Search

Background: Use of vegetation in urban areas for climate change adaptation is becoming increasingly important; however, urban vegetation is itself vulnerable to the effects of climate change. Better understanding of which species will survive and thrive in urban areas with projected climate change will increase confidence in choosing climate-ready species for resilient urban greening outcomes. Plant selector tools based on the suitability of species for future climates, however, are lacking. Methods: The Which Plant Where plant selector webtool (www.whichplantwhere.com.au) was created by combining sophisticated species distribution models and trait and environmental tolerance data from a variety of sources to allow users to select appropriate species which are climatically suitable for Australian urban environments for 3 different time periods (2030, 2050, and 2070). The tool allows users to calculate co-benefits afforded by planting palettes and offers suggestions for alternative species based on climate suitability to help diversify plantings and provide options where substitutions may have to be made. Results: The tool contains information for over 2,500 unique plant entries (encompassing species, subspecies, cultivars, varieties, and hybrids) from 9 different growth forms (trees, shrubs, palms, ferns, cycads, climbers, succulents, grass, and herbs). The tool contains many resources to design and maintain resilient urban green spaces, from the planning stage up to monitoring and maintenance. Conclusion: Which Plant Where was designed to allow practitioners and urban forest managers to confidently identify climate-ready species now to ensure urban green spaces remain diverse and resilient into the future. [ABSTRACT FROM AUTHOR]

The expansions and contractions of a species' range in response to temporal changes in selective filters leave genetic signatures that can inform a more accurate reconstruction of their evolutionary history across the landscape. After a long period of continental decline, Australian rainforests settled into localized patterns of contraction or expansion during the climatic fluctuations of the Quaternary. The environmental impacts of recurring glacial and interglacial periods also intensified the arrival of new lineages from the Sunda shelf, and it can be expected that immigrant versus locally persistent taxa responded to environmental challenges in quantifiably different manner. To investigate how such differences impact on species' distribution, we contrast landscape genomic patterns and changes in habitat availability between a species with a long continental history on Doryphora sassafras and a Sunda‐derived species (Toona ciliata), across a distributional overlap. Extensive landscape‐level homogeneity across chloroplast and nuclear genomes for the Sunda‐derived T. ciliata, characterize the genetic signature of a very recent invasion and a rapid southern "exploratory" expansion that had not been previously recorded in the Australian flora (i.e., of Gondwanan origin or Sahul‐derived). In contrast, D. sassafras is consistent with other Sahul‐derived species characterized by strong geographical divergence and regional differentiation. Interestingly, our findings suggest that admixture between genetically divergent populations during expansion events might be a contributing factor to the successful colonization of novel habitats. Overall, this study identifies some of the mechanisms regulating the rearrangements in species distributions and assemblage composition that follow major environmental shifts, and reminds us how a species' current range might not necessarily define species' habitat preference, with the consequence that estimates of past or future range might not always be reliable. [ABSTRACT FROM AUTHOR]

Aim: Correlative species distribution models (SDMs) are among the most frequently used tools for conservation planning under climate and land use changes. Conservation‐focused climate change studies are often conducted on a national or local level and can use different sources of occurrence records (e.g., local databases, national biodiversity monitoring) collated at different geographic extents. However, little is known about how these restrictions in geographic space (i.e., Wallacean shortfall) can lead to restrictions in environmental space (i.e., Hutchinsonian shortfall) and accordingly affect conclusions about a species' vulnerability to climate change. Location: Americas with a focus on Mexico. Methods: We present an example study constructing SDMs for three Mexican tree species (Alnus acuminata, Liquidambar styraciflua and Quercus xalapensis) using datasets collated at a global (Americas), national (Mexico) and local (cloud forests of eastern Mexico) level to demonstrate the potential effects of a Wallacean shortfall on the estimation of the environmental niche—and thus on a Hutchinsonian shortfall—its projection in space and time and, consequently, on species' potential vulnerability to climate change. Results: The consequence of using the three datasets was species‐specific and strongly depended on the extent to which the Wallacean shortfall affected estimations of environmental niches (i.e., Hutchinsonian shortfall). Where restrictions in geographic space lead to an underestimation of the environmental niche, vulnerability to climate change was estimated to be substantially higher. Additionally, the restrictions in geographic space may increase the likelihood of issues with non‐analogue climates, increasing model uncertainty. Main conclusion: We recommend assessing the extent to which a species' entire realized environmental niche is captured within the target conservation area, and increasing the geographic extent, if needed, to account for environments and occurrences reflecting potential future conditions. This way, the risk of underestimating the climatic potential of the species (i.e., Hutchinsonian shortfall), as well as the errors induced by extrapolation into "locally novel" climates, can be minimized. [ABSTRACT FROM AUTHOR]

The ENMTools software package was introduced in 2008 as a platform for making measurements on environmental niche models (ENMs, frequently referred to as species distribution models or SDMs), and for using those measurements in the context of newly developed Monte Carlo tests to evaluate hypotheses regarding niche evolution. Additional functionality was later added for model selection and simulation from ENMs, and the software package has been quite widely used. ENMTools was initially implemented as a Perl script, which was also compiled into an executable file for various platforms. However, the package had a number of significant limitations; it was only designed to fit models using Maxent, it relied on a specific Perl distribution to function, and its internal structure made it difficult to maintain and expand. Subsequently, the R programming language became the platform of choice for most ENM studies, making ENMTools less usable for many practitioners. Here we introduce a new R version of ENMTools that implements much of the functionality of its predecessor as well as numerous additions that simplify the construction, comparison and evaluation of niche models. These additions include new metrics for model fit, methods of measuring ENM overlap, and methods for testing evolutionary hypotheses. The new version of ENMTools is also designed to work within the expanding universe of R tools for ecological biogeography, and as such includes greatly simplified interfaces for analyses from several other R packages. [ABSTRACT FROM AUTHOR]

Tephritid fruit flies are among the most destructive horticultural pests posing risks to Australia's multi-billion-dollar horticulture industry. Currently, there are 11 pest fruit fly species of economic concern in Australia. Of these, nine are native to this continent (Bactrocera aquilonis, B. bryoniae, B. halfordiae, B. jarvisi, B. kraussi, B. musae, B. neohumeralis, B. tryoni and Zeugodacus cucumis), while B. frauenfeldi and Ceratitis capitata are introduced. To varying degrees these species are costly to Australia's horticulture through in-farm management, monitoring to demonstrate pest freedom, quarantine and trade restrictions, and crop losses. Here, we used a common species distribution model, Maxent, to assess climate suitability for these 11 species under baseline (1960–1990) and future climate scenarios for Australia. Projections indicate that the Wet Tropics is likely to be vulnerable to all 11 species until at least 2070, with the east coast of Australia also likely to remain vulnerable to multiple species. While the Cape York Peninsula and Northern Territory are projected to have suitable climate for numerous species, extrapolation to novel climates in these areas decreases confidence in model projections. The climate suitability of major horticulture areas currently in eastern Queensland, southern-central New South Wales and southern Victoria to these pests may increase as climate changes. By highlighting areas at risk of pest range expansion in the future our study may guide Australia's horticulture industry in developing effective monitoring and management strategies. [ABSTRACT FROM AUTHOR]

Knowledge of threatened species' distributions is essential for effective conservation decision‐making. Species distribution models (SDMs) are widely used to map species' geographic ranges, identify new areas of suitable habitat and guide field surveys. In New South Wales (NSW), Australia, there are grave doubts about whether populations of the critically endangered long‐footed potoroo (Potorous longipes) remain extant, and identification of occupied sites is a high priority for its conservation. We used an SDM (Maxent) to identify regions in NSW that may have suitable habitat for the potoroo. The SDM was built with seven climate layers and had strong predictive performance (cross‐validated AUC = 0.94). We then combined this information on habitat suitability with vegetation and topography, to identify 58 survey sites across NSW. From April 2016 to May 2017, we undertook six field trips deploying six to eight cameras at each site for 52–63 days, resulting in 25 120 camera trap nights. A total of 215 759 images captured 43 native and feral animal species, but no long‐footed potoroos. Following the survey, newly available, independent presence and absence data were used to validate our model. A Kruskal–Wallis H test indicated that habitat suitability values were significantly higher at presence locations than absence locations (H = 58.66, d.f. = 1, P < 0.001). Finally, we refitted the Maxent model with the new data and identified additional regions that future surveys could explore. We conclude, however, that if the long‐footed potoroo remains extant in NSW, it is extremely rare. [ABSTRACT FROM AUTHOR]

Societal Impact Statement: Urban forests are recognized for the multiple benefits they provide to city‐dwellers. However, climate change will affect tree species survival and persistence in urban ecosystems. Tree failures will cause economic losses and jeopardize the delivery of societal benefits. The impacts of climate change will depend on the species' resilience and adaptive capacity, as well as management actions which may ameliorate some of the negative impacts. Here, we assessed the potential vulnerability of Australia's urban forests to climate extremes. Our results can be used for future urban planning aiming to incorporate species that are well‐adapted to the hotter, drier climates expected with climate change. SummaryUrban forests (UFs) are recognized for the multiple benefits they provide to city‐dwellers. However, global climate change—particularly predicted increases in the frequency and intensity of heatwaves and drought—will affect tree species' performance and survival in urban ecosystems.Here, we assessed species composition and potential vulnerability of UFs in 22 Australian significant urban areas (SUAs) to heat and/or moisture stress. We quantified species' realized climatic niches across their known distribution, and assessed the extent to which baseline climate in the SUAs where a particular species is planted fell within its niche. We used three environmental variables to group species based on their potential climate vulnerability.UFs varied in species composition and climate vulnerability across the continent. In general, neither climate similarity nor geographical proximity were good predictors of species composition among UFs. Of 1,342 tree species assessed (68.4% natives), 53% were considered potentially vulnerable to heat and/or moisture stress in at least one city where they are currently planted.Our results highlight the climate vulnerability of current plantings across Australian SUAs and can be used to direct future species selection that considers the species' climate of origin and climatic niche. UF planning can incorporate species from SUAs with similar climates and with low vulnerability to contemporary, as well as future climate conditions. Species with high climate vulnerability, in contrast, may require more intensive management to avoid failure under future hotter, drier climate conditions. [ABSTRACT FROM AUTHOR]

Online access to species occurrence records has opened new windows into investigating biodiversity patterns across multiple scales. The value of these records for research depends on their spatial, temporal, and taxonomic quality. We assessed temporal patterns in records from the Australasian Virtual Herbarium, asking: (1) How temporally consistent has collecting been across Australia? (2) Which areas of Australia have the most reliable records, in terms of temporal consistency and inventory completeness? (3) Are there temporal trends in the completeness of attribute information associated with records? We undertook a multi-step filtering procedure, then estimated temporal consistency and inventory completeness for sampling units (SUs) of 50 km × 50 km. We found temporal bias in collecting, with 80% of records collected over the period 1970-1999. South-eastern Australia, the Wet Tropics in north-east Queensland, and parts of Western Australia have received the most consistent sampling effort over time, whereas much of central Australia has had low temporal consistency. Of the SUs, 18% have relatively complete inventories with high temporal consistency in sampling. We also determined that 25% of digitized records had missing attribute information. By identifying areas with low reliability, we can limit erroneous inferences about distribution patterns and identify priority areas for future sampling. [ABSTRACT FROM AUTHOR]

The ability of species to track their climate niche is dependent on their dispersal potential and the connectivity of the landscape matrix linking current and future suitable habitat. However, studies modeling climate-driven range shifts rarely address the movement of species across landscapes realistically, often assuming “unlimited” or “no” dispersal. Here, we incorporate dispersal rate and landscape connectivity with a species distribution model (Maxent) to assess the extent to which the Cunningham’s skink (Egernia cunninghami) may be capable of tracking spatial shifts in suitable habitat as climate changes. Our model was projected onto four contrasting, but equally plausible, scenarios describing futures that are (relative to now) hot/wet, warm/dry, hot/with similar precipitation and warm/wet, at six time horizons with decadal intervals (2020–2070) and at two spatial resolutions: 1 km and 250 m. The size of suitable habitat was projected to decline 23–63% at 1 km and 26–64% at 250 m, by 2070. Combining Maxent output with the dispersal rate of the species and connectivity of the intervening landscape matrix showed that most current populations in regions projected to become unsuitable in the medium to long term, will be unable to shift the distance necessary to reach suitable habitat. In particular, numerous populations currently inhabiting the trailing edge of the species’ range are highly unlikely to be able to disperse fast enough to track climate change. Unless these populations are capable of adaptation they are likely to be extirpated. We note, however, that the core of the species distribution remains suitable across the broad spectrum of climate scenarios considered. Our findings highlight challenges faced by philopatric species and the importance of adaptation for the persistence of peripheral populations under climate change. [ABSTRACT FROM AUTHOR]

One of the major uncertainties of 21st century climate change is the potential for shifts to the intensity and frequency of the El Niño Southern Oscillation ( ENSO) cycle. Although this phenomenon is known to have dramatic impacts on ecosystems regionally and globally, the biological consequences of climate change-driven shifts in future ENSO events have been unexplored. Here, we investigate the potential impacts that a persistent El Niño, La Niña, or 'Neutral' phase may have on species distributions. Using MaxEnt, we model the distribution of climatically suitable habitat for three northeast Australian butterfly subspecies ( Doleschallia bisaltide australis, Hypolimnas alimena lamina, and Mycalesis terminus terminus) across the three ENSO phases. We find that the spatial extent and quality of habitat are lowest under conditions that would characterize a persistent El Niño (hot/dry). In contrast, suitable habitat is broadest under the warm/wet conditions associated with La Niña. Statistical analyses of the difference between pair-wise combinations of suitability maps using Hellinger distance showed that projections for each subspecies and ENSO phase combination were significantly different from other combinations. The resilience of these, and other, butterfly (sub)species to changes in ENSO will be influenced by fluctuations in the strength of these events, availability of refugia, and life-history characteristics. However, the population dynamics of wet- and dry-season phenotypes of M. t. terminus and physiological limitations to high temperatures suggest that this subspecies, in particular, may have limited resilience should the strength and frequency of El Niño events increase. [ABSTRACT FROM AUTHOR]

Species distribution models (SDMs) frequently project substantial declines in the spatial extent of climatically suitable habitat in response to scenarios of future climate change. Such projections are highly disconcerting. Yet, considerable variation can occur in the direction and magnitude of range changes projected by different SDM methods, even when predictive performance is similar. In this study, we assessed whether particular methods have a tendency to predict substantial loss or gain of suitable habitat. In particular, we asked, “are 14 SDM methods equally likely to predict extreme changes to the future extent of suitable habitat for 220 Australian mammal species?”. We defined five non-mutually exclusive categories of ‘extreme’ change, based on stability or loss of current habitat, or the dislocation of current and future habitat: a) no future habitat (range extinction); b) low stability of current habitat (≤10% remains); c) no gain of habitat in new locations; d) all future habitat is in new locations (i.e. completely displaced from current habitat); and e) substantial increase in size of habitat (future habitat is ≥100% larger than current). We found that some SDM methods were significantly more likely than others to predict extreme changes. In particular, distance-based models were significantly less likely than other methods to predict substantial increases in habitat size; Random Forest models and Surface Range Envelopes were significantly more likely to predict a complete loss of current habitat, and future range extinction. Generalised Additive Models and Generalised Linear Models rarely predicted range extinction; future habitat completely disjunct from current habitat was predicted more frequently than expected by Classification Tree Analysis and less frequently by Maxent. Random Forest generally predicted extreme range changes more frequently than other SDM methods. Our results identify trends among different methods with respect to tendency to predict extreme range changes. These are of significance for climate-impact assessments, with implications for transferability of models to novel environments. Our findings emphasise the need to explore and justify the use of different models and their parameterisations, and to develop approaches to assist with optimisation of models. [ABSTRACT FROM AUTHOR]

Advances in computing power and infrastructure, increases in the number and size of ecological and environmental datasets, and the number and type of data collection methods, are revolutionizing the field of Ecology. To integrate these advances, virtual laboratories offer a unique tool to facilitate, expedite, and accelerate research into the impacts of climate change on biodiversity. We introduce the uniquely cloud-based Biodiversity and Climate Change Virtual Laboratory (BCCVL), which provides access to numerous species distribution modelling tools; a large and growing collection of biological, climate, and other environmental datasets; and a variety of experiment types to conduct research into the impact of climate change on biodiversity. Users can upload and share datasets, potentially increasing collaboration, cross-fertilisation of ideas, and innovation among the user community. Feedback confirms that the BCCVL's goals of lowering the technical requirements for species distribution modelling, and reducing time spent on such research, are being met. [ABSTRACT FROM AUTHOR]

Aim Maintenance of ecosystem function under climate change may depend on variation in the way species within functional guilds and individuals within species respond to changes in temperature. What level of variation among and within species do we need to understand, and is it predictable from distribution and trait data? Location Northern California, USA. Methods We used a mesocosm experiment and a set of feeding trials to test the ability of a guild of marine invertebrate grazers to control benthic algal blooms along a local temperature gradient. We investigate links between the locally disjunct distributions of these species, their temperature tolerance traits and the temperature dependence of their population growth and grazing rates. Results We found that the effect of increased temperature on population growth rates is independent of species interactions, but strongly species specific. This variation among herbivores is only partly predictable from differences in species distributions and traits, but translated into large differences in their ability to control macroalgae. Further, these species effects were amplified at warmer temperatures. Finally, we found evidence for a strong response to temperature in the feeding rates of the most important consumer over the course of the experiment, resulting in almost as much variation in grazing rates within one species as the average difference between species. Main conclusions These results illustrate the importance of understanding the variation in responses to temperature among species within a trophic level, among populations within key species and potentially within populations over time. However, this variation is not easily predicted. Further research is needed to uncover links between individuals' thermal optima and their current and future temperature niche. [ABSTRACT FROM AUTHOR]

Aim Species distribution modelling ( SDM) allows identification of suitable areas where the species might occur. The Grinnellian approach relates species occurrence to abiotic limiting factors, such as topography and climate, measured at present or averaged over time at large spatial scales, whilst Eltonian approaches relate processes as reproductive rates or species abundance to factors as resource availability or functional traits described at a finer spatio-temporal resolution. In this study, we aimed to compare the performance and reliability achieved by Eltonian and Grinnellian models developed at geographical scale when addressing two questions that may affect species distribution: breeding site selection and historical local extinctions. Location Spain. Methods We used a temporal series (1988-2010) of breeding success data of Great Bustards ( Otis tarda) across Spain to create: (1) a yearly series of correlative Eltonian models based on satellite data (1-km pixel Normalized Difference Vegetation Index derived from NOAA- AVHRR) as a proxy for resource availability and (2) a static topo-climatic Grinnellian model. We applied two modelling algorithms (MaxEnt and Bootstrapped Generalized Mixed-Effects Models) calibrated and validated across different distributional ranges to investigate breeding success in abiotically suitable areas and recent extinctions within the accessible range. Results Eltonian models showed higher performance and reliability than Grinnellian models at geographical scale and allowed for comparisons of breeding habitat suitability through the study period. Then, satellite data were a more successful predictor of the species breeding success than topography and climate. Breeding was possible out of the range currently occupied by the species, but habitat suitability was significantly lower in non-occupied areas, on where local extinctions have occurred in recent historical times. Main conclusions This study highlights the advisability of using remote-sensing explanatory variables at high temporal resolution matched with population fitness data in Eltonian approaches to improve the knowledge of the relationship between realized and potential distributions of the species at geographical scale. This is a fundamental question in biogeography and a key issue for designing comprehensive conservation strategies. [ABSTRACT FROM AUTHOR]

Aim Spatially-explicit trends in species richness and endemism on sandy shores are quantified to assess representation of beach ecosystems in existing reserve networks. Also, the relative importance of different drivers of species distributions are compared through species distribution modelling. Location The South African beaches are among the best-studied in the world, providing sufficient biological data for the analyses. There is also a well-established coastal protected-area network that putatively provides moderate protection to nearly all beach habitats. Methods Species distribution maps of beach-dependent vertebrates, macrofauna, microflora, and foredune vegetation were compiled from existing sources or modelled using standard techniques. While some data were available for the latter analyses, additional sampling was required to improve bioregional coverage. Each species' distribution was coded to a detailed map of the South African shoreline. Representation of habitats and species in various configurations of existing reserves was determined. Results Overall, 535 species have been recorded on the South African sandy shores, including 139 common species. Endemism is variable among taxonomic groups. Notably, two-thirds of the macrofauna are regional endemics, half of which are endemic to South Africa. For almost all of the common species, < 20 % of their distributions are protected in land-sea reserves. Protection of beach habitats is similarly poor; conservation targets were not met in all cases, and most habitat types are considered Poorly Protected. Main conclusions Sandy beach communities are considerably more diverse than they are generally acknowledged to be, and comprise a unique suite of biota. Given the species' narrow distributions and high degree of exposure to threats, many probably qualify as threatened/endangered. In spite of this, beaches are poorly represented in coastal reserve networks. To redress this issue, conservation planners need to account for beaches specifically, and to ensure they are represented in contiguous land-sea protected areas. [ABSTRACT FROM AUTHOR]

Species distribution models (SDMs) are common tools for assessing the potential impact of climate change on species ranges. Uncertainty in SDM output occurs due to differences among alternate models, species characteristics and scenarios of future climate. While considerable effort is being devoted to identifying and quantifying the first two sources of variation, a greater understanding of climate scenarios and how they affect SDM output is also needed. Climate models are complex tools: variability occurs among alternate simulations, and no single ‘best’ model exists. The selection of climate scenarios for impacts assessments should not be undertaken arbitrarily - strengths and weakness of different climate models should be considered. In this paper, we provide bioclimatic modellers with an overview of emissions scenarios and climate models, discuss uncertainty surrounding projections of future climate and suggest steps that can be taken to reduce and communicate climate scenario-related uncertainty in assessments of future species responses to climate change. [ABSTRACT FROM AUTHOR]

Bioclimatic models are the primary tools for simulating the impact of climate change on species distributions. Part of the uncertainty in the output of these models results from uncertainty in projections of future climates. To account for this, studies often simulate species responses to climates predicted by more than one climate model and/or emission scenario. One area of uncertainty, however, has remained unexplored: internal climate model variability. By running a single climate model multiple times, but each time perturbing the initial state of the model slightly, different but equally valid realizations of climate will be produced. In this paper, we identify how ongoing improvements in climate models can be used to provide guidance for impacts studies. In doing so we provide the first assessment of the extent to which this internal climate model variability generates uncertainty in projections of future species distributions, compared with variability between climate models. We obtained data on 13 realizations from three climate models (three from CSIRO Mark2 v3.0, four from GISS AOM, and six from MIROC v3.2) for two time periods: current (1985–1995) and future (2025–2035). Initially, we compared the simulated values for each climate variable ( P, Tmax, Tmin, and Tmean) for the current period to observed climate data. This showed that climates simulated by realizations from the same climate model were more similar to each other than to realizations from other models. However, when projected into the future, these realizations followed different trajectories and the values of climate variables differed considerably within and among climate models. These had pronounced effects on the projected distributions of nine Australian butterfly species when modelled using the BIOCLIM component of DIVA-GIS. Our results show that internal climate model variability can lead to substantial differences in the extent to which the future distributions of species are projected to change. These can be greater than differences resulting from between-climate model variability. Further, different conclusions regarding the vulnerability of species to climate change can be reached due to internal model variability. Clearly, several climate models, each represented by multiple realizations, are required if we are to adequately capture the range of uncertainty associated with projecting species distributions in the future. [ABSTRACT FROM AUTHOR]

Discusses a research on the effects of climate change on the distribution of species. Percentage of species that will be committed to extinction by 2050; Discussion of the results of the research.