title: Variable detectability in long-term population surveys of small mammals

authors: Watkins, Alison Fern

subjects: abundance

abstract: Simple survey methods for small mammals, such as indices of trap captures per unit effort, are often the only practicable means of monitoring populations over the long term and at landscape scale and the only source of valuable historical data. They include two fundamental assumptions about the target populations (uniform distribution and equal detectability). Concern has often been expressed that, if these assumptions are violated, conventional density indices could give misleading results. Site occupancy analysis (SOA) can detect significantly uneven distribution of local populations (from variation in probability of occupancy) and reliability of indices of abundance (from variation in detectability) without requiring enumeration. We use this method to examine standardised capture records from long-term population surveys of non-commensal house mice (Mus musculus), ship rats (Rattus rattus), Norway rats (Rattus norvegicus) and stoats (Mustela erminea), sampled in four representative temperate forest habitats in New Zealand. Best fit models generated by SOA were consistent with (1) constant or random probability of occupancy for stoats and dynamic equilibrium probability of occupancy for most populations of mice and rats; (2) widespread site-specific variation in probability of detection, especially substantial in rats and correlated with habitat covariates; (3) direct correlations between detectability and density index in mice and rats sampled at 50 m intervals over 3 days, probably because the effects on the density index of variation in numbers available to be caught (population size) were much larger than the effects of changes in catchability (individual behaviour); (4) declines after 6 days in detectability of stoats and rats sampled at 3–400 m intervals over 10 days, attributed to a local trap-out effect. Longer-term variations in the density index were consistent with observed changes in reproductive parameters and age structure that are known to follow variations in real numbers. We conclude that violations of the assumptions of uniform distribution and equal detectability, while real, were not sufficient to prevent these data from providing information adequate for (1) short-term population assessments (2) long-term, low-level monitoring and (3) preliminary modelling.

text: Variable detectability in long-term population surveys of small mammals Simple survey methods for small mammals, such as indices of trap captures per unit effort, are often the only practicable means of monitoring populations over the long term and at landscape scale and the only source of valuable historical data. They include two fundamental assumptions about the target populations (uniform distribution and equal detectability). Concern has often been expressed that, if these assumptions are violated, conventional density indices could give misleading results. Site occupancy analysis (SOA) can detect significantly uneven distribution of local populations (from variation in probability of occupancy) and reliability of indices of abundance (from variation in detectability) without requiring enumeration. We use this method to examine standardised capture records from long-term population surveys of non-commensal house mice (Mus musculus), ship rats (Rattus rattus), Norway rats (Rattus norvegicus) and stoats (Mustela erminea), sampled in four representative temperate forest habitats in New Zealand. Best fit models generated by SOA were consistent with (1) constant or random probability of occupancy for stoats and dynamic equilibrium probability of occupancy for most populations of mice and rats; (2) widespread site-specific variation in probability of detection, especially substantial in rats and correlated with habitat covariates; (3) direct correlations between detectability and density index in mice and rats sampled at 50 m intervals over 3 days, probably because the effects on the density index of variation in numbers available to be caught (population size) were much larger than the effects of changes in catchability (individual behaviour); (4) declines after 6 days in detectability of stoats and rats sampled at 3–400 m intervals over 10 days, attributed to a local trap-out effect. Longer-term variations in the density index were consistent with observed changes in reproductive parameters and age structure that are known to follow variations in real numbers. We conclude that violations of the assumptions of uniform distribution and equal detectability, while real, were not sufficient to prevent these data from providing information adequate for (1) short-term population assessments (2) long-term, low-level monitoring and (3) preliminary modelling.

year: 2009

title: Mining Arbitrarily Large Datasets Using Heuristic k-Nearest Neighbour Search

authors: Wu, Xing

subjects: computer science

abstract: Nearest Neighbour Search (NNS) is one of the top ten data mining algorithms. It is simple and effective but has a time complexity that is the product of the number of instances and the number of dimensions. When the number of dimensions is greater than two there are no known solutions that can guarantee a sublinear retrieval time. This paper describes and evaluates two ways to make NNS efficient for datasets that are arbitrarily large in the number of instances and dimensions. The methods are best described as heuristic as they are neither exact nor approximate. Both stem from recent developments in the field of data stream classification. The first uses Hoeffding Trees, an extension of decision trees to streams and the second is a direct stream extension of NNS. The methods are evaluated in terms of their accuracy and the time taken to find the neighbours. Results show that the methods are competitive with NNS in terms of accuracy but significantly faster.

text: Mining Arbitrarily Large Datasets Using Heuristic k-Nearest Neighbour Search Nearest Neighbour Search (NNS) is one of the top ten data mining algorithms. It is simple and effective but has a time complexity that is the product of the number of instances and the number of dimensions. When the number of dimensions is greater than two there are no known solutions that can guarantee a sublinear retrieval time. This paper describes and evaluates two ways to make NNS efficient for datasets that are arbitrarily large in the number of instances and dimensions. The methods are best described as heuristic as they are neither exact nor approximate. Both stem from recent developments in the field of data stream classification. The first uses Hoeffding Trees, an extension of decision trees to streams and the second is a direct stream extension of NNS. The methods are evaluated in terms of their accuracy and the time taken to find the neighbours. Results show that the methods are competitive with NNS in terms of accuracy but significantly faster.

year: 2008

title: Aroha

authors: Maxwell, Te Kahautu

abstract: Waita Tira (choral), Lyrics composed by Te Kahautu Maxwell and Performed by Ōpotoki Mai Tawhiti (Kapa Haka group)

Waiata ā Tira: Aroha
Whakaeke: Pinepine Te Kura, Hau Te Kura
Mōteatea: Te Ao o Te Pārerā
Waiata ā Ringa: E pā to reo, Te Matatini, Herewini Parata
Poi: E ia e ia
Haka: Te Kōtiritiri, Te Kōtaratara
Whakawātea: Ōranga Tōiti, tōnui kōrero

text: Aroha Waita Tira (choral), Lyrics composed by Te Kahautu Maxwell and Performed by Ōpotoki Mai Tawhiti (Kapa Haka group)

Waiata ā Tira: Aroha
Whakaeke: Pinepine Te Kura, Hau Te Kura
Mōteatea: Te Ao o Te Pārerā
Waiata ā Ringa: E pā to reo, Te Matatini, Herewini Parata
Poi: E ia e ia
Haka: Te Kōtiritiri, Te Kōtaratara
Whakawātea: Ōranga Tōiti, tōnui kōrero

year: 2023-02-22