Detecting state changes for ecosystem conservation with long-term monitoring of species composition.

Effective conservation requires an understanding not only of contemporary vegetation distributions in the landscape, but also cognizance of vegetation transitions over time with the goal of maintaining persistence of all states within the landscape. Using a state and transition model framework, we investigated temporal transitions over 31 years in species composition among five upland swamp vegetation communities in southeastern Australia. We applied fuzzy clustering to document transitions across communities; evaluated the resilience and resistance of communities to change; and explored the relationship between ecosystem states and major environmental factors posited to structure the system. We also evaluated the predictive ability of an established vegetation dynamics model. We found that community composition remained stable or underwent reversible or directional transitions depending on the vegetation type. Wetter communities (Ti-tree thicket and Cyperoid heath) were more stable (i.e., resistant) while drier communities showed a greater propensity to transition (i.e., had lower resistance) under the observed disturbance regime (low variance fire intervals). The resilience of drier communities differed under this regime, with Banksia thicket showing reversible compositional change, while Restioid heath and Sedgeland showed directional change. In accord with an established conceptual model, we found that communities were distributed along a hydrological gradient. In addition, vegetation structure, along with light penetration to ground level, differentiated communities. However, internal dynamics of drier communities were complex: differences in fire regime (penultimate fire interval in 2014 and number of fires since 1965) were unable to predict differences in community membership among sites. Aspects of the fire regime are expected to be more important predictors if fire intervals vary more strongly among sites in the future. Fuzzy clustering of compositional data allows managers to track community transitions over time and facilitates planned interventions for conservation purposes.

[1]  J. Sah,et al.  Multi-Scaled Grassland-Woody Plant Dynamics in the Heterogeneous Marl Prairies of the Southern Everglades , 2010, Ecosystems.

[2]  M. Krogh.  Management of longwall coal mining impacts in Sydney's southern drinking water catchments , 2007 .

[3]  David B. Lindenmayer,et al.  The science and application of ecological monitoring , 2010 .

[4]  Rob H. Marrs,et al.  The resistance of a chalk grassland to disturbance , 2003 .

[5]  S. Pimm.  The complexity and stability of ecosystems , 1984, Nature.

[6]  J. Reynolds,et al.  Impact of drought on desert shrubs : Effects of seasonality and degree of resource island development , 1999 .

[7]  Miguel Equihua,et al.  Fuzzy clustering of ecological data. , 1990 .

[8]  Brandon T Bestelmeyer,et al.  Land Management in the American Southwest: A State-and-Transition Approach to Ecosystem Complexity , 2004, Environmental management.

[9]  Mark A. Burgman,et al.  Scientific Foundations for an IUCN Red List of Ecosystems , 2013, PloS one.

[10]  M. McClaran,et al.  Empirical assessment of state-and-transition models with a long-term vegetation record from the Sonoran Desert. , 2012, Ecological applications : a publication of the Ecological Society of America.

[11]  R. Bradstock,et al.  Fire and competition in Australian heath: a conceptual model and field investigations , 1994 .

[12]  J. A. Schaefer,et al.  The fuzzy structure of populations , 2002 .

[13]  Julian D Olden,et al.  Rediscovering the species in community-wide predictive modeling. , 2006, Ecological applications : a publication of the Ecological Society of America.

[14]  Claudia Bolognesi,et al.  Urinary Benzene Biomarkers and DNA Methylation in Bulgarian Petrochemical Workers: Study Findings and Comparison of Linear and Beta Regression Models , 2012, PloS one.

[15]  D. Driscoll,et al.  Detectability of the global weed Hypochaeris radicata is influenced by species, environment and observer characteristics , 2015 .

[16]  M. Westoby,et al.  Opportunistic management for rangelands not at equilibrium. , 1989 .

[17]  M. Jordán,et al.  Conceptual ecological models for the Long Island pitch pine barrens: implications for managing rare plant communities , 2003 .

[18]  Graeme Caughley,et al.  Directions in conservation biology , 1994 .

[19]  Helmut Hillebrand,et al.  Consumer versus resource control of producer diversity depends on ecosystem type and producer community structure , 2007, Proceedings of the National Academy of Sciences.

[20]  D. Keith,et al.  Floristics and soil relations of upland swamp vegetation near Sydney , 1993 .

[21]  Bill Shipley,et al.  The individualistic and community-unit concepts as falsifiable hypotheses , 1987 .

[22]  William C. Krueger,et al.  State and transition modeling: An ecological process approach , 2003 .

[23]  P. Legendre,et al.  Ecologically meaningful transformations for ordination of species data , 2001, Oecologia.

[24]  D. Briske,et al.  State-and-Transition Models, Thresholds, and Rangeland Health: A Synthesis of Ecological Concepts and Perspectives , 2005 .

[25]  S. Wipf,et al.  Observation bias and its causes in botanical surveys on high‐alpine summits , 2015 .

[26]  Mark A. Burgman,et al.  The IUCN Red List of Ecosystems: Motivations, Challenges, and Applications , 2015 .

[27]  D. Keith,et al.  Decadal change in wetland–woodland boundaries during the late 20th century reflects climatic trends , 2009 .

[28]  A. York,et al.  Recognising fuzzy vegetation pattern: the spatial prediction of floristically defined fuzzy communities using species distribution modelling methods , 2014 .

[29]  Rajesh N. Davé,et al.  Characterization and detection of noise in clustering , 1991, Pattern Recognit. Lett..

[30]  Carrie V. Kappel,et al.  Thresholds in Caribbean coral reefs: implications for ecosystem‐based fishery management , 2015 .

[31]  D. Keith,et al.  Plant functional types can predict decade‐scale changes in fire‐prone vegetation , 2007 .

[32]  C. S. Holling,et al.  Resilience, Adaptability and Transformability in Social–ecological Systems , 2004 .

[33]  Helen M. Regan,et al.  A TAXONOMY AND TREATMENT OF UNCERTAINTY FOR ECOLOGY AND CONSERVATION BIOLOGY , 2002 .

[34]  Homer Sanchez,et al.  Practical Guidance for Developing State-and-Transition Models , 2010 .

[35]  Xavier Font,et al.  The management of vegetation classifications with fuzzy clustering , 2010 .

[36]  Jane Elith,et al.  Predicting distribution changes of a mire ecosystem under future climates , 2014 .

[37]  S. Carpenter,et al.  Catastrophic shifts in ecosystems , 2001, Nature.

[38]  Simon A. Levin,et al.  A Spatial Patch Dynamic Modeling Approach to Pattern and Process in an Annual Grassland , 1994 .

[39]  D. Twidwell,et al.  Refining thresholds in coupled fire-vegetation models to improve management of encroaching woody plants in grasslands , 2013 .

[40]  Larry D. Harris,et al.  Nodes, networks, and MUMs: Preserving diversity at all scales , 1986 .

[41]  M. Scheffer,et al.  Alternative equilibria in shallow lakes. , 1993, Trends in ecology & evolution.

[42]  R. Bradstock,et al.  Simulation of the Effect of Spatial and Temporal Variation in Fire Regimes on the Population Viability of a Banksia Species , 1996 .

[43]  A. Zeileis,et al.  Beta Regression in R , 2010 .