Evaluating the performance of weed containment programmes

Aim: To develop approaches to the evaluation of programmes whose strategic objectives are to halt or slow weed spread. Location: Australia. Methods: Key aspects in the evaluation of weed containment programmes are considered. These include the relevance of models that predict the effects of management intervention on spread, the detection of spread, evidence for containment failure and metrics for absolute or partial containment. Case studies documenting either near-absolute (Orobanche ramosa L., branched broomrape) or partial (Parthenium hysterophorus (L.) King and Robinson, parthenium) containment are presented. Results: While useful for informing containment strategies, predictive models cannot be employed in containment programme evaluation owing to the highly stochastic nature of realized weed spread. The quality of observations is critical to the timely detection of weed spread. Effectiveness of surveillance and monitoring activities will be improved by utilizing information on habitat suitability and identification of sites from which spread could most compromise containment. Proof of containment failure may be difficult to obtain. The default option of assuming that a new detection represents containment failure could lead to an underestimate of containment success, the magnitude of which will depend on how often this assumption is made. Main conclusions: Evaluation of weed containment programmes will be relatively straightforward if containment is either absolute or near-absolute and may be based on total containment area and direct measures of containment failure, for example, levels of dispersal, establishment and reproduction beyond (but proximal to) the containment line. Where containment is only partial, other measures of containment effectiveness will be required. These may include changes in the rates of detection of new infestations following the institution of interventions designed to reduce dispersal, the degree of compliance with such interventions, and the effectiveness of tactics intended to reduce fecundity or other demographic drivers of spread. © 2012 Blackwell Publishing Ltd.

[1]  Susan Hester,et al.  Allocating surveillance effort in the management of invasive species: A spatially-explicit model , 2010, Environ. Model. Softw..

[2]  David Pullar,et al.  Surveillance protocols for management of invasive plants: modelling Chilean needle grass (Nassella neesiana) in Australia , 2009 .

[3]  P. Hulme Beyond control : wider implications for the management of biological invasions , 2006 .

[4]  S. Brooks,et al.  Progress towards the Eradication of Mikania Vine (Mikania micrantha) and Limnocharis (Limnocharis flava) in Northern Australia , 2008, Invasive Plant Science and Management.

[5]  Daniel Simberloff,et al.  Eradication—preventing invasions at the outset , 2003, Weed Science.

[6]  Lisa J. Rew,et al.  The Rationale for Monitoring Invasive Plant Populations as a Crucial Step for Management , 2009, Invasive Plant Science and Management.

[7]  Jorge L. Renteria,et al.  Eradications and People: Lessons from the Plant Eradication Program in Galapagos , 2010 .

[8]  Brett A. Melbourne,et al.  Highly Variable Spread Rates in Replicated Biological Invasions: Fundamental Limits to Predictability , 2009, Science.

[9]  F. Panetta Weed Eradication—An Economic Perspective , 2009, Invasive Plant Science and Management.

[10]  S. Hester,et al.  Deriving Efficient Frontiers for Effort Allocation in the Management of Invasive Species , 2011 .

[11]  Danny A. P. Hooftman,et al.  Modelling spread of British wind‐dispersed plants under future wind speeds in a changing climate , 2012 .

[12]  A. Grice,et al.  Commercially valuable weeds: Can we eat our cake without choking on it? , 2006 .

[13]  Roger Lawes,et al.  Evaluation of the Australian Branched Broomrape (Orobanche ramosa) Eradication Program , 2007, Weed Science.

[14]  Philip E. Hulme,et al.  Spatio-temporal dynamics of plant invasions: Linking pattern to process , 2005 .

[15]  C. Howell Progress toward Environmental Weed Eradication in New Zealand , 2012, Invasive Plant Science and Management.

[16]  M. Crawley,et al.  Management of the Invasive Hill Raspberry (Rubus niveus) on Santiago Island, Galapagos: Eradication or Indefinite Control? , 2012, Invasive Plant Science and Management.

[17]  R. Sheley,et al.  A Conceptual Framework for Preventing the Spatial Dispersal of Invasive Plants , 2007, Weed Science.

[18]  F. Panetta,et al.  Beyond fecundity control: which weeds are most containable? , 2012 .

[19]  David Pimentel,et al.  Biological Invasions : Economic and Environmental Costs of Alien Plant, Animal, and Microbe Species , 2002 .

[20]  R. Hufbauer Population Genetics of Invasions: Can We Link Neutral Markers to Management?1 , 2004 .

[21]  John R. U. Wilson,et al.  Contain or eradicate? Optimizing the management goal for Australian acacia invasions in the face of uncertainty , 2011 .

[22]  Stefano Benvenuti,et al.  Weed seed movement and dispersal strategies in the agricultural environment , 2007 .

[23]  F. Panetta,et al.  The biology of Australian weeds, 16. Chondrilla juncea L. [skeleton weed; gum succory] , 1987 .

[24]  B. D. Hardesty,et al.  Persistence and spread in a new landscape: Dispersal ecology and genetics of Miconia invasions in Australia , 2011 .

[25]  C. M. Harris,et al.  Invasive species control: Incorporating demographic data and seed dispersal into a management model for Rhododendron ponticum , 2009, Ecol. Informatics.

[26]  S. Brooks,et al.  Estimating and influencing the duration of weed eradication programmes , 2011 .

[27]  B. D. Hardesty,et al.  Getting here from there: testing the genetic paradigm underpinning introduction histories and invasion success , 2012 .

[28]  D. Maitre,et al.  An assessment of the effectiveness of a large, national-scale invasive alien plant control strategy in South Africa , 2012 .

[29]  Alexei A. Sharov,et al.  BIOECONOMICS OF MANAGING THE SPREAD OFEXOTIC PEST SPECIES WITH BARRIER ZONES , 1998 .

[30]  Shaun R. Coutts,et al.  Modeling population dynamics, landscape structure, and management decisions for controlling the spread of invasive plants , 2012, Annals of the New York Academy of Sciences.

[31]  Catherine S. Jarnevich,et al.  Temporal Management of Invasive Species , 2009 .

[32]  M. Neubert,et al.  Projecting Rates of Spread for Invasive Species , 2004, Risk analysis : an official publication of the Society for Risk Analysis.

[33]  Oscar J. Cacho,et al.  Bioeconomic modeling for control of weeds in natural environments , 2008 .

[34]  Daniel J. Murphy,et al.  Risk assessment, eradication, and biological control: global efforts to limit Australian acacia invasions , 2011 .

[35]  Justin M. J. Travis,et al.  Improving prediction and management of range expansions by combining analytical and individual‐based modelling approaches , 2011 .

[36]  C. Phillips,et al.  Transmission of weed seed by livestock: a review , 2011 .

[37]  Shaun R. Coutts,et al.  What are the key drivers of spread in invasive plants: dispersal, demography or landscape: and how can we use this knowledge to aid management? , 2011, Biological Invasions.

[38]  D. Simberloff,et al.  BIOTIC INVASIONS: CAUSES, EPIDEMIOLOGY, GLOBAL CONSEQUENCES, AND CONTROL , 2000 .

[39]  Caz M Taylor,et al.  The spatial spread of invasions: new developments in theory and evidence , 2004 .

[40]  R. Lawes,et al.  Evaluation of weed eradication programs: the delimitation of extent , 2005 .

[41]  Heather North,et al.  Slowing down a pine invasion despite uncertainty in demography and dispersal , 2005 .

[42]  F. Dane Panetta,et al.  Evaluation of weed eradication programs: containment and extirpation , 2006 .