Incorporating altered fire frequency scenarios in species distribution models improves climate change predictions for tropical savanna birds

Biodiversity conservation in the face of changing climate requires reliable predictions of species distributions. Distribution models need to include variables that strongly influence species persistence. Species will be affected by climate change directly by altering the amount and location of suitable climatic space, and indirectly by climate driven modification of habitat. While climate is a good predictor of species distributions, biotic and abiotic landscape factors also influence distribution. Very few studies of climate change effects on biodiversity have included key landscape factors in distribution modelling, despite recognition that landscape alteration through processes such as fire and land clearing changes fauna patterning. For birds in Australian tropical savannas, change in fire regimes is a critical conservation issue, linked to species decline. While species may show gradual shifts in distribution due to changes in temperature and rainfall, species are likely to show a more immediate response to changes in fire as a result of climatic changes. This study examines species' responses to changes in fire by projecting species distribution modelling algorithms built using Maxent onto scenarios with increased fire frequency. We accounted for important static landscape elements by including remnant vegetation and soil spatial layers. This study identified that increased fire frequency alters the predictions for birds by changing the amount of suitable habitat. Climate change combined with increased fire frequency will reduce available habitat; more than simply using climate predictions alone. Our results demonstrate the importance of including landscape factors into distribution modelling when generating species predictions. Understanding the impacts of landscape factors on bird distributions, in particular fire, is a critical step in conservation planning and adaptation of land management for combating biodiversity loss due to climate change

Weather, Not Climate, Defines Distributions of Vagile Bird Species

Background\ud \ud Accurate predictions of species distributions are essential for climate change impact assessments. However the standard practice of using long-term climate averages to train species distribution models might mute important temporal patterns of species distribution. The benefit of using temporally explicit weather and distribution data has not been assessed. 1We hypothesized that short-term weather associated with the time a species was recorded should be superior to long-term climate measures for predicting distributions of mobile species.\ud \ud Methodology\ud \ud We tested our hypothesis by generating distribution models for 157 bird species found in Australian tropical savannas (ATS) using modelling algorithm Maxent. The variable weather of the ATS supports a bird assemblage with variable movement patterns and a high incidence of nomadism. We developed “weather” models by relating climatic variables (mean temperature, rainfall, rainfall seasonality and temperature seasonality) from the three month, six month and one year period preceding each bird record over a 58 year period (1950–2008). These weather models were compared against models built using long-term (30 year) averages of the same climatic variables.\ud \ud Conclusions\ud \ud Weather models consistently achieved higher model scores than climate models, particularly for wide-ranging, nomadic and desert species. Climate models predicted larger range areas for species, whereas weather models quantified fluctuations in habitat suitability across months, seasons and years. Models based on long-term climate averages over-estimate availability of suitable habitat and species' climatic tolerances, masking species potential vulnerability to climate change. Our results demonstrate that dynamic approaches to distribution modelling, such as incorporating organism-appropriate temporal scales, improves understanding of species distributions

Establishing effective conservation management strategies for a poorly known endangered species: A case study using Australia’s night parrot (Pezoporus occidentalis)

An evidence-based approach to the conservation management of a species requires knowledge of that species’ status, distribution, ecology, and threats. Coupled with budgets for specific conservation strategies, this knowledge allows prioritisation of funding toward activities that maximise benefit for the species. However, many threatened species are poorly known, and determining which conservation strategies will achieve this is difficult. Such cases require approaches that allow decision-making under uncertainty. Here we used structured expert elicitation to estimate the likely benefit of potential management strategies for the Critically Endangered and, until recently, poorly known Night Parrot (Pezoporus occidentalis). Experts considered cat management the single most effective management strategy for the Night Parrot. However, a combination of protecting and actively managing existing intact Night Parrot habitat through management of grazing, controlling feral cats, and managing fire specifically to maintain Night Parrot habitat was thought to result in the greatest conservation gains. The most cost-effective strategies were thought to be fire management to maintain Night Parrot habitat, and intensive cat management using control methods that exploit local knowledge of cat movements and ecology. Protecting and restoring potentially suitable, but degraded, Night Parrot habitat was considered the least effective and least cost-effective strategy. These expert judgements provide an informed starting point for land managers implementing on-ground programs targeting the Night Parrot, and those developing policy aimed at the species’ longer-term conservation. As a set of hypotheses, they should be implemented, assessed, and improved within an adaptive management framework that also considers the likely co-benefits of these strategies for other species and ecosystems. The broader methodology is applicable to conservation planning for the management and conservation of other poorly known threatened species

Quantifying extinction risk and forecasting the number of impending Australian bird and mammal extinctions

A critical step towards reducing the incidence of extinction is to identify and rank the species at highest risk, while implementing protective measures to reduce the risk of extinction to such species. Existing global processes provide a graded categorisation of extinction risk. Here we seek to extend and complement those processes to focus more narrowly on the likelihood of extinction of the most imperilled Australian birds and mammals. We considered an extension of existing IUCN and NatureServe criteria, and used expert elicitation to rank the extinction risk to the most imperilled species, assuming current management. On the basis of these assessments, and using two additional approaches, we estimated the number of extinctions likely to occur in the next 20 years. The estimates of extinction risk derived from our tighter IUCN categorisations, NatureServe assessments and expert elicitation were poorly correlated, with little agreement among methods for which species were most in danger – highlighting the importance of integrating multiple approaches when considering extinction risk. Mapped distributions of the 20 most imperilled birds reveal that most are endemic to islands or occur in southern Australia. The 20 most imperilled mammals occur mostly in northern and central Australia. While there were some differences in the forecasted number of extinctions in the next 20 years among methods, all three approaches predict further species loss. Overall, we estimate that another seven Australian mammals and 10 Australian birds will be extinct by 2038 unless management improves

Distribution and habitat of the flute-nosed bat Murina florium (Chiroptera: Vespertilionidae) in the wet tropics of north-eastern Queensland

The flute-nosed bat Murina florium is a poorly known species that was first discovered in Australia at Mt Baldy State Forest on the Atherton Tablelands in north-eastern Queensland in 1981. Subsequently there have been few other documented records despite intensive harp trapping studies, with the species only recorded from an additional six localities up until December 1995. This study provides four new locality records for the species, including two records which extend the known southern range limits of M. florium by 150 km across the Herbert River discontinuity within the Wet Tropics bioregion. The broad habitat characteristics of all known localities for the species are reviewed and the paper presents the first account of this bat occurring in non-rainforest habitat. Occurrence of M. florium in this habitat is discussed using current knowledge of roosting and ecomorphology characteristics. A predicted distribution of M. fforium based on the 11 locality records, is calculated using DOMAIN and 16 biophysical parameters

Predictive Logistic Likelihood of Occurrence of Mammals Based on Climatic Suitability

Australia has one of the highest rates of mammal extinctions in the world. There are various studies to identify the causes of extinction including introduction of predictor species, cattle grazing, European invasion, climate change and land management factors. This data set is a model output for over 100 mammal species in a monthly time-series occurrence based on climatic suitability. The data will be used to develop a tool to visualise mammal occurrence in different time-steps

Review of environmental factors - Flinders Highway (Gardner Creek to Burra Range)

[Extract] It is proposed by the Queensland Department of Main Roads (QDMR) to undertake roadwidening of an approximately 87km stretch of the Flinders Highway from Gardner Creek to 141 km west of Charters Towers as well as full re-construction of a section of the highway near the Burra Range from 131-137km west of Charters Towers. The Australian Centre for Tropical Freshwater Research (ACTFR) was commissioned by Connell Wagner Pty. Ltd. to assess the environmental values of the site with regard to the potential impacts of the proposal on behalf of QDMR. This report forms part of a Review of Environmental Factors (REF) being prepared by Connell Wagner Pty. Ltd. An REF is intended only to provide a preliminary assessment of the possible environmental effects of a project, to enable a decision to be undertaken as to whether more detailed assessment is required

Lizard diversity on a rainforest–savanna altitude gradient in north-eastern Australia

Mountain ecosystems act as natural experiments for investigating the relationship between environmental heterogeneity and species diversity. A review of the global altitudinal distribution of reptiles identified a diverse range of patterns driven by climate and taxonomy. No Australian examples were included in this analysis. We addressed this gap by surveying the reptile assemblage along an altitude gradient from upland rainforest (~1000 m) through to open savanna woodlands (~350 m) in north-eastern Australia. Reptiles were sampled on four separate occasions between May 2006 and November 2007. Thirty-six species, representing seven families, were recorded along the gradient. As we used only diurnal active searching, snakes and nocturnal geckoes were probably under-sampled; thus we considered only lizards in the analysis of altitude pattern. Lizard species richness peaked at the mid-altitudes (600–900 m, 11–12 spp.) and abundance highest at the lower (800 m) zones. This pattern is likely a factor of both the increase in radiant heat sources (reduced canopy cover) and increased species packing due to the diversity of niches available (presence of rock cover and increase in saxicolous species). In the lower-altitude sites the high abundance of few species seems linked to the dominance of disturbance-tolerant species. We conclude that lizard richness and abundance patterns on this transect are not necessarily exhibiting a mid-domain effect, but instead are a function of species-specific ecological and habitat requirements

Burdekin catchment study – a desktop study of environmental issues associated with dam and irrigation area development

The Australian Centre for Tropical Freshwater Research (ACTFR) has been commissioned by the Department of Natural Resources (DNR) to provide an environmental evaluation of prospective dam sites located within the Burdekin Catchment. This exercise is a desk-top study based on existing data sources, with no original information being collected. Prospective dam sites that remain in consideration after this exercise will be subject to more thorough investigations at a later date. The purpose of this exercise is to raise issues that are currently known to exist

Incorporating low-resolution historic species location data decreases performance of distribution models

Developing robust species distribution models is important as model outputs are increasingly being incorporated into conservation policy and management decisions. A largely overlooked component of model assessment and refinement is whether to include historic species occurrence data in distribution models to increase the data sample size. Data of different temporal provenance often differ in spatial accuracy and precision. We test the effect of inclusion of historic coarse-resolution occurrence data on distribution model outputs for 187 species of birds in Australian tropical savannas. Models using only recent (after 1990), fine-resolution data had significantly higher model performance scores measured with area under the receiver operating characteristic curve (AUC) than models incorporating both fine- and coarse-resolution data. The drop in AUC score is positively correlated with the total area predicted to be suitable for the species (R2 = 0.163–0.187, depending on the environmental predictors in the model), as coarser data generally leads to greater predicted areas. The remaining unexplained variation is likely to be due to the covariate errors resulting from resolution mismatch between species records and environmental predictors. We conclude that decisions regarding data use in species distribution models must be conscious of the variation in predictions that mixed-scale datasets might cause