Google scholar page All papers can also be downloaded from dropbox or researchgate

2018

Xing S., B. C. Bonebrake, L. A. Ashton, R. L. Kitching, M. Cao, Z. Sun, J. C. Ho, A. Nakamura. 2018 Colors of night: climate–morphology relationships of geometrid moths along spatial gradients in southwestern China. Oecologia 188: 537-546

Griffiths H. M.*, L. A. Ashton*, A. E. Walker, F. Hasan, T. Evans, P. Eggleton and C. L. Parr. 2018. Ants are the major agents of nutrient redistribution from tropical rainforests. The Journal of Animal Ecology 87: 293-300. *joint first author.

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2017

Nakamura, A., R. L. Kitching, M. Cao, T. J. Creedy, T. M. Fayle, M. Freiberg, C. N. Hewitt, T. Itioka, L. P. Koh, K. Ma, Y. Malhi, A. Mitchell, V. Novotny, C. M. P. Ozanne, L. Song, H. Wang, and L. A. Ashton. 2017. Forest canopy science: achievements and horizons. Trends in Ecology and Evolution 32: 438-451.

Beck, J., McCain, C. M., Axmacher, J. C., Ashton, L. A., Bärtschi, F., Brehm, G., Choi, S., Cizek, O., Cowell. R. K., Fiedler, K., Francois, C. L., Highland, S,, Holloway, J. D., Intachat, J., Kadlec, T., Kitching, R. L., Maunsell, S. C., Merckx, T., Nakamura, A., Odell, E., Sang, W., Toko, P. S., Zamecnik, J., Zou, Y. & Novotny, V. 2017. Elevational species richness gradients in a hyperdiverse insect taxon: a global meta-study on geometrid moths. Global Ecology and Biogeography 26: 412-424.

We aim to document elevational richness patterns of geometrid moths in a globally replicated, multi-gradient setting; and to test general hypotheses on environmental and spatial effects (i.e., productivity, temperature, precipitation, area, mid-domain effect, and human habitat disturbance) on these richness patterns.

We aim to document elevational richness patterns of geometrid moths in a globally replicated, multi-gradient setting; and to test general hypotheses on environmental and spatial effects (i.e., productivity, temperature, precipitation, area, mid-domain effect, and human habitat disturbance) on these richness patterns.

2016

Colwell, R. K., N. J. Gotelli, L. A. Ashton, J. Beck, G. Brehm, T. M. Fayle, K. Fiedler, M. L. Forister, M. Kessler, R. L. Kitching, P. Klimes, J. Kluge, J. T. Longino, S. C. Maunsell, C. M. McCain, J. Moses, S. Noben, K. Sam, L. Sam, A. M. Shapiro, X. Wang, and V. Novotny. 2016. Midpoint attractors and species richness: Modelling the interaction between environmental drivers and geometric constraints. Ecology Letters 19: 1009-1022.

Geometric constraint models. (a) The classic geometric constraint model illustrated by a physical analogy: a set of pencils (species), some shorter and some longer (narrower and wider elevational ranges), stored in a schoolchild's old-fashioned pencil-box (the bounded elevational domain) (Colwell et al . 2004 ). If the box is shaken end to end, horizontally, so that the position of each pencil is randomised, the expected number E ( n ) of pencils that overlap (species richness) near the middle of the box is inevitably greater than the number that overlap nearer the ends of the box, a pattern that is symmetric around the centre of the box. But the constraint does not act uniformly on the pencils as the box is shaken: the shorter pencil stubs move more widely and freely than the longer pencils. By analogy, the distribution of small-ranged species is less constrained by geometry than the distribution of large-ranged species (Colwell & Lees 2000 ; Dunn et al . 2007 ). (b) A physical analogy for the midpoint attractor model . Suppose that each pencil has a steel ball bearing embedded at its midpoint (blue circles). A magnetic field, the attractor, is applied across the pencil box (green). As the box is shaken end to end, the pencils tend to collect near the attractor, as their midpoint ball bearings are drawn towards the magnet. If the attractor is located near one end of the box, as illustrated, the expected number of pencils E ( n ) that stack up at any location along the length of the pencil box is asymmetric. However, because the midpoints of the longer pencils cannot align with the magnet (since longer pencils abut the end of the box), the peak of E ( n ) does not coincide with the centre of the attractor. Thus E ( n ) is influenced jointly by the attractor (the magnet) and the constraint (the limits of the pencil box). The pattern of E ( n ) is narrow when the attractor is strong, broad when the attractor is weak.

Geometric constraint models. (a) The classic geometric constraint model illustrated by a physical analogy: a set of pencils (species), some shorter and some longer (narrower and wider elevational ranges), stored in a schoolchild's old-fashioned pencil-box (the bounded elevational domain) (Colwell et al. 2004). If the box is shaken end to end, horizontally, so that the position of each pencil is randomised, the expected number E(n) of pencils that overlap (species richness) near the middle of the box is inevitably greater than the number that overlap nearer the ends of the box, a pattern that is symmetric around the centre of the box. But the constraint does not act uniformly on the pencils as the box is shaken: the shorter pencil stubs move more widely and freely than the longer pencils. By analogy, the distribution of small-ranged species is less constrained by geometry than the distribution of large-ranged species (Colwell & Lees 2000; Dunn et al. 2007). (b) A physical analogy for the midpoint attractor model. Suppose that each pencil has a steel ball bearing embedded at its midpoint (blue circles). A magnetic field, the attractor, is applied across the pencil box (green). As the box is shaken end to end, the pencils tend to collect near the attractor, as their midpoint ball bearings are drawn towards the magnet. If the attractor is located near one end of the box, as illustrated, the expected number of pencils E(n) that stack up at any location along the length of the pencil box is asymmetric. However, because the midpoints of the longer pencils cannot align with the magnet (since longer pencils abut the end of the box), the peak of E(n) does not coincide with the centre of the attractor. Thus E(n) is influenced jointly by the attractor (the magnet) and the constraint (the limits of the pencil box). The pattern of E(n) is narrow when the attractor is strong, broad when the attractor is weak.

Ashton, L. A., A. Nakamura, C. J. Burwell, Y. Tang, M. Cao, T. Whitaker, Z. Sun, H. Huang & R. L. Kitching. 2016. Elevational sensitivity in an Asian ‘hotspot’: moth diversity across elevational gradients in tropical, sub-tropical and sub-alpine China. Scientific Reports 6: 26513.

Leach, E., C. J. Burwell, L. A. Ashton, D. Jones and R. L. Kitching. 2016. Comparison of point counts and automated acoustic monitoring: detecting birds in a rainforest biodiversity survey. Emu 116: 305-309.

Bassian Thrush ( JJ Harrison)

Bassian Thrush (JJ Harrison)

Ashton, L. A., A. Nakamura, Y. Basset, C. J. Burwell, M. Cao, R. Eastwood, E. Odell, E. G. d. Oliveira, K. Hurley, M. Katabuchi, S. Maunsell, J. McBroom, J. Schmidl, Z. H. Sun, Y. Tang, T. Whitaker, M. J. Laidlaw, W. J. F. McDonald, and R. L. Kitching. 2016. Vertical stratification of moth across elevation and latitude. Journal of Biogeography 43: 59-69.

Proportional difference in estimated species richness between canopy and understorey strata of 64 moth data sets collected from 13 locations from Northern and Southern Hemispheres, plotted against ‘corrected’ elevation (see Methods for more details). Closed points are Northern Hemisphere and open points Southern Hemisphere locations. Positive values indicate that more estimated species in the canopy and negative values more in the understorey.

Proportional difference in estimated species richness between canopy and understorey strata of 64 moth data sets collected from 13 locations from Northern and Southern Hemispheres, plotted against ‘corrected’ elevation (see Methods for more details). Closed points are Northern Hemisphere and open points Southern Hemisphere locations. Positive values indicate that more estimated species in the canopy and negative values more in the understorey.

2015

Ashton, L. A., E. H. Odell, C. J. Burwell, S. C. Maunsell, A. Nakamura, W. J. F. McDonald, and R. L. Kitching. 2015. Altitudinal patterns of moth diversity in tropical and subtropical rainforest. Austral Ecology 42: 197-208.

NMDS ordinations for (a) Border Ranges, (b) Lamington, (c) Eungella and (d) ML altitudinal transects with superimposed vectors. Only the significant variables were incorporated into this visual summary of variables that correlate with the observed moth assemblage pattern. The direction of each vector line indicates the positive or negative direction of the trend, and the length of each vector line indicates the strength of the relationship.

NMDS ordinations for (a) Border Ranges, (b) Lamington, (c) Eungella and (d) ML altitudinal transects with superimposed vectors. Only the significant variables were incorporated into this visual summary of variables that correlate with the observed moth assemblage pattern. The direction of each vector line indicates the positive or negative direction of the trend, and the length of each vector line indicates the strength of the relationship.

Odell, E. H., L. A. Ashton, and R. L. Kitching. 2015. Elevation and moths in acentral eastern Queensland rainforest. Austral Ecology 42: 133-144.

Nakamura, A., C. J. Burwell, L. A. Ashton, M. J. Laidlaw, M. Katabuchi, and R. L. Kitching. 2015. Identifying indicator species of elevation: Comparing the utility of woody plants, ants and moths for long-term monitoring. Austral Ecology 42: 177-188.

2014

Ashton, L. A., H. S. Barlow, A. Nakamura, and R. L. Kitching. 2014. Diversity in tropical ecosystems: the species richness and turnover of moths in Malaysian rainforests. Insect Conservation and Diversity 8: 132-142.

2013

Kitching, R. L., and L. A. Ashton. 2013. Predictor sets and biodiversity assessments: the evolution and application of an idea. Pacific Conservation Biology 19: 418-426.

Y. Ji*, Ashton, L.*, S. M. Pedley*, D. P. Edwards*, Y. Tang, A. Nakamura, R. Kitching, P. M. Dolman, P. Woodcock, F. A. Edwards, T. H. Larsen, W. W. Hsu, S. Benedick, K. C. Hamer, D. S. Wilcove, C. Bruce, X. Wang, T. Levi, M. Lott, B. C. Emerson, and D. W. Yu*. 2013. Reliable, verifiable and efficient monitoring of biodiversity via metabarcoding. Ecology Letters 16: 1245-1257.

*These authors contributed equally to this study

Non-metric multidimensional scaling (NMDS) ordinations. Points are census sites, and coloured ellipses are 95% confidence intervals of species centroids foreach treatment level [‘ordiellipses’ (Oksanenet al. 2012)]. These ordinations are for visualisation; all statistical tests of treatment effects are conducted using mvabund (see text for details). (a, b) Ailaoshan. Altitude and Strata (canopy, ground) effects on moth communities. Sites within the same altitude are connected by line segments, with ellipses drawn for each combination of altitude and stratum.

Non-metric multidimensional scaling (NMDS) ordinations. Points are census sites, and coloured ellipses are 95% confidence intervals of species centroids foreach treatment level [‘ordiellipses’ (Oksanenet al. 2012)]. These ordinations are for visualisation; all statistical tests of treatment effects are conducted using mvabund (see text for details). (a, b) Ailaoshan. Altitude and Strata (canopy, ground) effects on moth communities. Sites within the same altitude are connected by line segments, with ellipses drawn for each combination of altitude and stratum.

Kitching, R. L., L. A. Ashton, C. J. Burwell, S. L. Boulter, P. Greenslade, M. J. Laidlaw, C. L. Lambkin, S. C. Maunsell, A. Nakamura, and F. Ødegaard. 2013a. Sensitivity and threat in high elevation rainforests: outcomes and consequences of the IBISCA-Queensland Project. in M. Lowman, editor. Canopies in danger. Springer-Verlag, Berlin.

Kitching, R. L., L. A. Ashton, A. Nakamura, T. Whitaker, and C. V. Khen. 2013b. Distance-driven species turnover in Bornean rainforests: homogeneity and heterogeneity in primary and post-logging forests. Ecography 36: 675-682

Relationships between geographical distance and (a) Sørensen and (b) Chao–Sørensen similarity values using moth assemblages collected from primary (triangles) and post-logging secondary (circles) forest. Shaded points represent similarity values based on comparisons of moth assemblages across the two years of the study. Trend lines were drawn for primary (solid line) and secondary (dotted line) forests.

Relationships between geographical distance and (a) Sørensen and (b) Chao–Sørensen similarity values using moth assemblages collected from primary (triangles) and post-logging secondary (circles) forest. Shaded points represent similarity values based on comparisons of moth assemblages across the two years of the study. Trend lines were drawn for primary (solid line) and secondary (dotted line) forests.

2011

 

 

 

Ashton, L. A., R. L. Kitching, S. Maunsell, D. Bito, and D. Putland. 2011. Macrolepidopteran assemblages along an altitudinal gradient in subtropical rainforest - exploring indicators of climate change. Memoirs of the Queensland Museum 55: 375-389.

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Kitching, R. L., D. Putland, L. A. Ashton, M. J. Laidlaw, S. L. Boulter, H. Christensen, and C. L. Lambkin. 2011. Detecting biodiversity changes along climatic gradients: the IBISCA Queensland Project. Memoirs of the Queensland Museum 55: 235-250.

 

Conference papers

BES – British Ecological Society Meeting 2016 –  Annual Conference. Symposium presentation - Forest canopy science: achievements and horizons. Liverpool, UK.

International Canopy Conference 2016 – Plenary Speaker. Forest canopy science: achievements and horizons. London, UK. 

ATBC – Association for Tropical Biology 2016 – Annual Conference. Symposium organizer - Ecosystem manipulation experiments for understanding human disturbance. Montpelier, France.

ATBC – Association for Tropical Biology 2016 – Annual Conference. Symposium presentation. Sensitivity in an Asian 'Hotspot': moth diversity across elevational gradients is tropical, sub-tropical and sub-alpine China. Montpelier, France.

BES – British Ecological Society Meeting 2015 – Symposium presentation. Moths and mountains: Disentangling the drivers of diversity and creating base-line data for climate change monitoring. Edinburgh, UK.

International Canopy Workshop 2015 – Symposium presentation. Canopies, compartments and continents: food-web structure across spatial scales. Xishangbanna Tropical Botanic Garden, Mengla, Yunnan, China.

ATBC – Association for Tropical Biology 2014 – Annual Conference. Symposium presentation. Multi-taxa inventories in Australia and China. Cairns, Australia.

ATBC – Association for Tropical Biology 2014 – Annual Conference. Symposium presentation. What’s up is up: vertical stratification of moths is universal. Cairns, Australia.

Environmental Futures Centre 2011 – Annual Conference. Symposium presentation. Altitudinal gradients and patterns of moth diversity in subtropical and tropical rainforests. Mt Tamborine, Australia.

ESA – Ecological Society of Australia 2011 – Annual Conference. Symposium presentation. Altitudinal patterns in rainforests – a comparison of three different biomes. Tasmania, Australia.

ESA – Ecological Society of Australia – 2010 Annual Conference. Symposium presentation. Moths as indicators of climate change – investigating species with restricted altitudinal distributions. Canberra, Australia.

INTECOL – International Congress of Ecology 2009 – Symposium presentation. Moth diversity along a sub-tropical altitudinal gradient: family patterns and indicators of climate change. Brisbane, Australia.

INTECOL – International Congress of Ecology 2009 – Poster presentation. The change in macro-lepidopteran assemblages along a fine-scale altitudinal gradient in subtropical rainforest. Brisbane, Australia.