title: An evaluation of behavioural evolution and environmental DNA to inform the management and monitoring of biological invasions

authors: Pyper, Nicola

subjects: adaptation

abstract: Invasive species are a global phenomenon and the rate of biological invasion is only increasing as human trade and transport, together with climate change, cause the rapid reorganisation of species distributions. The impacts of invasive species can be both severe and wide ranging, adversely affecting native ecosystems, human health, agriculture, and economies. Therefore, understanding how species become invasive and subsequently inflict damage on host environments is imperative. Additionally, once an incursion has occurred, its rapid detection and ongoing monitoring are essential for management measures to be effective. My thesis aimed to further a greater understanding of behavioural evolution in invasive species and advance the application of genetic tools to inform and enhance the prediction, monitoring, and management of invasion events. 

In the first analysis (Chapter 2), I reviewed the scientific literature reporting behavioural changes in the traits of invasive species when compared to the mean traits of conspecifics in the native range. In this research, I demonstrated that, while behavioural changes are widely reported across invasive taxa, our understanding of the possible mechanisms enabling these changes – plasticity and adaptive evolution – are poorly understood. In addition, research on the fundamental molecular and genetic processes that underlie measured phenotypic changes is in its infancy. Moving forward, I recommend that opportunities to study new or recent biological invasions should be rapidly exploited such that temporal studies can be used to identify patterns of change that occur during invasion. 

In Chapter 3, I investigated the application of environmental DNA (eDNA: genetic material released by organisms into the environment) – an increasingly popular biomonitoring method that has yet to be optimised for lacustrine environments – to the detection and monitoring of the invasive brown bullhead catfish (Ameiurus nebulosus). I sampled lakes across two regions of the North Island of New Zealand and compared the results from different field (filter pore sizes, fyke nets) and laboratory (eDNA assays) protocols. I found that species-specific assays (quantitative PCR and catfish-specific metabarcoding) had the highest rates of catfish detection and further showed that multi-species metabarcoding assays were correlated with catfish capture from fyke nets. This indicates that species-specific assays may be the most effective where detection is the primary goal, while multi-species assays hold some promise for producing semi-quantitative results. Therefore, I recommend the development of standardised, flexible, eDNA protocols that will ensure their sound application and interpretation. 

As species continue to move beyond their natural ranges, the need for a better mechanistic understanding of invasion, and for more effective detection and monitoring methods, is increasing. With technological advances and increasing availability of genomic data, more rapidly identifying new incursions and studying invasions in real time will lead to better understanding, monitoring, and management of invasive species.

text: An evaluation of behavioural evolution and environmental DNA to inform the management and monitoring of biological invasions Invasive species are a global phenomenon and the rate of biological invasion is only increasing as human trade and transport, together with climate change, cause the rapid reorganisation of species distributions. The impacts of invasive species can be both severe and wide ranging, adversely affecting native ecosystems, human health, agriculture, and economies. Therefore, understanding how species become invasive and subsequently inflict damage on host environments is imperative. Additionally, once an incursion has occurred, its rapid detection and ongoing monitoring are essential for management measures to be effective. My thesis aimed to further a greater understanding of behavioural evolution in invasive species and advance the application of genetic tools to inform and enhance the prediction, monitoring, and management of invasion events. 

In the first analysis (Chapter 2), I reviewed the scientific literature reporting behavioural changes in the traits of invasive species when compared to the mean traits of conspecifics in the native range. In this research, I demonstrated that, while behavioural changes are widely reported across invasive taxa, our understanding of the possible mechanisms enabling these changes – plasticity and adaptive evolution – are poorly understood. In addition, research on the fundamental molecular and genetic processes that underlie measured phenotypic changes is in its infancy. Moving forward, I recommend that opportunities to study new or recent biological invasions should be rapidly exploited such that temporal studies can be used to identify patterns of change that occur during invasion. 

In Chapter 3, I investigated the application of environmental DNA (eDNA: genetic material released by organisms into the environment) – an increasingly popular biomonitoring method that has yet to be optimised for lacustrine environments – to the detection and monitoring of the invasive brown bullhead catfish (Ameiurus nebulosus). I sampled lakes across two regions of the North Island of New Zealand and compared the results from different field (filter pore sizes, fyke nets) and laboratory (eDNA assays) protocols. I found that species-specific assays (quantitative PCR and catfish-specific metabarcoding) had the highest rates of catfish detection and further showed that multi-species metabarcoding assays were correlated with catfish capture from fyke nets. This indicates that species-specific assays may be the most effective where detection is the primary goal, while multi-species assays hold some promise for producing semi-quantitative results. Therefore, I recommend the development of standardised, flexible, eDNA protocols that will ensure their sound application and interpretation. 

As species continue to move beyond their natural ranges, the need for a better mechanistic understanding of invasion, and for more effective detection and monitoring methods, is increasing. With technological advances and increasing availability of genomic data, more rapidly identifying new incursions and studying invasions in real time will lead to better understanding, monitoring, and management of invasive species.

year: 2024

title: Acquiring and Exploiting Lexical Knowledge for Twitter Sentiment Analysis

authors: Bravo-Marquez, Felipe

subjects: sentiment analysis

abstract: The most popular sentiment analysis task in Twitter is the automatic classification of tweets into sentiment categories such as positive, negative, and neutral. State-of-the-art solutions to this problem are based on supervised machine learning models trained from manually annotated examples. These models are affected by label sparsity, because the manual annotation of tweets is labour-intensive and time-consuming.

This thesis addresses the label sparsity problem for Twitter polarity classification by automatically building two type of resources that can be exploited when labelled data is scarce: opinion lexicons, which are lists of words labelled by sentiment, and synthetically labelled tweets.

In the first part of the thesis, we induce Twitter-specific opinion lexicons by training words level classifiers using representations that exploit different sources of information: (a) the morphological information conveyed by part-of-speech (POS) tags, (b) associations between words and the sentiment expressed in the tweets that contain them, and (c) distributional representations calculated from unlabelled tweets. Experimental results show that the induced lexicons produce significant improvements over existing manually annotated lexicons for tweet-level polarity classification.

In the second part of the thesis, we develop distant supervision methods for generating synthetic training data for Twitter polarity classification by exploiting unlabelled tweets and prior lexical knowledge. Positive and negative training instances are generated by averaging unlabelled tweets annotated according to a given polarity lexicon. We study different mechanisms for selecting the candidate tweets to be averaged. Our experimental results show that the training data generated by the proposed models produce classifiers that perform significantly better than classifiers trained from tweets annotated with emoticons, a popular distant supervision approach for Twitter sentiment analysis.

text: Acquiring and Exploiting Lexical Knowledge for Twitter Sentiment Analysis The most popular sentiment analysis task in Twitter is the automatic classification of tweets into sentiment categories such as positive, negative, and neutral. State-of-the-art solutions to this problem are based on supervised machine learning models trained from manually annotated examples. These models are affected by label sparsity, because the manual annotation of tweets is labour-intensive and time-consuming.

This thesis addresses the label sparsity problem for Twitter polarity classification by automatically building two type of resources that can be exploited when labelled data is scarce: opinion lexicons, which are lists of words labelled by sentiment, and synthetically labelled tweets.

In the first part of the thesis, we induce Twitter-specific opinion lexicons by training words level classifiers using representations that exploit different sources of information: (a) the morphological information conveyed by part-of-speech (POS) tags, (b) associations between words and the sentiment expressed in the tweets that contain them, and (c) distributional representations calculated from unlabelled tweets. Experimental results show that the induced lexicons produce significant improvements over existing manually annotated lexicons for tweet-level polarity classification.

In the second part of the thesis, we develop distant supervision methods for generating synthetic training data for Twitter polarity classification by exploiting unlabelled tweets and prior lexical knowledge. Positive and negative training instances are generated by averaging unlabelled tweets annotated according to a given polarity lexicon. We study different mechanisms for selecting the candidate tweets to be averaged. Our experimental results show that the training data generated by the proposed models produce classifiers that perform significantly better than classifiers trained from tweets annotated with emoticons, a popular distant supervision approach for Twitter sentiment analysis.

year: 2017

title: Evaluation of land in Wairoa District for potential horticultural development

authors: Rau, Johnathon Taukiri

subjects: land suitability

abstract: The Wairoa District, on the East Coast of the North Island of New Zealand has less horticultural development than areas to the north and south. The objective of this study was to provide information to help inform landowners who may wish to invest in horticulture which could improve the economic situation of the district.
Field work involving local scale climate mapping and soil characterisation of areas with potential for horticulture in Wairoa District was completed in 2017. Constructed climate and topographical maps with existing soil maps were evaluated against known crop growth requirements to produce crop potential maps identifying areas with potential for crop production. Horticultural crops included in this study include kiwifruit, apples, cherries. Alternative crops include hemp and poppies.
Between mid-April – 31 October 2017, 45 portable iButton temperature loggers were deployed throughout Land Use Capability classes 1-3 in the Wairoa District and were set to record hourly temperature. When regressed against nearby climate stations, long term (18 -26 years) temperature datasets were derived from the short term iButton datasets. MODIS satellite images were also analysed to help identify areas prone to frost. From the long-term datasets local scale chill hour, growing degree days and October frost risk maps were constructed for the Wairoa District. For the central area from Wairoa to Frasertown, chill hours were estimated to range between 600 – 900, growing degree days 1300 – 1400 and October frost risk less than 25%.
From four representative soil types, horizon samples were taken to determine the soils physical properties including each horizons Total Available Water Holding Capacity (TAWHC). Horizon TAWHC for each soil type were summed to give TAWHC to 1 meter which along with 21 years of estimated daily rainfall and potential evapotranspiration were used in a soil water balance model to estimate seasonal crop irrigation requirements. Irrigation estimates for kiwifruit compared well against published values ranging between mean 204 – 247 mm.
The crop potential maps with seasonal crop irrigation estimates can enhance a land owner’s ability to make informed decisions resulting in economic benefit to whanau, community and the Wairoa District.

text: Evaluation of land in Wairoa District for potential horticultural development The Wairoa District, on the East Coast of the North Island of New Zealand has less horticultural development than areas to the north and south. The objective of this study was to provide information to help inform landowners who may wish to invest in horticulture which could improve the economic situation of the district.
Field work involving local scale climate mapping and soil characterisation of areas with potential for horticulture in Wairoa District was completed in 2017. Constructed climate and topographical maps with existing soil maps were evaluated against known crop growth requirements to produce crop potential maps identifying areas with potential for crop production. Horticultural crops included in this study include kiwifruit, apples, cherries. Alternative crops include hemp and poppies.
Between mid-April – 31 October 2017, 45 portable iButton temperature loggers were deployed throughout Land Use Capability classes 1-3 in the Wairoa District and were set to record hourly temperature. When regressed against nearby climate stations, long term (18 -26 years) temperature datasets were derived from the short term iButton datasets. MODIS satellite images were also analysed to help identify areas prone to frost. From the long-term datasets local scale chill hour, growing degree days and October frost risk maps were constructed for the Wairoa District. For the central area from Wairoa to Frasertown, chill hours were estimated to range between 600 – 900, growing degree days 1300 – 1400 and October frost risk less than 25%.
From four representative soil types, horizon samples were taken to determine the soils physical properties including each horizons Total Available Water Holding Capacity (TAWHC). Horizon TAWHC for each soil type were summed to give TAWHC to 1 meter which along with 21 years of estimated daily rainfall and potential evapotranspiration were used in a soil water balance model to estimate seasonal crop irrigation requirements. Irrigation estimates for kiwifruit compared well against published values ranging between mean 204 – 247 mm.
The crop potential maps with seasonal crop irrigation estimates can enhance a land owner’s ability to make informed decisions resulting in economic benefit to whanau, community and the Wairoa District.

year: 2018