Clive A. McAlpine's research while affiliated with The University of Queensland and other places

... Locally relevant information relating to koala tree species use therefore ensures that councils and conservation groups achieve revegetation outcomes relevant to local populations. Consistent with previous studies, we predicted that koalas (being nocturnal feeders) would use a greater diversity of tree species at night relative to day, and would preferentially select larger trees relative to their availability in the landscape (Pfeiffer et al. 2005;Crowther et al. 2014;Law et al. 2022a;McAlpine et al. 2023). ...

... We used the Conformal Cubic Atmospheric Model (CCAM; Thatcher, 2020) developed by CSIRO (McGregor, 2005;McGregor & Dix, 2008), to dynamically downscale 15 CMIP6 GCMs. Typically, GCMs are downscaled by running the RCM over a limited domain of interest (Giorgi, 2019), however, CCAM is a global stretched grid model, and so runs for the entire globe, while the domain of interest can be at a higher resolution (McGregor, 2015;Syktus & McAlpine, 2016;Trancoso et al., 2022). In comparison to limited-domain RCMs, a stretched grid model provides self-consistent interactions between global and regional scales (Fox-Rabinovitz et al., 2006). ...

... Using Bayes theorem [135], the model calculates the probability of a given outcome for any defined scenario. Bayesian networks are widely used in a range of decision support settings including public health [136,137], environmental conservation [138] and natural resource management [139]. ...

... By focusing on the scale of the four LGAs, we have provided the ecological basis for developing a regional approach to koala conservation. There is strong local community support for koala conservation and recovery measures to help ensure koalas continue to survive in the region (Brown et al., 2018;Brown, McAlpine, et al., 2019;Fielding et al., 2022;Lunney et al., 2022). This study has provided an ecological foundation for a koala conservation strategy for the four LGAs, as well as the neighbouring Kyogle and Richmond Valley ...

... In contrast, mangrove ecosystems in the Mon Region experienced more pronounced impacts from the expansion of aquaculture and charcoal production (Zöckler and Aung, 2019). Meanwhile, in Rakhine and Ayeyarwady zones, the conversion of mangroves to shrimp farms is a significant factor, along with their transformation into rice paddies (Gaw et al., 2018;Yang et al., 2019;De Alban et al., 2020;Tun et al., 2021). The coastal zones of Ayeyarwady, Yangon, and Mon are the most critical areas experiencing deforestation of mangroves . ...

... Several studies have also contributed towards analysing Kalimantan's climatic condition on land surface temperature (LST). Chapman et al. (2020) assessed the impact of deforestation in Kalimantan by analysing the rainfall and temperature using data derived from Conformal Cubic Atmospheric Model (CCAM). McAlpine et al. (2018) used the LST mODIS data to analysed the impact of forest loss in Kalimantan on its temperature. ...

... If natural forest ecosystems have indeed evolved mechanisms to stabilize and sustain the continental water cycle, their destruction contributes to the destabilization and impoverishment of regional water cycles and climates. This contribution is underestimated (Sheil et al., 2019). Future studies of vegetation cover impacts on atmospheric moisture flows must emphasize the role of natural forests (Zemp et al., 2017a;Sheil, 2018;Makarieva et al., 2020;Leite-Filho et al., 2021;Hua et al., 2022). ...

... Women also respond more often to studies on green areas in the Nordic region 47 . While socio-demographic factors can influence the number of points assigned to a map, socio-demographic biases wrought by different sampling techniques (e.g., convenience vs. representative), have been found to have little influence on location accuracy of SV 55 . It was difficult to test this hypothesis in the current paper given that most data were collated through a convenience sample. ...

... In the analysis phase, values of each survey respondent are aggregated together to represent society, hence forming 'social values' or SV 32 . PPGIS and spatial conservation prioritization approaches have been combined to spatially identify synergies and trade-offs between SV and biophysical values for biodiversity, whereby SV for biodiversity are generated using PPGIS and biophysical values are generated using either scoring or complementary methods for assessing conservation priorities 15,28,[33][34][35] . Socially acceptable and scientifically defensible priorities for conservation can be derived from the spatial overlays of high-high, and high-low value areas of social and biophysical value 28,34 , enabling the identification of specific geographic locations where conservation options are supported or opposed 28 . ...

... 312 313 This spatial and tabular dataset is ideal for users that would like to understand how the habitat suitability 314 of areas for species is predicted to change over time or under different climate scenarios. Due to its 5km 2 315 spatial resolution, the data is best for understanding broader spatial trends that can be integrated into 316 spatial planning (Maxwell et al., 2019), rather than more local management such as identifying specific 317 sites for translocation (Eyre et al., 2022). For example, as presented above, these maps can be combined 318 to evaluate how habitat suitability changes over time ( Figure 6) or over space and time ( Figure 5), which 319 can then be considered into conservation or monitoring plans in areas which are predicted to lose or gain 320 suitable areas for the species. ...