title: Responses of zooplankton assemblages to spatio-temporal variability in environmental conditions of brackish coastal ponds, Auckland, New Zealand

authors: Marshall, Gabrielle Millicent Jocelyn

subjects: Zooplankton

abstract: Brackish waters, including estuaries, lagoons and coastal ponds, all experience fluctuations in their physicochemical properties, particularly their salt concentrations, due to the mixing of marine and freshwater inflows. Brackish water ecosystems are particularly vulnerable to a number of threats, including sea-level rise projections, salinization and biological invasions. Most of the research pertaining to brackish waters is skewed towards lagoons and estuaries, while relatively little information exists on coastal ponds. Based on their intermediate trophic position and sensitivity to the environment, zooplankton are widely accepted as the ideal model group for aquatic research. Consequently, the aim of this study was to examine changes in zooplankton community composition and species richness relative to environmental conditions  in brackish coastal ponds, Auckland, New Zealand. To achieve this, eight coastal stormwater ponds, four freshwater ponds, and four marine sites, all with varying salt concentrations, were selected to represent a wide salinity gradient. Zooplankton and environmental variables, including salinity, chlorophyll-a, temperature, dissolved oxygen, pH and seasonal change in salinity, were sampled in winter (22 July 2021) and summer (14 January 2022). 

The coastal ponds displayed marked spatio-temporal variability in environmental conditions. Species richness was generally lower in brackish coastal ponds than in the freshwater and marine sites, providing some support for the predictions of Remane (1934). A canonical correspondence analysis (CCA) indicated that salinity explained the greatest proportion of variation in zooplankton community composition in summer (18.8 %, p = 0.002) and in winter (18.6 %, p = 0.002). In the winter ordination, seasonal change in salinity explained a large proportion of variation in zooplankton community composition (11.5 %, p = 0.024), independent of the variation explained by salinity. Temperature also explained a small proportion of variation in the winter ordination (10.4 %, p = 0.040). A community shift in dominance was recorded along the sampled salinity gradient. At the lower extreme of the gradient, the zooplankton assemblages of the freshwater sites were dominated by freshwater cladocerans, such as Chydorus sp. and Alona sp., and small rotifer species, including Lecane closterocerca, Lecane luna, Trichocerca stylata and Trichocerca porcellus. In sites characterised by intermediate salinities, euryhaline copepods, such as Sulcanus conflictus and Gladioferens pectinatus, and bdelloid rotifers dominated the assemblages. The zooplankton assemblages of marine sites and highly saline ponds were dominated by marine copepods, including Paracalnus parvus and Oithona similis, and the marine cladoceran Penilia avirostris, and crustacean larvae. Four non-indigenous zooplankton species were identified in this study, with only two species (Daphnia galaeta and Sulcanus conflictus) present in brackish coastal ponds. One cryptogenic species (Notholca cf. salina) was recorded in two brackish coastal ponds. 

This study demonstrates that coastal ponds display high levels of spatio-temporal environmental variability and low species richness. Further, my findings suggest that salinity is the most important factor driving zooplankton community composition in coastal ponds, as widely reported in estuaries and lagoons. Changes in salinity may lead to the disappearance of species that are less tolerant to such changes and the appearance of euryhaline species. Overall, my study has provided insights into these overlooked ecosystems and their unique characteristics. I recommend that this study should be expanded to brackish coastal ponds outside of the Auckland region with the inclusion of further potential explanatory variables, such as fish and macroinvertebrate sampling. Future research should also be conducted to understand the effects of salinity on cryptic speciation in brackish waters.

text: Responses of zooplankton assemblages to spatio-temporal variability in environmental conditions of brackish coastal ponds, Auckland, New Zealand Brackish waters, including estuaries, lagoons and coastal ponds, all experience fluctuations in their physicochemical properties, particularly their salt concentrations, due to the mixing of marine and freshwater inflows. Brackish water ecosystems are particularly vulnerable to a number of threats, including sea-level rise projections, salinization and biological invasions. Most of the research pertaining to brackish waters is skewed towards lagoons and estuaries, while relatively little information exists on coastal ponds. Based on their intermediate trophic position and sensitivity to the environment, zooplankton are widely accepted as the ideal model group for aquatic research. Consequently, the aim of this study was to examine changes in zooplankton community composition and species richness relative to environmental conditions  in brackish coastal ponds, Auckland, New Zealand. To achieve this, eight coastal stormwater ponds, four freshwater ponds, and four marine sites, all with varying salt concentrations, were selected to represent a wide salinity gradient. Zooplankton and environmental variables, including salinity, chlorophyll-a, temperature, dissolved oxygen, pH and seasonal change in salinity, were sampled in winter (22 July 2021) and summer (14 January 2022). 

The coastal ponds displayed marked spatio-temporal variability in environmental conditions. Species richness was generally lower in brackish coastal ponds than in the freshwater and marine sites, providing some support for the predictions of Remane (1934). A canonical correspondence analysis (CCA) indicated that salinity explained the greatest proportion of variation in zooplankton community composition in summer (18.8 %, p = 0.002) and in winter (18.6 %, p = 0.002). In the winter ordination, seasonal change in salinity explained a large proportion of variation in zooplankton community composition (11.5 %, p = 0.024), independent of the variation explained by salinity. Temperature also explained a small proportion of variation in the winter ordination (10.4 %, p = 0.040). A community shift in dominance was recorded along the sampled salinity gradient. At the lower extreme of the gradient, the zooplankton assemblages of the freshwater sites were dominated by freshwater cladocerans, such as Chydorus sp. and Alona sp., and small rotifer species, including Lecane closterocerca, Lecane luna, Trichocerca stylata and Trichocerca porcellus. In sites characterised by intermediate salinities, euryhaline copepods, such as Sulcanus conflictus and Gladioferens pectinatus, and bdelloid rotifers dominated the assemblages. The zooplankton assemblages of marine sites and highly saline ponds were dominated by marine copepods, including Paracalnus parvus and Oithona similis, and the marine cladoceran Penilia avirostris, and crustacean larvae. Four non-indigenous zooplankton species were identified in this study, with only two species (Daphnia galaeta and Sulcanus conflictus) present in brackish coastal ponds. One cryptogenic species (Notholca cf. salina) was recorded in two brackish coastal ponds. 

This study demonstrates that coastal ponds display high levels of spatio-temporal environmental variability and low species richness. Further, my findings suggest that salinity is the most important factor driving zooplankton community composition in coastal ponds, as widely reported in estuaries and lagoons. Changes in salinity may lead to the disappearance of species that are less tolerant to such changes and the appearance of euryhaline species. Overall, my study has provided insights into these overlooked ecosystems and their unique characteristics. I recommend that this study should be expanded to brackish coastal ponds outside of the Auckland region with the inclusion of further potential explanatory variables, such as fish and macroinvertebrate sampling. Future research should also be conducted to understand the effects of salinity on cryptic speciation in brackish waters.

year: 2022

title: Efficiently correcting machine learning: considering the role of example ordering in human-in-the-loop training of image classification models

authors: Holmes, Geoffrey

subjects: computer science

abstract: Arguably the most popular application task in artificial intelligence is image classification using transfer learning. Transfer learning enables models pre-trained on general classes of images, available in large numbers, to be refined for a specific application. This enables domain experts with their own—generally, substantially smaller—collections of images to build deep learning models. The good performance of such models poses the question of whether it is possible to further reduce the effort required to label training data by adopting a human-in-the-loop interface that presents the expert with the current predictions of the model on a new batch of data and only requires correction of these predictions—rather than de novo labelling by the expert—before retraining the model on the extended data. This paper looks at how to order the data in this iterative training scheme to achieve the highest model performance while minimising the effort needed to correct misclassified examples. Experiments are conducted involving five methods of ordering, using four image classification datasets, and three popular pre-trained models. Two of the methods we consider order the examples a priori whereas the other three employ an active learning approach where the ordering is updated iteratively after each new batch of data and retraining of the model. The main finding is that it is important to consider accuracy of the model in relation to the number of corrections that are required: using accuracy in relation to the number of labelled training examples—as is common practice in the literature—can be misleading. More specifically, active methods require more cumulative corrections than a priori methods for a given level of accuracy. Within their groups, active and a priori methods perform similarly. Preliminary evidence is provided that suggests that for “simple” problems, i.e., those involving fewer examples and classes, no method improves upon random selection of examples. For more complex problems, an a priori strategy based on a greedy sample selection method known as “kernel herding” performs best.

text: Efficiently correcting machine learning: considering the role of example ordering in human-in-the-loop training of image classification models Arguably the most popular application task in artificial intelligence is image classification using transfer learning. Transfer learning enables models pre-trained on general classes of images, available in large numbers, to be refined for a specific application. This enables domain experts with their own—generally, substantially smaller—collections of images to build deep learning models. The good performance of such models poses the question of whether it is possible to further reduce the effort required to label training data by adopting a human-in-the-loop interface that presents the expert with the current predictions of the model on a new batch of data and only requires correction of these predictions—rather than de novo labelling by the expert—before retraining the model on the extended data. This paper looks at how to order the data in this iterative training scheme to achieve the highest model performance while minimising the effort needed to correct misclassified examples. Experiments are conducted involving five methods of ordering, using four image classification datasets, and three popular pre-trained models. Two of the methods we consider order the examples a priori whereas the other three employ an active learning approach where the ordering is updated iteratively after each new batch of data and retraining of the model. The main finding is that it is important to consider accuracy of the model in relation to the number of corrections that are required: using accuracy in relation to the number of labelled training examples—as is common practice in the literature—can be misleading. More specifically, active methods require more cumulative corrections than a priori methods for a given level of accuracy. Within their groups, active and a priori methods perform similarly. Preliminary evidence is provided that suggests that for “simple” problems, i.e., those involving fewer examples and classes, no method improves upon random selection of examples. For more complex problems, an a priori strategy based on a greedy sample selection method known as “kernel herding” performs best.

year: 2022

title: Protecting hapū data privacy: A Ngāti Tiipa case study of technology considerations

authors: Clark, Vanessa

abstract: This brief is a part of a series, produced for the Tikanga in Technology (TiNT) research programme, that provides a hapū perspective on data sovereignty, data governance, and data privacy. In this brief we focus on the Ngāti Tiipa ‘layer cake’ technology approach and assess it against the values of the TiNT Māori Data Privacy Framework. This brief should be read in conjunction with TiNT Brief #3 The Māori Data Privacy Framework: How useful is it for hapū?

text: Protecting hapū data privacy: A Ngāti Tiipa case study of technology considerations This brief is a part of a series, produced for the Tikanga in Technology (TiNT) research programme, that provides a hapū perspective on data sovereignty, data governance, and data privacy. In this brief we focus on the Ngāti Tiipa ‘layer cake’ technology approach and assess it against the values of the TiNT Māori Data Privacy Framework. This brief should be read in conjunction with TiNT Brief #3 The Māori Data Privacy Framework: How useful is it for hapū?

year: 2025-02-11