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    Functional groups of rotifers and an exotic species in a tropical shallow lake

    Our study on a shallow tropical lake identified fluctuations and interactions of rotifer assemblage, based on trophic guild analysis, comparable to those found in temperate lakes. We also highlighted that trophic guilds, based on trophi structure, has broad application to both temperate and tropical water bodies, which shows the universality of this approach. In addition, our analysis on the interaction between the exotic species Kellicottia bostoniensis and other microphagous rotifers were sufficient to demonstrate that it does not have invasive characteristics.
    The Guild Ratio (GR), based on the density of raptorial and microphagous functional groups of rotifers, revealed to be an appropriate tool in the evaluation of possible interactions with other planktonic groups, as well as in the evaluation of temporal changes of functional groups. Unlike Obertegger and Manca5 and Obertegger et al.11, we used the database of densities of functional groups instead of biomass, according to Smith et al.10. The significant correlation between GR and cladocerans showed that GR, based on number of individuals, indicated interaction between microphagous rotifers and cladocerans like that reported by Obertegger and Manca5 and Obertegger et al.11 in temperate lakes, based on biomass (GR′). The relationship GR-cladocerans showed a similar trend with monthly and bimonthly data, indicating its adequacy even when data are less frequently obtained, which agrees with results from Obertegger et al.11 in Lake Washington, USA. Given this point, our findings reinforce that other studies may be designed with lower sampling frequency and certainly achieve satisfactory results, allowing a cheaper logistic planning in further research.
    The significant positive correlation between GR and cladoceran densities indicates competition between the groups, corroborating the initial hypothesis. The predominance of microphagous rotifers (i.e. lower GR values) when cladoceran densities decreased, represented mainly by Daphnia gessneri (max. size 1.22 mm) and Ceriodaphnia richardi (max. 0.70 mm)29, is a sign of competition between both groups. Therefore, when cladocerans were more abundant during the cool season (May–September), the raptorial rotifers species predominated, which coexist with filtering cladocerans, similarly to results obtained by Obertegger et al.11, in lakes Washington (USA) and Caldonazzo (Italy). Exploitative competition between cladocerans and rotifers, particularly microphagous species, which occupy a similar niche, may even lead to competitive exclusion of rotifers. Herbivorous cyclopoid nauplii could compete with rotifers and make our analysis meaningful, however there was no evidence of interaction between them and microphagous rotifers, which does not support our hypothesis.
    Several studies report the competitive superiority of cladocerans4,30,31. The inferiority of rotifers may be partly due to lower clearance rate (1–10 µL ind.−1 h−1) than cladocerans (10–150 µL ind.−1 h−1) as well as a more limited size food range (ca. 4–17 µm)1. The maximum clearance rate of cladocerans may be much higher than that already mentioned by Nogrady et al.1 and dependent on various factors such as temperature, food concentration and body size9. Rotifer populations may be suppressed by more efficient cladocerans through exploitative competition, although rotifers may also suffer effects from interference competition32,33. Cladocerans larger than 1.2 mm may suppress small rotifer populations by interference34. In Lake Monte Alegre, cladoceran species are relatively small and probably exploitative competition is the most important interaction in this community.
    The increase in algal carbon and temperature in the Lake Monte Alegre during the warm season (October–April) was not followed by increase of the total rotifer densities, indicating a preponderant influence of another factor. However, as mentioned above, there was an increase in the abundance of microphagous species and a decrease in densities of raptorial species in this season. Raptorial species, particularly large species (e.g., Synchaeta spp.), prefer larger items ( > 50 µm) such as algae, ciliates and other rotifers13. Species of the genus Ascomorpha feed on dinophytes, such as Peridinium and Ceratium, which are grasped, and the content sucked1. In Lake Monte Alegre, an increase of Peridinium in the fall and winter (March–September) was already reported35,36, which would benefit some raptorial rotifers, including Ascomorpha. However, in this study in 2011–2012, dinophytes were not abundant (L.H.S. Silva, unpublished data), representing about 1.4% of the total phytoplankton density, chlorophytes predominating, increasing the contribution of cyanobacteria in the warm season. Therefore, higher densities of raptorial species in the cool season were unrelated to phytoplankton composition and, on the other hand, higher temperatures in the warm season did not favor the increase in populations of this group.
    The distribution of organisms can be a strategy to avoid competition and predation. In Lake Monte Alegre, several species of Colotheca, Keratella, Polyarthra, and Trichocerca occupied the entire water column in the cool season (A. J. Meschiatti et al. unpublished data). In the warm season, species of these genera, in addition to Brachionus, Hexarthra and Ptygura were limited to the oxygenated layer, avoiding the anoxic hypolimnion. Another feature of the vertical distribution of rotifers in this lake was the frequent occupation of the most superficial layer, even during the day, which is rarely occupied by cladocerans37, reducing overlap and possible interactions with other organisms.
    Direct predation on rotifers by chaoborid larvae is low in Lake Monte Alegre, representing 9% of the prey number for instars I and II, 4% for instar III, not being preyed on by instar IV38. In an experiment with mesocosm in this lake, no predation effect by Chaoborus larvae on Keratella spp. densities was detected39. Zooplankton predation by fish in the lake is mainly exerted by adult of the exotic cichlid Tilapia rendalli (current name Coptodon rendalli), a pump filter-feeder40, which collects organisms with lower evasion to the filtering current, which, however, are not abundant in the lake. Although Keratella sp. was not rejected by tilápia, its consumption is low by this fish species, whose predation is higher on cladocerans40.
    Temporal variations of functional groups of rotifers in Lake Monte Alegre indicated the indirect effect of cladoceran predation by invertebrates, such as Chaoborus brasiliensis larvae and the aquatic mite, Krendowskia sp., in 2011–201241. Predation pressure by invertebrates is generally higher in the warm season when their populations increased, resulting in declining cladoceran populations29,41. Consequently, there is a decrease in exploitative competition by cladocerans and the possibility of competitive exclusion when resources are limiting. Predation by invertebrates has emerged as the main structuring factor of the lake zooplankton29, and this study highlights the indirect effect of this factor on rotifers.
    The high frequency of occurrence of the exotic species Kellicottia bostoniensis in the present study, combined with the weekly sampling strategy adopted, demonstrates the great persistence capacity of this species in the environment, with rare occasions when it is excluded from the water column. This feature indicates success of the exotic species in the new habitat22. The characteristics of an invasive species are not always scientifically proven, and many failures are not reported in publications, introducing a bias in evaluating the success of exotic species19. The presence of this exotic species in Lake Monte Alegre had not been detected in previous studies conducted in the 1980s42,43. Although very common, according to Josefsson and Andersson18 it is not invasive in the lake, as it did not constitute a threat to the local community of rotifers. It does not outcompete other microphagous rotifers and, on the contrary, there is evidence of being competitively inferior, as its population decreased in periods of dominance of other microphagous species. A laboratory experiment showed that K. bostoniensis had no effect on zooplankton composed of native copepods, cladocerans, and rotifers, affecting only ciliates, which are part of its food resources23, reinforcing the idea that it does not constitute a threat to the whole planktonic community.
    This exotic species was caught in lakes from River Doce valley44 and in Furnas Reservoir45, in Brazil, at lower densities than those of Lake Monte Alegre (max. 127 ind. L−1). The vertical distribution in Nado Reservoir, located in Brazil, showed its highest abundance in the anoxic hypolimnion, on a diel cycle46, indicating resistance of this species to adverse conditions. In some Swedish lakes, the exotic species K. bostoniensis was also found in deeper layers18, as well as in Mirror Lake, United States, where the production of K. bostoniensis, a native species, was higher at the bottom47. Apparently, this species maintains similar distribution in its original habitat and a new habitat. The ability to occupy lower layers, often anoxic, where few microcrustaceans and rotifers are found, would lower negative interactions with other populations and even constitutes a defense strategy against predation by most invertebrates and filtering fish48. More

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    Revised estimates of ocean-atmosphere CO2 flux are consistent with ocean carbon inventory

    Effect of temperature corrections
    Figure 1 illustrates the effect of the two adjustments described above on a calculation of annual global ocean-atmosphere fluxes for this period, with calculations starting from the SOCAT v2019 database. To interpolate the SOCAT surface water fCO2 data in space and time we adopt as our standard method the two-step neural network approach described by Landschützer et al.8,18, (see also description below and Methods section). The interpolation was applied to the SOCAT data without modification, after adjusting the data to a subskin temperature and regridding (as described in refs. 12,19, see also Methods section) then additionally after repeating the flux calculation assuming a ΔT across the cool skin of 0.17 K15 salinity increase of 0.1 unit11 and the conservative “rapid transport” scheme of Woolf et al.11 (see Methods section). Each adjustment increases the calculated flux by ~0.4 PgC yr−1 when integrated over the global ocean. For the period ~2000, this approximately doubles the calculated flux into the ocean. Over the 27 years 1992–2018 inclusive, the cumulative uptake is increased from 43 to 67 PgC.
    Fig. 1: Effect of near-surface temperature corrections.

    Global air–sea flux calculated by interpolating SOCAT gridded data using a neural network technique8, followed by the gas exchange equation applied to the ocean mass boundary layer. The net flux into the ocean is shown as negative, following convention. The uncorrected curve uses the SOCAT fCO2 at inlet temperature as usually done. Correction of the data to a satellite-derived subskin temperature is shown, and the additional change in flux due to a thermal skin assumed to be cooler and saltier than the subskin by 0.17 K15 and 0.1 salinity units11. Excludes the Arctic and some regional seas—ocean regions included are shown in Supplementary Fig. 2.

    Full size image

    Uncertainty estimates
    Ocean-atmosphere fluxes calculated using the gas exchange equation are subject to two broad sources of uncertainty: (1) specification of the gas transfer velocity, which depends on the thickness of the MBL and is usually parameterized as a function of wind speed, and (2) specification of the CO2 concentration difference across the MBL. The recent study by Woolf et al.20 contains a detailed treatment of the uncertainties due to the gas transfer, concluding that a realistic estimate (approximately, a 90% confidence interval) is ±10% when applying this to global data.
    The second source of uncertainty, due to the concentration difference, is dominated by that introduced by the interpolation in time and location of surface ocean CO2. This is relatively well constrained in the more densely observed regions such as the North and Equatorial Atlantic and North and Tropical Pacific. However, in more remote regions such as the Southern, South Pacific, and Indian Oceans, the observational coverage is patchier in space and time and often seasonally biased, with few winter measurements (see Supplementary Fig. 3). New sensors and designs of autonomous floats, as now being deployed in the Southern Ocean21, show promise to solve the problem of adequately observing surface CO2 in remote regions22, but for the gap-prone historical data, the interpolation method used can have a substantial influence on the results in these data-poor regions.
    To evaluate the uncertainty in flux estimates introduced by the gap-filling procedure, we used three methods for interpolating in space and time, each applied to the global data divided according to three different spatial clustering schemes, for a total of nine mappings. The interpolation methods were as follows: (1) a time series (TS) of fCO2sw data, constructed by a least squares fit to all monthly averaged fCO2 values within the defined region. The model fitted was a seasonal cycle with three harmonics superimposed on a linear trend; (2) simple multilinear regression (MLR) of the fCO2 data on latitude, longitude, and four variables for which continuous comprehensive mappings are available, these being sea-surface temperature (SST), salinity (SSS), mixed layer depth (MLD), and atmospheric CO2 mixing ratio (XCO2); (3) the feed-forward neural network method of Landschützer et al.8,18 (FFN), which also seeks a regression on these four variables. The spatial clustering schemes applied to each of the techniques (shown in Supplementary Fig. 1) were as follows: (a) division into 14 regions along latitude–longitude lines; (b) division into the 17 biogeochemical divisions suggested by Fay and Mckinley23, and (c) division into 16 biomes using a self-organizing map technique employed by Landschützer et al.8.
    Where the data are adequately distributed over space and time, the use of multiple mapping techniques and different clustering schemes to estimate uncertainty gives similar results to formal geostatistical techniques, such as kriging7,20. However, in regions of very sparse and uneven coverage, statistically based techniques can underestimate uncertainties because of the assumption that the available data are representative of the true data population over a region, which may not be the case if whole regions or seasons are poorly sampled. In this instance, different mapping techniques can give substantially different results. Altering the clustering of the data by changing the shape of the geographical divisions can also have a major effect, because unsampled areas are assumed to have the same statistical properties as the sampled regions with which they are grouped.
    For each combined mapping-and-clustering technique, Table 1 shows the spread and mean of the residuals (the global set of predicted values minus observed values). The neural network FFN mapping method provides a much smaller spread of residuals, giving better agreement with data at a given location and time than do the other methods. This is to be expected given its much greater flexibility, with typically several hundred parameters being adjusted to provide a non-linear fit to each cluster, compared to only 8 and 11 fitted parameters for respectively the TS and MLR methods. Figure 2 shows estimates of global and hemispheric ocean-atmosphere CO2 flux over the period 1992–2018 by the nine interpolations (using a single parameterization of the gas transfer velocity). Despite the difference in the quality of the fits to the individual data as evidenced by Table 1, convergent results are obtained by all the calculations for the Northern Hemisphere over the whole period, and there is a good agreement in the Southern Hemisphere for much of the period after 2000. The average of all the methods is shown, with one and two standard deviations of the nine separate estimates. A few regions are excluded (see Supplementary Fig. 2) to ensure compatibility in the comparison between methods, but these affect the results by More

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    A realistic fish-habitat dataset to evaluate algorithms for underwater visual analysis

    Based on the labels of DeepFish, we consider these four computer vision tasks: classification, counting, localization, and segmentation. Deep learning have consistently achieved state-of-the-art results on these tasks as they can leverage the enormous size of the datasets they are trained on. These datasets include ImageNet6, Pascal7, CityScapes5 and COCO24. DeepFish aims to be part of these large scale datasets with the unique goal of understanding complex fish habitats for the purpose of inspiring further research in this area.
    We present standard deep learning methods for each of these tasks. Shown as the blue module in Figure 4, these methods have the ResNet-5013 backbone which is one of the most popular feature extractors for image understanding and visual recognition. They enable models to learn from large datasets and transfer the acquired knowledge to train efficiently on another dataset. This process is known as transfer learning and has been consistently used in most current deep learning methods22. Such pretrained models can even recognize object classes that they have never been trained on29. This property illustrates how powerful the extracted features are from a pretrained ResNet-50.
    Therefore, we initialize the weights of our ResNet-50 backbones by pre-training it on ImageNet following the procedure discussed in6. ImageNet consists of over 14 million images categorized over 1,000 classes. As a result, the backbone learns to extract strong, general features for unseen images by training on such dataset. These features are then used by a designated module to perform their respective computer vision task such as classification and segmentation. We describe these modules in the sections below.
    To put the results into perspective, we also include baseline results by training the same methods without ImageNet pretraining (Table 3). In this case, we randomly initialize the weights of the ResNet-50 backbone with Xavier’s method11. These results also illustrate the efficacy of having pretrained models over randomly initialized models.
    Table 3 Comparison between randomly initialized and ImageNet pretrained models.
    Full size table

    Classification results
    The goal of the classification task is to identify whether images are foreground (contains fish) or background (contains no fish). We use accuracy to evaluate the models on this task which is a standard metric for binary classification problems3,8,9,15,27. The metric is computed as

    $$begin{aligned} { ACC}=({ TP}+{ TN})/{ N}, end{aligned}$$

    where ({ TP}) and ({ TN}) are the true positives and true negatives, respectively, and ({ N}) is the total number of images. A true positive represents an image with at least one fish that is predicted as foreground, whereas a true negative represents an image with no fish that is predicted as background. For this task we used the FishClf dataset for this task where the number of images labeled is 39,766.
    The classification architecture consists of a ResNet-50 backbone and a feed-forward network (FFN) (classification branch of Fig. 4). FFN takes as input features extracted by ResNet-50 and outputs a probability for the image corresponding to how likely it contains a fish. If the probability is higher than 0.5 the predicted classification label is foreground. For the FFN, we use the network presented in ImageNet which consists of 3 layers. However, instead of the original 1,000-class output layer, we use a 2-class output layer to represent the foreground or background class.
    During training, the classifier learns to minimize the binary cross-entropy objective function28 using the Adam16 optimizer. The learning rate was set as (10^{-3}) and the batch size was set to be 16. Since FFN require a fixed resolution of the extracted features, the input images are resized to (224times 224). At test time, the model outputs a score for each of the two classes for a given unseen image. The predicted class for that image is the class with the higher score.
    In Table 3 we compare between a classifier with the backbone pretrained on ImageNet and with the randomly initialized backbone. Note that both classifiers have their FFN network initialized at random. We see that the pretrained model achieved near-perfect classification results outperforming the baseline significantly. This result suggests that transfer learning is important and that deep learning has strong potential for analyzing fish habitats.
    Figure 4

    Deep learning methods. The architecture used for the four computer vision tasks of classification, counting, localization, and segmentation consists of two components. The first component is the ResNet-50 backbone which is used to extract features from the input image. The second component is either a feed-forward network that outputs a scalar value for the input image or an upsampling path that outputs a value for each pixel in the image.

    Full size image

    Counting results
    The goal of the counting task is to predict the number of fish present in an image. We evaluate the models on the FishLoc dataset, which consists of 3,200 images labeled with point-level annotations. We measure the model’s efficacy in predicting the fish count by using the mean absolute error. It is defined as,

    $$begin{aligned} { MAE}=frac{1}{N}sum _{i=1}^N|hat{C}_i-C_i|, end{aligned}$$

    where (C_i) is the true fish count for image i and (hat{C}_i) is the model’s predicted fish count for image i. This metric is standard for object counting12,23 and it measures the number of miscounts the model is making on average across the test images.
    The counting branch in Fig. 4 shows the architecture used for the counting task, which, similar to the classifier, consists of a ResNet-50 backbone and a feed-forward network (FFN). Given the extracted features from the backbone for an input image, the FFN outputs a number that correspond to the count of the fish in the image. Thus, instead of a 2-class output layer like with the classifier, the counting model has a single node output layer.
    We train the models by minimizing the squared error loss28, which is a common objective function for the counting task. At test time, the predicted value for an image is the predicted object count.
    The counting model with the backbone pretrained on ImageNet achieved an MAE of 0.38 (Table 3. This result corresponds to making an average of 0.38 fish miscounts per image which is satisfactory as the average number of fish per image is 7. In comparison, the counting model initialized randomly achieved an MAE of 1.30. This result further confirms that transfer learning and deep learning can successfully address the counting task despite the fact that the dataset for counting (FishLoc) is much smaller than classification (FishClf).
    Localization results
    Localization considers the task of identifying the locations of the fish in the image. It is a more difficult task than classification and counting as the fish can extensively overlap. Like with the counting task, we evaluate the models on the FishLoc dataset. However, MAE scores do not provide how well the model performs at localization as the model can count the wrong objects and still achieve perfect score. To address this limitation, we use a more accurate evaluation for localization by following12, which considers both the object count and the location estimated for the objects. This metric is called Grid Average Mean absolute Error (GAME). It is computed as

    $$begin{aligned} GAME = sum _{i=1}^4 { GAME}(L),quad { GAME}(L) = frac{1}{N}sum _{i=1}^Nleft( sum _{l=1}^{4^L}|D^l_i – hat{D}^l_i|right) , end{aligned}$$

    where (D^l_i) is the number of point-level annotations in region l, and (hat{D}^l_i) is the model’s predicted count for region l. ({ GAME}(L)) first divides the image into a grid of (4^L) non-overlapping regions, and then computes the sum of the MAE scores across these regions. The higher L, the more restrictive the GAME metric will be. Note that ({ GAME}(0)) is equivalent to MAE.
    The localization branch in Fig. 4 shows the architecture used for the localization task, which consists of a ResNet-50 backbone and an upsampling path. The upsampling path is based on the network described in FCN826 which is a standard fully convolutional neural network meant for localization and segmentation, which consists of three upsampling layers.
    FCN8 processes images as follows. The features extracted with the backbone are of a smaller resolution than the input image. These features are then upsampled with the upsampling path to match the resolution of the input image. The final output is a per-pixel probability map where each pixel represents the likelihood that it belongs to the fish class.
    The models is trained using a state-of-the-art localization-based loss function called LCFCN21. LCFCN is trained using four objective functions: image-level loss, point-level loss, split-level loss, and false positive loss. The image-level loss encourages the model to predict all pixels as background for background images. The point-level loss encourages the model to predict the centroids of the fish. Unfortunately, these two loss terms alone do not prevent the model from predicting every pixel as fish for foreground images. Thus, LCFCN also minimizes the split loss and false-positive loss. The split loss splits the predicted regions so that no region has more than one point annotation. This results in one blob per point annotation. The false-positive loss prevents the model from predicting blobs for regions where there are no point annotations. Note that training LCFCN only requires point-level annotations which are spatial locations of where the objects are in the image.
    At test time, the predicted probability map are thresholded to become 1 if they are larger than 0.5 and 0 otherwise. This results in a binary mask, where each blob is a single connected component and they can be collectively obtained using the standard connected components algorithm. The number of connected components is the object count and each blob represents the location of an object instance (see Fig. 5 for example predictions with FCN8 trained with LCFCN).
    Models trained on this dataset are optimized using Adam16 with a learning rate of (10^{-3}) and weight decay of 0.0005, and have been ran for 1,000 epochs on the training set. In all cases the batch size is 1, which makes it applicable for machines with limited memory.
    Table 3 shows the MAE and GAME results of training an FCN8 with and without a pretrained ResNet-50 backbone using the LCFCN loss function. We see that pretraining leads to significant improvement on MAE and a slight improvement for GAME. The efficacy of the pretrained model is further confirmed by the qualitative results shown in Fig. 5a where the predicted blobs are well-placed on top of the fish in the images.
    Figure 5

    Qualitative results on counting, localization, and segmentation. (a) Prediction results of the model trained with the LCFCN loss21. (b) Annotations that represent the (x, y) coordinates of each fish within the images. (c) Prediction results of the model trained with the focal loss25. (d) Annotations that represent the full segmentation masks of the corresponding fish.

    Full size image

    Segmentation results
    The task of segmentation is to label every pixel in the image as either fish or not fish (Fig. 5c,d). When combined with depth information, a segmented image allows us to measure the size and the weight of the fish in a location, which can vastly improve our understanding of fish communities. We evaluate the model on the FishSeg dataset for which we acquired per-pixel labels for 620 images. We evaluate the models on this dataset using the standard Jaccard index5,7 which is defined as the number of correctly labelled pixels of a class, divided by the number of pixels labelled with that class in either the ground truth mask or the predicted mask. It is commonly known as the intersection-over-union metric IoU, computed as (frac{TP}{TP + FP + FN}), where TP, FP, and FN are the numbers of true positive, false positive, and false negative pixels, respectively, which is determined over the whole test set. In segmentation tasks, the IoU is preferred over accuracy as it is not as affected by the class imbalances that are inherent in foreground and background segmentation masks like in DeepFish.
    During training, instead of minimizing the standard per-pixel cross-entropy loss26, we use the focal loss function25 which is more suitable when the number of background pixels is much higher than the foreground pixels like in our dataset. The rest of the training procedure is the same as with the methods trained for localization.
    At test time, the model outputs a probability for each pixel in the image. If the probability is higher than 0.5 for the foreground class, then the pixel is labeled as fish, resulting in a segmentation mask for the input image.
    The results in Table 3 show a comparison between the pretrained and randomly initialized segmentation model. Like with the other tasks, the pretrained model achieves superior results both quantitatively and qualitatively (Fig. 5).
    Ethical approval
    This work was conducted with the approval of the JCU Animal Ethics Committee (protocol A2258), and conducted in accordance with DAFF general fisheries permit #168652 and GBRMP permit #CMES63. More