Biology subjects

Ondei, S.

Publications and source records attributed to Ondei, S..

4 recordsLinked to original sources

bioRxiv2022

Landscape functional connectivity for butterflies under different scenarios of land-use, land-cover, and climate change in Australia

Pollinating invertebrates are vital to terrestrial ecosystems but are impacted by anthropogenic habitat loss/fragmentation and climate change. Conserving and improving landscape connectivity is important to offset those threats, yet its assessment for invertebrates is lacking. In this study, we evaluated the functional connectivity between protected areas in Australia for 59 butterfly species, under present conditions and different future scenarios (for 2050 and 2090) of land-use, land-cover, and climate change. Using circuit-theory analysis, we found that functional connectivity under present conditions varies widely between species, even when their estimated geographical ranges are similar. Under future scenarios, functional connectivity is predicted to decrease overall, with negative changes worsening from 2050 to 2090, although a few species are positive exceptions. We have made our results available as spatial datasets to allow comparisons with taxa from other studies and can be used to identify priority areas for conservation in terms of establishing ecological corridors or stepping-stone habitat patches. Our study highlights the importance of considering pollinating invertebrates when seeking holistic conservation and restoration of a landscapes functional connectivity, underscoring the need to expand and promote protected areas to facilitate functional connectivity under future scenarios of global change. Research DataThe habitat suitability maps and functional connectivity maps are made available as GeoTiff images via Figshare (10.6084/m9.figshare.19130078).

ecology↗

bioRxiv2021

Robust, data-driven bioregionalizations emerge from diversity concordance

AimDespite the increasing interest in developing new bioregionalizations and assessing the most widely accepted biogeographic frameworks, no study to date has sought to systematically define a system of small bioregions nested within larger ones that better reflect the distribution and patterns of biodiversity. Here, we examine how an algorithmic, data-driven model of diversity patterns can lead to an ecologically interpretable hierarchy of bioregions. LocationAustralia. Time periodPresent. Major taxa studiedTerrestrial vertebrates and vascular plants. MethodsWe compiled information on the biophysical characteristics and species occupancy of Australias geographic conservation units (bioregions). Then, using cluster analysis to identify groupings of bioregions representing optimal discrete-species areas, we evaluated what a hierarchical bioregionalization system would look like when based empirically on the within-and between-site diversity patterns across taxa. Within an information-analytical framework, we then assessed the degree to which the World Wildlife Funds (WWF) biomes and ecoregions and our suite of discrete-species areas are spatially associated and compared those results among bioregionalization scenarios. ResultsInformation on biodiversity patterns captured was moderate for WWFs biomes (50- 58% for birds beta, and plants alpha and beta diversity, of optimal discrete areas, respectively) and ecoregions (additional 4-25%). Our plants and vertebrate optimal areas retained more information on alpha and beta diversity across taxa, with the two algorithmically derived biogeographic scenarios sharing 86.5% of their within- and between-site diversity information. Notably, discrete-species areas for beta diversity were parsimonious with respect to those for alpha diversity. Main conclusionsNested systems of bioregions must systematically account for the variation of species diversity across taxa if biodiversity research and conservation action are to be most effective across multiple spatial or temporal planning scales. By demonstrating an algorithmic rather than subjective method for defining bioregionalizations using species-diversity concordances, which reliably reflects the distributional patterns of multiple taxa, this work offers a valuable new tool for systematic conservation planning.

ecology↗

bioRxiv2021

Predicted the impacts of climate change and extreme-weather events on the future distribution of fruit bats in Australia

AimFruit bats (Megachiroptera) are important pollinators and seed dispersers whose distribution might be affected by climate change and extreme-weather events. We assessed the potential impacts of those changes, particularly more frequent and intense heatwaves, and drought, on the future distribution of fruit bats in Australia. We also focus a case study on Tasmania, the southernmost island state of Australia, which is currently devoid of fruit bats but might serve as a future climate refugium. LocationAustralia (continental-scale study) and Tasmania. MethodsSpecies distribution modelling was used to predict the occurrence of seven species of fruit bats, using an ensemble of machine-learning algorithms. Predictors included extreme-weather events (heatwave and drought), vegetation (as a proxy for habitat) and bioclimatic variables. Predictions were made for the current-day distribution and future (2050 and 2070) scenarios using multiple emission scenarios and global circulation models. ResultsChanges in climate and extreme-weather events are forecasted to impact all fruit-bat species, with the loss and gain of suitable areas being predominantly along the periphery of a species current distribution. A higher emission scenario resulted in a higher loss of areas for Grey-headed flying fox (Pteropus poliocephalus) and Spectacled flying fox (P. conspicillatus) but a higher gain of areas for the Northern blossom bat (Macroglossus minimus). The Grey-headed flying fox (Pteropus poliocephalus) is the only study species predicted to potentially occur in Tasmania under future scenarios. Main conclusionsFruit bats are likely to respond to climate change and extreme weather by migrating to more suitable areas, including regions not historically inhabited by those species such as Tasmania--possibly leading to human-wildlife conflicts. Conservation strategies (e.g., habitat protection) should focus on areas we found to remain suitable under future scenarios, and not be limited by state-political boundaries.

ecology↗

bioRxiv2021

Association between land cover, plant genera and pollinator dynamics in mixed-use landscapes

Pollinators are globally threatened by land-use change, but its effect varies depending on the taxa and the intensity of habitat degradation. However, pollinator-landscape studies typically focus on regions of intensive human activities and on a few focal species. Evaluating pollinator responses in landscapes with moderate land-use changes and on multiple pollinator groups would therefore fill an important knowledge gap. This study aims to determine the predictive capacity and effect of habitat characteristics on the relative abundance of multiple pollinator groups in mixed-use landscapes. To do this, we collected field data on the relative abundance of nectivorous birds, bees, beetles, and butterflies across the Tasman Peninsula (Tasmania, Australia). We then applied Random Forests to resolve the effects of land use (protected areas, plantation, and pasture), land cover at different radii (100 m and 2000 m), and plant genera on pollinator abundance. Overall, land cover and plant genera were more important predictors of pollinator abundance than land use. And the effect of land use, land cover, and plant genera varied depending on the pollinating group. Pollinator groups were associated with a range of plant genera, with the native genera Acacia, Leptospermum, Leucopogon, Melaleuca, Pomaderris, and Pultenaea being among the most important predictors. Our results highlight that one size does not fit all--that is pollinator response to different landscape characteristics vary, emphasise the importance of considering multiple habitat factors to manage and support a dynamic pollinator community, and demonstrates how land management can be informed using predictive modelling.

ecology↗