A comparison of long-term flowering patterns of Box–Ironbark species in Havelock and Rushworth forests

Keatley, Marie R.; Hudson, Irene L.

doi:10.1007/s10666-006-9063-5

Cited by 31 publications

(48 citation statements)

References 44 publications

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“…budding, seeding) in Eucalypts (Ashton 1975, Bassett 1995, Murray and Lutze 2004. This phenomenon has also been reported specifically in E. tricarpa (Keatley and Murray 2006). The second sub-component, d2, reflects both the duration and the pattern of the original flowering record, for a given species; as shown also earlier for 4 of the 8 species studied in this chapter by Hudson et al (2010a,b).…”

Section: Subcomponentssupporting

confidence: 81%

Wavelet Signatures of Climate and Flowering: Identification of Species Groupings

Hudson¹,

Keatley²,

Kang³

2011

Discrete Wavelet Transforms - Biomedical Applications

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Section: Subcomponentssupporting

confidence: 81%

Wavelet Signatures of Climate and Flowering: Identification of Species Groupings

Hudson¹,

Keatley²,

Kang³

2011

Discrete Wavelet Transforms - Biomedical Applications

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“…An intensity value of 0-5 was determined for each provenance derived as a function of the quantity of flowers and the proportion of trees in flower from that provenance (modified from Keatley et al (2004) and Keatley and Hudson (2007)). The quantity score ranged from 0 to 3 and was assigned as follows: 0 = no flowering; 0.5 = <100 flowers; 1 = 101-1000 flowers; 2 = 1001-5000 flowers and 3 = >5000 flowers.…”

Section: Statistical Analysis Flowering Intensity and Synchronymentioning

confidence: 99%

“…We report intensity scores on a monthly basis as appropriate for our analysis but they are usually given as an annual average in long-term flowering studies (e.g. Keatley et al 2004;Keatley and Hudson 2007). An intensity score of zero indicated that no tree from the provenance flowered, whereas a score of five indicated flowering was heavy in that month.…”

Section: Statistical Analysis Flowering Intensity and Synchronymentioning

confidence: 99%

“…An intensity score of zero indicated that no tree from the provenance flowered, whereas a score of five indicated flowering was heavy in that month. Peak flowering was defined as the month or months with the highest flowering intensity (Keatley et al 2004;Keatley and Hudson 2007).…”

Section: Statistical Analysis Flowering Intensity and Synchronymentioning

confidence: 99%

“…Flowering intensity is a function of the quantity of flowers and the proportion of trees that flower in a provenance (adapted from Keatley et al (2004) and Keatley and Hudson (2007)). Flowering intensities are indicated as follows: 2 (black fill), 1.5 (grey stippled), 1.0 (light grey fill), 0 (no fill)' and not assessed (-).…”

Section: Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Octmentioning

confidence: 99%

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Genetic control of flowering in spotted gum, Corymbia citriodora subsp. variegata and C. maculata

et al. 2014

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Abstract. Genetically controlled asynchrony in anthesis is an effective barrier to gene flow between planted and native forests. We investigated the degree of genetically controlled variation in the timing of key floral developmental stages in a major plantation species in subtropical Australia, Corymbia citriodora subsp. variegata K.D. Hill and L.A.S Johnson, and its relative C. maculata K.D. Hill and L.A.S. Johnson. Flowering observations were made in a common garden planting at Bonalbo in northern New South Wales in spring on 1855 trees from eight regions over three consecutive years, and monthly on a subset of 208 trees for 12 months. Peak anthesis time was stable over years and observations from translocated trees tended to be congruent with the observations in native stands, suggesting strong genetic control of anthesis time. A cluster of early flowering provenances was identified from the north-east of the Great Dividing Range. The recognition of a distinct flowering race from this region accorded well with earlier evidence of adaptive differentiation of populations from this region and geographically-structured genetic groupings in C. citriodora subsp. variegata. The early flowering northern race was more fecund, probably associated with its disease tolerance and greater vigour. Bud abundance fluctuated extensively at the regional level across 3 years suggesting bud abundance was more environmentally labile than timing of anthesis. Overall the level of flowering in the planted stand (age 12 years) was low (8-12% of assessed trees with open flowers), and was far lower than in nearby native stands. Low levels of flowering and asynchrony in peak anthesis between flowering races of C. citriodora subsp. variegata may partially mitigate a high likelihood of gene flow where the northern race is planted in the south of the species range neighbouring native stands.

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Models of Eucalypt phenology predict bat population flux

Giles

Plowright

Eby

et al. 2016

Ecology and Evolution

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Fruit bats (Pteropodidae) have received increased attention after the recent emergence of notable viral pathogens of bat origin. Their vagility hinders data collection on abundance and distribution, which constrains modeling efforts and our understanding of bat ecology, viral dynamics, and spillover. We addressed this knowledge gap with models and data on the occurrence and abundance of nectarivorous fruit bat populations at 3 day roosts in southeast Queensland. We used environmental drivers of nectar production as predictors and explored relationships between bat abundance and virus spillover. Specifically, we developed several novel modeling tools motivated by complexities of fruit bat foraging ecology, including: (1) a dataset of spatial variables comprising Eucalypt‐focused vegetation indices, cumulative precipitation, and temperature anomaly; (2) an algorithm that associated bat population response with spatial covariates in a spatially and temporally relevant way given our current understanding of bat foraging behavior; and (3) a thorough statistical learning approach to finding optimal covariate combinations. We identified covariates that classify fruit bat occupancy at each of our three study roosts with 86–93% accuracy. Negative binomial models explained 43–53% of the variation in observed abundance across roosts. Our models suggest that spatiotemporal heterogeneity in Eucalypt‐based food resources could drive at least 50% of bat population behavior at the landscape scale. We found that 13 spillover events were observed within the foraging range of our study roosts, and they occurred during times when models predicted low population abundance. Our results suggest that, in southeast Queensland, spillover may not be driven by large aggregations of fruit bats attracted by nectar‐based resources, but rather by behavior of smaller resident subpopulations. Our models and data integrated remote sensing and statistical learning to make inferences on bat ecology and disease dynamics. This work provides a foundation for further studies on landscape‐scale population movement and spatiotemporal disease dynamics.

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A comparison of long-term flowering patterns of Box–Ironbark species in Havelock and Rushworth forests

Cited by 31 publications

References 44 publications

Wavelet Signatures of Climate and Flowering: Identification of Species Groupings

Wavelet Signatures of Climate and Flowering: Identification of Species Groupings

Genetic control of flowering in spotted gum, Corymbia citriodora subsp. variegata and C. maculata

Models of Eucalypt phenology predict bat population flux

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