approaches to fisheries management

The increasing need to account for the many factors that influence fish population dynamics, particularly those external to the population, has led to repeated calls for an ecosystem approach to fisheries management (EAFM). Yet systematically and clearly addressing these factors, and hence implementing EAFM, has suffered from a lack of clear operational guidance. Here, we propose 13 main factors (shift in location, migration route or timing, overfishing (three types), decrease in physiology, increase in predation, increase in competition, decrease in prey availability, increase in disease or parasites and a decline in habitat quality or habitat quantity) that can negatively influence fish populations via mechanisms readily observable in ~20 population features. Using these features as part of a diagnostic framework, we develop flow charts that link probable mechanism(s) underlying population change to the most judicious management actions. We then apply the framework for example case studies that have well‐known and documented population dynamics. To our knowledge, this is the first attempt to provide a clearly defined matrix of all the probable responses to the most common factors influencing fish populations, and to examine possible diagnostics simultaneously, comparatively and relatively in an attempt to elucidate the most probable mechanisms responsible. The framework we propose aims to operationalize EAFM, thereby not only better diagnosing factors influencing fish populations, but also suggesting the most appropriate management interventions, and ultimately leading to improved fisheries. We assert the framework proposed should result in both better use of limited analytical and observational resources and more tailored and effective management actions.     

Genetic differentiation between and within ecotypes of pike (Esox lucius) in the Baltic Sea

1. Aquatic systems often lack physical boundaries for gene flow, but ecological and behavioural barriers can form surprisingly fine spatial scale genetic patterns that challenge traditional, large scale management. To detect fine spatial scale structures, understand sources of intraspecific diversity, and design appropriate management plans requires identification of reproductively isolated units.
2. This study reports on genetic differentiation in pike (Esox lucius) within a coastal area stretching 55 km from south to north in the central Baltic Sea. Pike is here an economically and ecologically important top predator that has declined in abundance. However, population structures have mostly been studied on large spatial scales, and without considering the potential for genetic divergence between the sympatric anadromous fresh water and the resident brackish water spawning ecotypes.
3. To this end, 487 individuals from the east coast of Sweden and the island of Öland, representing sympatric anadromous and resident spawning individuals, categorized to ecotype based on spawning location or otolith microchemistry, were genotyped for 10 microsatellites and used to test for divergence between ecotypes. Furthermore, divergence between regions (island/mainland), neighbouring spawning locations (n = 13) and isolation by distance within and between regions were evaluated for the anadromous ecotype.
4. The results revealed strong genetic differences between regions, between spawning locations separated by as little as 5 km and the first evidence of genetic differentiation between resident and anadromous ecotypes; despite a high dispersal capacity of pike and a high connectivity within the study area. The signatures of isolation by distance indicated that connectivity among populations differed between regions, probably reflecting availability of spawning habitats.
5. To safeguard against the challenges and uncertainties associated with environmental change, adaptive conservation management should aim to promote high intra‐population functional genetic diversity without compromising the continued integrity and coexistence of the different ecotypes and of locally adapted sub‐populations.

     

Effects of no‐take area size and age of marine protected areas on fisheries yields: a meta‐analytical approach

Marine protected areas (MPAs) are often promoted as tools for biodiversity conservation as well as for fisheries management. Despite increasing evidence of their usefulness, questions remain regarding the optimal design of MPAs, in particular concerning their function as fisheries management tools, for which empirical studies are still lacking. Using 28 data sets from seven MPAs in Southern Europe, we developed a meta‐analytical approach to investigate the effects of protection on adjacent fisheries and asking how these effects are influenced by MPA size and age. Southern European MPAs showed clear effects on the surrounding fisheries, on the ‘catch per unit effort’ (CPUE) of target species, but especially on the CPUE of the marketable catch. These effects depended on the time of protection and on the size of the no‐take area. CPUE of both target species and the marketable catch increased gradually by 2–4% per year over a long time period (at least 30 years). The influence of the size of the no‐take area appeared to be more complex. The catch rates of the entire fishery in and around the MPA were higher when the no‐take areas were smaller. Conversely, catch rates of selected fisheries that were expected to benefit most from protection increased when the no‐take area was larger. Our results emphasize the importance of MPA size on its export functions and suggest that an adequate, often extended, time frame be used for the management and the evaluation of effectiveness of MPAs.