Beyond simple means: integrating extreme events and biotic interactions in species distribution models: conservation implications for the northern bettong (Bettongia tropica) under climate change
Bateman, Brooke Lee (2010) Beyond simple means: integrating extreme events and biotic interactions in species distribution models: conservation implications for the northern bettong (Bettongia tropica) under climate change. PhD thesis, James Cook University.
|PDF (Thesis front) - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader|
|PDF (Thesis whole) - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader|
|Video (QuickTime) (Animated Figure 5.1)|
|Video (QuickTime) (Animated Figure 5.2)|
In order to adopt pertinent management strategies for a species, it is imperative to have an understanding of its distribution and requirements. Species distribution models (SDMs) are broadly applied in ecological studies to generate hypotheses on both current and future distributions of a species. These models utilise statistical approaches to link where a species occurs with environmental data from those locations to infer hypotheses about factors limiting the species' distribution. SDMs have many applications in conservation biology, including being one of the few tools capable of predicting the impacts of climate change on a species. However, applications of SDMs are often limited to using long-term climate means and some measure of variability to represent 'environment'. Although climate is an important factor determining a species distribution, it is not the sole driver. These models exclude important influences such as biotic interactions, physiological limitations, and extreme weather events. Models based only on long-term climate overlook these factors. As these models are used for assessing conservation goals, it is critical to assess their limitations and usefulness.
I address the limitations of current SDM applications in my thesis, with the goal of improving their theoretical underpinning. I used the endangered northern bettong (Bettongia tropica), a tropical rat-kangaroo, as a study species for my research. The northern bettong is an ideal SDM candidate: it is a small, narrowly endemic species, restricted in habitat and diet. The ecology of the species is well understood, with knowledge on key process, interactions, and dietary requirements. I examined the links between climate, limiting resources, biotic interactions (competition with the more generalist rufous bettong (Aepyprymnus rufescens)) and extreme weather events to enhance the ecological theory of SDMs. Additionally, I developed suggestions for the proactive management of the northern bettong. In order to do this, this thesis had several components: 1) examination of the distribution and limits of northern bettong key resources for inclusion into SDMs; 2) assessment of the role of biotic interactions in SDMs; and 3) investigation of the impact of extreme weather events on current distributions.
Two crucial food resources for the northern bettong are 'truffles' and cockatoo grass (Alloteropsis semialata); I assessed site- and regional-scale effects of short-term weather, long-term climate and habitat on the distribution of these resources. Habitat type did have an influence on truffles, as did key soil nutrients, although these factors could only explain a small percentage of the variation in truffle availability. The availability of truffles was directly influenced by weather and climate, with temperature and precipitation driving productivity at both the site and regional scale. The long-term reliability of truffles as a resource may be linked to weather and could be detrimentally affected by increasing seasonality and dry season severity, particularly within the range edges of northern bettong distribution. Key 'extreme' weather events were identified to limit truffle abundance, which in turn would limit the distribution of the northern bettong; thus this resource provided a good modelling candidate for use in biotic interaction assessment. Cockatoo grass has a broad tolerance to temperature and precipitation values although appears to be limited by drought conditions in the dry season. Habitat features have a strong role in determining cockatoo grass density, with a positive response to a late dry season burn indicating this species may benefit from fire. Cockatoo grass distribution was also affected by climate, making it an appropriate variable for inclusion into biotic interaction models, although more research on the affect of fire and climate change on its distribution is warranted.
In order to assess the influence of biotic interactions on SDM predictions under climate change, the spatial distribution of the northern bettong was modelled with and without biotic interactions (two resources and the potential competitor) and their predictions compared under varying degrees of global warming. Climate-only models increasingly diverged from those including biotic interactions with increasing global warming. I showed that SDM exercises that explicitly include known biological interactions provide better, ecologically realistic predictions under climate change. As interactions are currently not included in the vast majority of SDMs, this has ramifications for the usefulness of current climate change impact assessments that employ SDM.
Long-term climate data masks short-term weather events; these weather events may be 'extreme' relative to a species and as such, have huge implications on local population densities. To explore this, I defined extreme weather events in terms of the ecology of the northern bettong. These extreme weather events (e.g. droughts and heat waves) were used to model the temporal variability in the short-term suitability of habitat for both the northern bettong and its potential competitor, the rufous bettong. Severe drought and temperature variability limited local population densities of the northern bettong at the edge of this species' range, and induced contractions in its distribution and niche tracking. Such contractions coincided with beneficial outcomes for the rufous bettong. Populations close to the edge of the range of this species occur in low densities as a result of frequent changes in the suitability of weather and increased pressure from their competitor. Traditional SDMs utilise data limited to spatial scale and do not detect dynamic processes such as temporal shifts in suitable weather and competitive outcomes between species. Failure to include extreme events can lead to overestimation of suitable habitat, which has implications for use in management decisions.
I integrated all of the results from my data chapters to improve our ecological understanding of the northern bettong. Northern bettongs may be vulnerable to climate change, particularly within populations at the edge of its range. Proactive conservation planning to mitigate the impacts of climate change can begin with the knowledge of predicted distributions, identified refugial areas (areas likely to maintain resources under climate change), and the impacts of extreme weather events, variable weather, and competitive pressure from the rufous bettong.
I demonstrate that although the use of SDM in climate change impact assessments is beneficial as a first pass for conservation and adaptation efforts, they can be improved with species-specific, ecologically relevant knowledge. The importance of my study was to highlight how climate-only models are limited in detecting important influences on a species distribution in time, as well as space. Improving on models by addressing these limitations provides for more realistic model outputs that can be utilized with greater confidence in proactive conservation efforts. The models developed here will be used in management decisions for the endangered northern bettong, to help ensure its continued persistence in a changing climate.
|Item Type:||Thesis (PhD)|
Animated figures listed in Appendix D of this thesis are available from the Upload field. The animated figures are:
Animated Figure 5.1. Generalised Linear Modelling top variable weather models in geographical space from 1980-2006 for northern bettongs core (Lamb Range; 'stable bettong weather') versus northern bettongs southern range edge (Coane Range); The core, Lamb Range, represents 'stable northern bettong weather' represented by red here and the southern range edge, the Coane Range weather, is represented by green.
Animated Figure 5.2. Generalised Linear Modelling top variable weather models in geographical space from 1980-2006 for northern bettongs versus rufous bettongs Northern bettong 'weather' is represented by green, rufous bettong 'weather' represented by red, and the niche overlap zone between these species is identified by yellow-tan.
Publications arising from this thesis are available from the Related URLs field. The publications are:
Appendix E: Bateman, B.L., Kutt, A.S., Vanderduys, E.P., and Kemp, J.E. (2010) Small-mammal species richness and abundance along a tropical altitudinal gradient: an Australian example. Journal of Tropical Ecology 26: 139-149.
|Keywords:||species distribution model, climate change, biotic interactions, extreme weather events, endangered species, Bettongia tropica (Potoroidae), northern bettong, Queensland, Australia, population dynamics, population modelling, population modeling, conservation planning and management, density-dependent and density-independent regulation, abiotic interactions|
|FoR Codes:||06 BIOLOGICAL SCIENCES > 0608 Zoology > 060899 Zoology not elsewhere classified @ 33%|
08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080110 Simulation and Modelling @ 34%
05 ENVIRONMENTAL SCIENCES > 0501 Ecological Applications > 050101 Ecological Impacts of Climate Change @ 33%
|SEO Codes:||97 EXPANDING KNOWLEDGE > 970106 Expanding Knowledge in the Biological Sciences @ 33%|
96 ENVIRONMENT > 9603 Climate and Climate Change > 960399 Climate and Climate Change not elsewhere classified @ 33%
96 ENVIRONMENT > 9608 Flora, Fauna and Biodiversity > 960805 Flora, Fauna and Biodiversity at Regional or Larger Scales @ 34%
|Deposited On:||29 Nov 2011 09:06|
|Last Modified:||05 Dec 2011 10:07|
Last 12 Months: 199
Repository Staff Only: item control page