Trait-based Australian mammal distribution patterns and extinction risks

Trait-based Australian mammal distribution patterns and extinction risks Jessica Berryman Salt Lake Community College Society of Conservation GIS Conference Pacific Grove, CA July 27, 2015

Extinction in Australian mammals Nineteen mammal species have become extinct in the past 200 years (IUCN Red List) Many more species listed as endangered or threatened Body size thought to be important in determining extinction risk Intermediate critical weight range between 35-5500g originally thought to be hardest hit (Burbidge and McKenzie 1989) Extinction risk was skewed with small mammals persisting better than other size classes (Cardillo and Brombam 2001) Difference between the body size associated with increased extinction risk in northern and southern Australian mammal species based on different introduced predators (foxes in the south and feral cats in the north) (Fisher et. al 2013)

What about other traits? Body size should not be the only trait associated with increased extinction risk. Dynamic environmental factors should not affect species with different intrinsic characteristics such as diet type or reproductive strategy in the same way Habitat template theory suggests that the habitat serves as a filter for particular life history characteristics in species (Southwood 1977, 1988) The ecosystem acts as a filter based on the temporal and spatial heterogeneity in the landscape. When the environmental factors of an area change, some species will be filtered out while other species will be able to persist in an area they were not previously suited to.

Environment = Sieve

Can you use look at life history strategies and see which types of species are more likely to be filtered out and therefore have a higher extinction risk?

Research Aims 1) Identify life history strategies in Australian mammals. I expected that a relatively few number of life history strategies would be found in Australian mammals as the complex of some life history traits would likely be more advantageous than others. 2) Determine if there were differences in extinction risk amongst these life history strategies. I predicted that differences in extinction risk would be found between life history strategies based on the inherent differential impact of changing climate and fire regimes on species with different characteristics such as body size or diet type. 3) Identify patterns in environmental variables associated with life history strategies at higher extinction risk. I predicted that life history strategies with higher extinction risk will be susceptible to being filtered out in areas with extreme climatic conditions and fire frequencies..

Life History Strategies Six behavioral traits of 236 species were compiled. Life history characteristic data was compiled from Mammals of Australia, Animal Diversity Web, IUCN red list and relevant primary literature. 1) Diet type (nominal) herbivore, carnivore, and omnivore. 2) Locomotion type (nominal) - ground, partially arboreal, strictly arboreal, and aerial 3) Median adult body size (numerical) - median value of the overall weight range for all sexes and subspecies 4) Activity pattern type (nominal) - strictly nocturnal, mostly nocturnal, strictly diurnal, mostly diurnal, crepuscular, or arrhythmic 5) Shelter type (nominal) - underground burrows or rock shelters, living vegetation, and dead vegetation such as hollow logs or leaf litter 6) Maximum number of offspring per reproductive bout (numerical) known for only a subset of all species

Seven clusters identified Table 1: Australian mammal life history clusters based on a two-step cluster analysis of body size, diet type and locomotion type (SPSS V21). Number of species in cluster Median Adult Body Size Diet type Locomotion type Maximum litter Activity pattern Shelter type Medium Herbivore Aerial (MHA) Small Carnivore Aerial (SCA) Medium Herbivore Partially Arboreal (MHPA) Medium Omnivore Partially Arboreal (MOPA) Large Herbivore Ground (LHG) Medium Omnivore Ground (MOG) Medium Carnivore Ground (MCG) 37 28 20 26 41 48 36 486.05g 23.37g 2697.82g 791.23g 10000.49g 429.03g 270.66g Herbivore (100%) Carnivore (100%) Herbivore (70%) Omnivore (100%) Herbivore (100%) Omnivore (100%) Carnivore (100%) Aerial Aerial Partly Partly Ground Ground Ground (70.3%) (100%) Arboreal Arboreal (100%) (100%) (100%) (100%) (73.1%) 1.41 1.07 3.5 3.96 1.93 5.04 6.81 Nocturnal (91.9%) Vegetation (51.4%) Nocturnal (96.4%) Soil (42.9%) Nocturnal (55.0%) Vegetation (70.0%) Nocturnal (73.1%) Vegetation (50.0%) Nocturnal (65.9%) Soil (43.9%) Nocturnal (93.8%) Soil (58.3%) Nocturnal (61.1%) Soil (33.3%)

Figure 1: Number of species that are either extinct, critically endangered, endangered or vulnerable within each cluster based on IUCN red list classification.

Large Herbivores Ground-dweller Northern Hairy-nosed Wombat Lasiorhinus krefftii Previous comparative studies have found that large body size is a predictor for increased extinction risk in mammals Large mammals tend to be disproportionately affected by anthropogenic activities

Large Herbivores Ground-dweller Northern Hairy-nosed Wombat Lasiorhinus krefftii Large sized, herbivore, ground-dwelling (LHG) cluster did NOT have significantly more extinct, critically endangered, or endangered species Artifact of number of species in that category

Medium Omnivores Ground-dweller Brush-tailed Bettong Bettongia ogilbyi MOG is a subset of critical weight range Significantly more extinct species (11 species) and significantly less species of least-concern (29 species) (χ2=50.555, df=30, p=0.011).

Medium Omnivores Ground-dweller Brush-tailed Bettong Bettongia ogilbyi Many large mammals may have been previously driven to extinction MOG may be susceptible to introduced predators, climate change, and fire regime change

Species filtered based on dynamic environmental conditions

Life history strategies will be differentially filtered

Changing environment = Changing filters

Occurrence and Environmental data Species occurrence data available through the Atlas of Living Australia (http://www.ala.org.au) was used throughout this study. Australia wide collection of data sets from museum collections, survey data, and volunteer observations. Positional accuracy is unknown for most records. Data points were limited to those observed between 1950 and 2010. 646,228 observations from 236 species were used. Environmental variables of fire frequency, minimum temperature in the coldest period, maximum temperature in the warmest period, precipitation in the driest period, precipitation in the wettest period Climate data from Bioclim, 5 km 2 resolution Predicted climate extremes would be strongest filters Fire frequency data from Northern Australian Fire Information (NAFI) Fire frequency based on processing of satellite imagery to determine fire scars, resolution of 1km 2. Fire frequency was calculated over a 13 year period from 1997 to 2010

Multinomial Logistic Regression Forward stepwise multinomial regression was run comparing the dependent nominal variable of life history cluster designation to independent numerical environmental variables fire frequency, minimum temperature in the coldest period, maximum temperature in the warmest period, precipitation in the driest period, and precipitation in the wettest period Estimates of the model coefficients were calculated by comparing each life history clusters response to changes in the environmental variable with the reference group s response. MOG was selected as the reference group because it contained the most number of extinct species. Determined the odds of a life history strategy being more prevalent in a given environmental condition as compared to the reference group.

Maximum Temperature Figure 5: Likelihood of finding a species within each cluster compared to the likelihood of finding a species within the reference MOG cluster as the warmest period max increases by one degree. As extreme hot days become more prevalent, the relative abundance of MOG will decrease. The average temperature in Australia has increased 0.9 since 1950 The frequency of extreme daily maximum temperatures above 35 o C has increased and is projected to increase for all Australian regions regardless of the climate scenario used

Minimum Temperature Figure 6: Likelihood of finding a species within each cluster compared to the likelihood of finding a species within the reference MOG cluster as the coldest period minimum decreases by one degree. As extreme cold days become more rare, the relative abundance of MOG will increase. The frequency of extreme cold days (below 15 C) and nights (below 5 C) has decreased since 1950 and is projected to continue to decline throughout Australia

Maximum Precipitation Figure 7: Likelihood of finding a species within each cluster compared to the likelihood of finding a species within the reference MOG cluster as the precipitation in the wettest period increases by one mm. As extreme precipitation events become more common, the relative abundance of MOG will decrease. The frequency of extreme precipitation events has increased throughout Australia since 1950 with increased precipitation intensity (precipitation per rain day) Longer droughts followed by heavier rain, exaggerated in northern monsoon climate

Minimum Precipitation Figure 8: Likelihood of finding a species within each cluster compared to the likelihood of finding a species within the reference MOG cluster as the precipitation in the driest period decreases by one mm. As extreme precipitation events become more common, the relative abundance of MOG will decrease. The frequency of extreme precipitation events has increased throughout Australia since 1950 with an increased number of dry days

Fire regime change Climatic factors Warmer, drier periods have already led to increased fire intensity throughout Australia and this is likely to continue and even to intensify Increased numbers of fires in southeastern Australia are predicted during El Niño events as climate extremes intensify Anthropogenic factors The majority of the fires occur in northern Australia, decreased in frequency and increased in intensity Outside of northern Australia, the environments historically experienced fire every 4 to 5 years in some ecosystems and 50 to 100 years in other ecosystems Ecosystems not able to withstand repeated burning within the same year or consecutive years 30-50% of the fires in Australia from arson, unplanned fires are highly disruptive to flora and fauna

Fire Frequency Figure 9: Likelihood of finding a species within each cluster compared to the likelihood of finding a species within the reference MOG cluster as the fire frequency increases by one more year with fire As fire frequency decreases, the relative abundance of MOG will decrease The frequency of fires has decreased while the intensity has increased

Extinction Risk Conclusions Medium, omnivores ground-dwelling mammals appear to be at greater risk for extinction within Australia. Increased maximum temperature, intensified precipitation patterns, and decreased fire frequency likely to filter out MOG compared to most other life history clusters, decreasing relative abundance. The extinction risk for species within MOG Cluster in northern Australia is likely to increase with changing fire regimes and climate patterns.

Future Work Can life history clusters be further refined? Fire data with more direct correlation Introduced predator impact Regional differences

Questions & Feedback