title: Comparative fish abundance in the shallow Waikato lakes Whangape and Hakanoa

authors: Hicks, Brendan J.

abstract: The Waikato-Tainui College for Research and Development contracted the University of Waikato to conduct boat electrofishing estimates of the fish abundance in lakes Whangape and Hakanoa; both are shallow, riverine lakes in the lower Waikato River floodplain and are about 2,000 years old. Lake Whangape (latitude 37.46853°S, longitude 175.05120°W) is a large (1,450 ha), shallow (maximum depth 2.7 m) lake to the west of the Waikato River; Lake Hakanoa (latitude 37.55258°S, longitude 175.16859°W) is a 52-ha lake in suburban Huntly with a maximum depth of 2.5 m.
We fished 10 sites for 10 mins in both lakes Whangape and Lake Hakanoa; sites fished were 253-609 m long (1,012-2,436 m² in area) in Lake Whangape and 125-276 m long (500-1,104 m² in area) in Lake Hakanoa.
No submerged aquatic macrophytes were seen in either lake. Fishing was conducted close to the water's edge in Lake Hakanoa, but the shallow margins in Lake Whangape restricted fishing at most sites to 30-70 m from the shoreline. The shoreline was much more accessible in Lake Hakanoa, and was dominated by raupo (Typha orientalis) with willows (Salix spp.) in places. Submerged tree trunks and logs in Lake Hakanoa made navigation difficult in parts of the lake margins.
We caught a total of 118 fish in Lake Whangape, where shortfin eels (Anguilla australis) were the most abundant fish, and 594 fish in Lake Hakanoa, where gambusia (Gambusia affinis) the most abundant species. In Lake Whangape, shortfin eels comprised the greatest total biomass (12.4 kg), with koi carp (Cyprinus carpio) almost equally abundant (11.4 kg). In Lake Hakanoa, koi carp were most abundant (74.4 kg), followed by shortfin eels (22.1 kg).
Fish were unevenly spread among sites in Lake Whangape and koi carp were relatively sparsely among sites. In Lake Hakanoa, in contrast, fish were more evenly spread among sites, but with higher concentrations of catfish (Ameiurus nebulosus) and goldfish (Carassius auratus) at a few sites. Biomass of fish caught in each of the ten 10-min fishing shots reflected the numerical distributions, with some wide variations between sites. Fish densities were generally lower in Lake Whangape than in Lake Hakanoa; however, common smelt (Retropinna retropinna) and grey mullet (Mugil cephalus) occurred in Whangape but not in Hakanoa. Areal fish biomass was dominated by shortfin eels and koi carp in Lake Whangape, but koi carp dominated the fish biomass in Lake Hakanoa, despite the smaller number of carp than eels. This was because of the large mean weight of koi carp (about 1,000 g) compared to the smaller shortfin eels (mean weight about 200 g in both lakes).

text: Comparative fish abundance in the shallow Waikato lakes Whangape and Hakanoa The Waikato-Tainui College for Research and Development contracted the University of Waikato to conduct boat electrofishing estimates of the fish abundance in lakes Whangape and Hakanoa; both are shallow, riverine lakes in the lower Waikato River floodplain and are about 2,000 years old. Lake Whangape (latitude 37.46853°S, longitude 175.05120°W) is a large (1,450 ha), shallow (maximum depth 2.7 m) lake to the west of the Waikato River; Lake Hakanoa (latitude 37.55258°S, longitude 175.16859°W) is a 52-ha lake in suburban Huntly with a maximum depth of 2.5 m.
We fished 10 sites for 10 mins in both lakes Whangape and Lake Hakanoa; sites fished were 253-609 m long (1,012-2,436 m² in area) in Lake Whangape and 125-276 m long (500-1,104 m² in area) in Lake Hakanoa.
No submerged aquatic macrophytes were seen in either lake. Fishing was conducted close to the water's edge in Lake Hakanoa, but the shallow margins in Lake Whangape restricted fishing at most sites to 30-70 m from the shoreline. The shoreline was much more accessible in Lake Hakanoa, and was dominated by raupo (Typha orientalis) with willows (Salix spp.) in places. Submerged tree trunks and logs in Lake Hakanoa made navigation difficult in parts of the lake margins.
We caught a total of 118 fish in Lake Whangape, where shortfin eels (Anguilla australis) were the most abundant fish, and 594 fish in Lake Hakanoa, where gambusia (Gambusia affinis) the most abundant species. In Lake Whangape, shortfin eels comprised the greatest total biomass (12.4 kg), with koi carp (Cyprinus carpio) almost equally abundant (11.4 kg). In Lake Hakanoa, koi carp were most abundant (74.4 kg), followed by shortfin eels (22.1 kg).
Fish were unevenly spread among sites in Lake Whangape and koi carp were relatively sparsely among sites. In Lake Hakanoa, in contrast, fish were more evenly spread among sites, but with higher concentrations of catfish (Ameiurus nebulosus) and goldfish (Carassius auratus) at a few sites. Biomass of fish caught in each of the ten 10-min fishing shots reflected the numerical distributions, with some wide variations between sites. Fish densities were generally lower in Lake Whangape than in Lake Hakanoa; however, common smelt (Retropinna retropinna) and grey mullet (Mugil cephalus) occurred in Whangape but not in Hakanoa. Areal fish biomass was dominated by shortfin eels and koi carp in Lake Whangape, but koi carp dominated the fish biomass in Lake Hakanoa, despite the smaller number of carp than eels. This was because of the large mean weight of koi carp (about 1,000 g) compared to the smaller shortfin eels (mean weight about 200 g in both lakes).

year: 2016

title: Popoia te reo kia penapena: Nurture the language

authors: Berryman, Mere

subjects: Maori language

abstract: Research to develop a Māori language screening tool (Specialist Education Services, 2001), identified that students entering Māori Medium at five, could be classified into one of four Māori language competency, groups. Concurrently, teachers raised their need to identify the Māori language proficiency of five year olds entering Māori Medium so that more appropriate teaching strategies could be incorporated in preparation for literacy.
Accordingly, three Māori oral-language assessment tools, to help identify the Māori language competency of students entering Māori Medium settings at five years of age and provide formative information, were developed in response to this need. This paper details the development and trial of these tools.

text: Popoia te reo kia penapena: Nurture the language Research to develop a Māori language screening tool (Specialist Education Services, 2001), identified that students entering Māori Medium at five, could be classified into one of four Māori language competency, groups. Concurrently, teachers raised their need to identify the Māori language proficiency of five year olds entering Māori Medium so that more appropriate teaching strategies could be incorporated in preparation for literacy.
Accordingly, three Māori oral-language assessment tools, to help identify the Māori language competency of students entering Māori Medium settings at five years of age and provide formative information, were developed in response to this need. This paper details the development and trial of these tools.

year: 2009

title: Binding data mining and expert knowledge for one-day-ahead prediction of hourly global solar radiation

authors: del Campo-Ávila, José

subjects: computer science

abstract: A new methodology to predict one-day-ahead hourly solar global radiation is proposed in this paper. This information is very useful to address many real problems; for instance, energy-market decision making is one of the contexts where that information is essential to ensure the correct integration of grid-connected photovoltaic solar systems. The developed methodology is based on the contribution of different experts to obtain improved data-driven models when included in the data mining process. The modelling phase, when models are induced and new patterns can be identified, is the one that most benefits from that expert knowledge. In this case, it is achieved by combining clustering, regression and classification methods that exploit meteorological data (directly measured or predicted by weather services). The developed models have been embedded in a prediction system that offers reliable forecasts on next-day hourly global solar radiation. As a result of the automatic learning process including the knowledge of different experts, 14 different types of day were identified based on the shape of hourly solar radiation throughout a day. The conventional definitions of types of days, that usually consider 4 options, are updated with this new proposal. The next-day prediction of hourly global radiation is obtained in two phases: in the first one, the next-day type is obtained from among the 14 possible types of day; in the second one, values of hourly global radiation are obtained using the centroid of the predicted type of day and extraterrestrial solar radiation. The relative root mean square error of the prediction model is less than 20%, meaning a significant reduction compared to previous models. Moreover, the proposed models can be recognized in the context of eXplainable Artificial Intelligence.

text: Binding data mining and expert knowledge for one-day-ahead prediction of hourly global solar radiation A new methodology to predict one-day-ahead hourly solar global radiation is proposed in this paper. This information is very useful to address many real problems; for instance, energy-market decision making is one of the contexts where that information is essential to ensure the correct integration of grid-connected photovoltaic solar systems. The developed methodology is based on the contribution of different experts to obtain improved data-driven models when included in the data mining process. The modelling phase, when models are induced and new patterns can be identified, is the one that most benefits from that expert knowledge. In this case, it is achieved by combining clustering, regression and classification methods that exploit meteorological data (directly measured or predicted by weather services). The developed models have been embedded in a prediction system that offers reliable forecasts on next-day hourly global solar radiation. As a result of the automatic learning process including the knowledge of different experts, 14 different types of day were identified based on the shape of hourly solar radiation throughout a day. The conventional definitions of types of days, that usually consider 4 options, are updated with this new proposal. The next-day prediction of hourly global radiation is obtained in two phases: in the first one, the next-day type is obtained from among the 14 possible types of day; in the second one, values of hourly global radiation are obtained using the centroid of the predicted type of day and extraterrestrial solar radiation. The relative root mean square error of the prediction model is less than 20%, meaning a significant reduction compared to previous models. Moreover, the proposed models can be recognized in the context of eXplainable Artificial Intelligence.

year: 2021