title: Recognising the negative impacts of aquatic weed management: Okawa Bay, Lake Rotoiti case study

authors: Scholes, Paul

abstract: The Te Arawa Rotorua Lakes Programme continues to implement measures to maintain and restore lake water quality in line with trophic level indices (TLI’s) for respective lakes. In Lakes Rotorua and Rotoiti this has also meant not only reducing nutrients to and in the lakes but also managing periodic cyano-bacterial (blue-green) algal blooms.
Restoration measures in the Rotorua and Rotoiti catchments have included land use management efforts to arrest nutrient inputs; diversion of Lake Rotorua waters from entering Rotoiti; sewage reticulation of lakeside communities; and treatment of inflows
with aluminium sulphate (alum). While restoration efforts have produced water quality gains in both lakes, in recent years, cyano-bacterial blooms have occurred in the shallow embayment of Lake Rotoiti, known as Okawa Bay.
These water quality gains have not come without a cost. Submerged aquatic weeds such as Hornwort are an increasing problem in the Rotorua Te Arawa Lakes and significant resources are needed to manage infestations primarily for aesthetic and recreational
amenity. Control of exotic aquatic weed is mostly with application of herbicide. Herbicide treatment leaves a decomposing biomass in the waterbody, potentially increasing the nutrient status.
We examine the impact aquatic weed decomposition has had on Okawa Bay’s nutrient status and the cyano-bacterial blooms over recent summers, and Bay of Plenty Regional Council’s response to reduce localised bloom development.

text: Recognising the negative impacts of aquatic weed management: Okawa Bay, Lake Rotoiti case study The Te Arawa Rotorua Lakes Programme continues to implement measures to maintain and restore lake water quality in line with trophic level indices (TLI’s) for respective lakes. In Lakes Rotorua and Rotoiti this has also meant not only reducing nutrients to and in the lakes but also managing periodic cyano-bacterial (blue-green) algal blooms.
Restoration measures in the Rotorua and Rotoiti catchments have included land use management efforts to arrest nutrient inputs; diversion of Lake Rotorua waters from entering Rotoiti; sewage reticulation of lakeside communities; and treatment of inflows
with aluminium sulphate (alum). While restoration efforts have produced water quality gains in both lakes, in recent years, cyano-bacterial blooms have occurred in the shallow embayment of Lake Rotoiti, known as Okawa Bay.
These water quality gains have not come without a cost. Submerged aquatic weeds such as Hornwort are an increasing problem in the Rotorua Te Arawa Lakes and significant resources are needed to manage infestations primarily for aesthetic and recreational
amenity. Control of exotic aquatic weed is mostly with application of herbicide. Herbicide treatment leaves a decomposing biomass in the waterbody, potentially increasing the nutrient status.
We examine the impact aquatic weed decomposition has had on Okawa Bay’s nutrient status and the cyano-bacterial blooms over recent summers, and Bay of Plenty Regional Council’s response to reduce localised bloom development.

year: 2015

title: Kia whakatōmuri te haere whakamua – A Māori framework for coastal climate change adaptation

authors: Bailey-Winiata, Akuhata

subjects: Climate Change Adaptation

abstract: Current climate change adaptation frameworks do not sufficiently represent or acknowledge the intricacies and complex nature of climate adaptation for all communities. This thesis contributes a Māori (Indigenous peoples of Aotearoa New Zealand (A-NZ)) climate adaptation framework that is informed by Te Ao Māori world views, mātauranga Māori (Māori knowledge), Te Tiriti o Waitangi (Treaty of Waitangi) and research at the interface of Western knowledge and mātauranga Māori. This is needed to promote a more equitable and culturally sensitive approach to the current climate change adaptation process for hapū (sub-tribes) and iwi (tribes). To achieve this, this thesis is rooted in the whakataukī (proverb) “Kia whakatōmuri te haere whakamua – I walk backwards into the future with my eyes fixed on my past”, which exemplifies the power of acknowledging Māori history and intergenerational knowledge, while moving forward to a prosperous future. This thesis had four key aims. First, I looked to the past to understand ways Māori ancestors adapted and relocated in response to natural hazards to inform a future climate adaptation or relocation process. Second, I took stock of the contemporary risk to coastal marae (Māori meeting grounds) from sea level rise. Third, I looked internationally at other Indigenous communities' adaptation challenges and how they have overcome them as a mutual learning opportunity. And finally, I developed a conceptual Māori climate adaptation framework based on existing models and informed by a wānanga (workshop) with over 100 Māori participants. This two-part framework emphasises the importance of fostering Te Tiriti-centric relationships and partnerships that are essential for climate adaptation with hapū and iwi. In addition, it serves as a guide to support adaptation decision-making for hapū and iwi Māori. This thesis took a multidisciplinary approach to achieve these aims, incorporating historical and archival analyses, semi-structured interviews, a systematic review, coastal flooding and exposure analyses using spatial information, and wānanga. The findings highlighted that relocation due to natural hazards is not uncommon for Māori, as this thesis identified 51 examples of where relocation was completed or discussed in response to natural hazards. Furthermore, in a case study with Tūhourangi and Ngāti Rangitihi, this thesis identified vital aspects that enabled their relocation following the 1886 Tarawera eruption, the most influential being tuku whenua – the gifting of land to relocate to. This thesis also identified that by 2150, under shared socio-economic pathway (SSP) 5-8.5, 27% and 28% of coastal marae (total of 186 nationally) are exposed to a 100–year and 1000–year annual recurrence interval (ARI) extreme sea level events, respectively. Overall, this thesis contributes to our understanding of the requirements to support hapū and iwi Māori in climate adaptation, with parallels to other Indigenous communities. Requirements include resourcing, partnerships, autonomy, intergenerational planning, recognition, respect and redress. In Chapter 8 – the discussion of this thesis, we focus on four overall contributions this thesis has made, which are: 1) Hapū and iwi Māori are at risk of climate change, 2) We’ve done it before we can do it again, 3) Adaptation approach cannot be one-size-fits-all, and 4) Adaptation in Aotearoa needs a Te Tiriti-centric approach. Ultimately, this thesis underscores the urgent need for inclusive climate adaptation strategies that honour Indigenous knowledge systems, ensuring that hapū and iwi Māori are not only heard but are actively supported in navigating the impacts of climate change.

text: Kia whakatōmuri te haere whakamua – A Māori framework for coastal climate change adaptation Current climate change adaptation frameworks do not sufficiently represent or acknowledge the intricacies and complex nature of climate adaptation for all communities. This thesis contributes a Māori (Indigenous peoples of Aotearoa New Zealand (A-NZ)) climate adaptation framework that is informed by Te Ao Māori world views, mātauranga Māori (Māori knowledge), Te Tiriti o Waitangi (Treaty of Waitangi) and research at the interface of Western knowledge and mātauranga Māori. This is needed to promote a more equitable and culturally sensitive approach to the current climate change adaptation process for hapū (sub-tribes) and iwi (tribes). To achieve this, this thesis is rooted in the whakataukī (proverb) “Kia whakatōmuri te haere whakamua – I walk backwards into the future with my eyes fixed on my past”, which exemplifies the power of acknowledging Māori history and intergenerational knowledge, while moving forward to a prosperous future. This thesis had four key aims. First, I looked to the past to understand ways Māori ancestors adapted and relocated in response to natural hazards to inform a future climate adaptation or relocation process. Second, I took stock of the contemporary risk to coastal marae (Māori meeting grounds) from sea level rise. Third, I looked internationally at other Indigenous communities' adaptation challenges and how they have overcome them as a mutual learning opportunity. And finally, I developed a conceptual Māori climate adaptation framework based on existing models and informed by a wānanga (workshop) with over 100 Māori participants. This two-part framework emphasises the importance of fostering Te Tiriti-centric relationships and partnerships that are essential for climate adaptation with hapū and iwi. In addition, it serves as a guide to support adaptation decision-making for hapū and iwi Māori. This thesis took a multidisciplinary approach to achieve these aims, incorporating historical and archival analyses, semi-structured interviews, a systematic review, coastal flooding and exposure analyses using spatial information, and wānanga. The findings highlighted that relocation due to natural hazards is not uncommon for Māori, as this thesis identified 51 examples of where relocation was completed or discussed in response to natural hazards. Furthermore, in a case study with Tūhourangi and Ngāti Rangitihi, this thesis identified vital aspects that enabled their relocation following the 1886 Tarawera eruption, the most influential being tuku whenua – the gifting of land to relocate to. This thesis also identified that by 2150, under shared socio-economic pathway (SSP) 5-8.5, 27% and 28% of coastal marae (total of 186 nationally) are exposed to a 100–year and 1000–year annual recurrence interval (ARI) extreme sea level events, respectively. Overall, this thesis contributes to our understanding of the requirements to support hapū and iwi Māori in climate adaptation, with parallels to other Indigenous communities. Requirements include resourcing, partnerships, autonomy, intergenerational planning, recognition, respect and redress. In Chapter 8 – the discussion of this thesis, we focus on four overall contributions this thesis has made, which are: 1) Hapū and iwi Māori are at risk of climate change, 2) We’ve done it before we can do it again, 3) Adaptation approach cannot be one-size-fits-all, and 4) Adaptation in Aotearoa needs a Te Tiriti-centric approach. Ultimately, this thesis underscores the urgent need for inclusive climate adaptation strategies that honour Indigenous knowledge systems, ensuring that hapū and iwi Māori are not only heard but are actively supported in navigating the impacts of climate change.

year: 2024-05-28

title: Embedding Java classes with code2vec: improvements from variable obfuscation

authors: Compton, Rhys

abstract: Automatic source code analysis in key areas of software engineering, such as code security, can benefit from Machine Learning (ML). However, many standard ML approaches require a numeric representation of data and cannot be applied directly to source code. Thus, to enable ML, we need to embed source code into numeric feature vectors while maintaining the semantics of the code as much as possible. code2vec is a recently released embedding approach that uses the proxy task of method name prediction to map Java methods to feature vectors. However, experimentation with code2vec shows that it learns to rely on variable names for prediction, causing it to be easily fooled by typos or adversarial attacks. Moreover, it is only able to embed individual Java methods and cannot embed an entire collection of methods such as those present in a typical Java class, making it difficult to perform predictions at the class level (e.g., for the identification of malicious Java classes). Both shortcomings are addressed in the research presented in this paper. We investigate the effect of obfuscating variable names during training of a code2vec model to force it to rely on the structure of the code rather than specific names and consider a simple approach to creating class-level embeddings by aggregating sets of method embeddings. Our results, obtained on a challenging new collection of source-code classification problems, indicate that obfuscating variable names produces an embedding model that is both impervious to variable naming and more accurately reflects code semantics. The datasets, models, and code are shared1 for further ML research on source code.

text: Embedding Java classes with code2vec: improvements from variable obfuscation Automatic source code analysis in key areas of software engineering, such as code security, can benefit from Machine Learning (ML). However, many standard ML approaches require a numeric representation of data and cannot be applied directly to source code. Thus, to enable ML, we need to embed source code into numeric feature vectors while maintaining the semantics of the code as much as possible. code2vec is a recently released embedding approach that uses the proxy task of method name prediction to map Java methods to feature vectors. However, experimentation with code2vec shows that it learns to rely on variable names for prediction, causing it to be easily fooled by typos or adversarial attacks. Moreover, it is only able to embed individual Java methods and cannot embed an entire collection of methods such as those present in a typical Java class, making it difficult to perform predictions at the class level (e.g., for the identification of malicious Java classes). Both shortcomings are addressed in the research presented in this paper. We investigate the effect of obfuscating variable names during training of a code2vec model to force it to rely on the structure of the code rather than specific names and consider a simple approach to creating class-level embeddings by aggregating sets of method embeddings. Our results, obtained on a challenging new collection of source-code classification problems, indicate that obfuscating variable names produces an embedding model that is both impervious to variable naming and more accurately reflects code semantics. The datasets, models, and code are shared1 for further ML research on source code.

year: 2020