Evaluating the costs and benefits of systematic data acquisition for conservation assessments

Effective decision-making in conservation often is constrained by data quality. Uncertainties associated with poor quality or sparse data can lead to the misuse of limited resources and potentially the failure of conservation practice. Data acquisition, which can help improve decision-making, is constrained by limited budgets and time. This is especially concerning for rare species, the most in need of conservation, but the most difficult to accurately represent in conservation plans. Here we test the suitability of three different sampling design strategies (two systematic vs random) designed to improve the quality of information available for conservation planning involving rare species. We modelled the spatial distribution of freshwater fish species in a data rich area in northern Australia using a large dataset (representing the best attainable data or true distribution) and simulate increasing subsets of data acquired through the three alternative sampling designs. We then evaluated omission and commission errors in conservation planning outcomes, efficiency and return on investment of data acquisition for conservation planning outcomes obtained from the different data availability × sampling design strategies. Even though we were able to find new species more effectively through systematic sampling designs, this did not 1) translate into reduced errors in conservation planning outcomes for rare species and 2) meet our goal of enhancing cost-effectiveness of conservation planning. Our results suggest that collecting more biodiversity data, irrespective of the sampling design used, does not necessarily reduce data uncertainty issues and could lead to the misuse of the limited resources and ultimately the failure of conservation practice.

[1]  Lucy Bastin,et al.  Raising the bar for systematic conservation planning. , 2011, Trends in ecology & evolution.

[2]  Hugh P. Possingham,et al.  How can you conserve species that haven't been found? , 2007 .

[3]  S. Ferrier.  Mapping spatial pattern in biodiversity for regional conservation planning: where to from here? , 2002, Systematic biology.

[4]  M. Kennard,et al.  Data Acquisition for Conservation Assessments: Is the Effort Worth It? , 2013, PloS one.

[5]  J. Nichols,et al.  Monitoring for conservation. , 2006, Trends in ecology & evolution.

[6]  Atte Moilanen,et al.  A successful community‐level strategy for conservation prioritization , 2008 .

[7]  Matthew E. Watts,et al.  Marxan and relatives: Software for spatial conservation prioritization , 2009 .

[8]  Kevin J. Gaston,et al.  How large do reserve networks need to be , 2001 .

[9]  Hugh P. Possingham,et al.  Effects of data characteristics on the results of reserve selection algorithms , 1999 .

[10]  Eve McDonald-Madden,et al.  Making robust decisions for conservation with restricted money and knowledge , 2008 .

[11]  Kevin J. Gaston,et al.  Why biodiversity surveys are good value , 1999, Nature.

[12]  R. Norris,et al.  Irreplaceability of river networks: towards catchment-based conservation planning , 2008 .

[13]  Bette A. Loiselle,et al.  Avoiding Pitfalls of Using Species Distribution Models in Conservation Planning , 2003 .

[14]  R. Pressey,et al.  When the suit does not fit biodiversity: Loose surrogates compromise the achievement of conservation goals , 2013 .

[15]  Hugh P Possingham,et al.  Tradeoffs of different types of species occurrence data for use in systematic conservation planning. , 2006, Ecology letters.

[16]  Kevin J. Gaston,et al.  Reserve Selection in Regions with Poor Biological Data , 2003 .

[17]  R. Pressey,et al.  Coarse-filter surrogates do not represent freshwater fish diversity at a regional scale in Queensland, Australia , 2011 .

[18]  A. Townsend Peterson,et al.  Novel methods improve prediction of species' distributions from occurrence data , 2006 .

[19]  John Bell,et al.  A review of methods for the assessment of prediction errors in conservation presence/absence models , 1997, Environmental Conservation.

[20]  Ian Phillip Vaughan,et al.  Improving the Quality of Distribution Models for Conservation by Addressing Shortcomings in the Field Collection of Training Data , 2003 .

[21]  Lucy Bastin,et al.  When do conservation planning methods deliver? Quantifying the consequences of uncertainty , 2009, Ecol. Informatics.

[22]  K. J. Wessels,et al.  An evaluation of the gradsect biological survey method , 1998, Biodiversity & Conservation.

[23]  Virgilio Hermoso,et al.  Uncertainty in coarse conservation assessments hinders the efficient achievement of conservation goals , 2012 .

[24]  Jane Elith,et al.  Predicting species distributions from museum and herbarium records using multiresponse models fitted with multivariate adaptive regression splines , 2007 .

[25]  T. Hastie,et al.  Using multivariate adaptive regression splines to predict the distributions of New Zealand ’ s freshwater diadromous fish , 2005 .

[26]  A. Arthington,et al.  Estimating local stream fish assemblage attributes: Sampling effort and efficiency at two spatial scales , 2006 .

[27]  Hugh P Possingham,et al.  Delaying conservation actions for improved knowledge: how long should we wait? , 2009, Ecology letters.

[28]  Byron K. Williams,et al.  Adaptive resource management and the value of information , 2011 .

[29]  Hugh P. Possingham,et al.  Diminishing return on investment for biodiversity data in conservation planning , 2008 .

[30]  A. Hirzel,et al.  Which is the optimal sampling strategy for habitat suitability modelling , 2002 .

[31]  Krishna Pacifici,et al.  A two‐phase sampling design for increasing detections of rare species in occupancy surveys , 2012 .

[32]  H. Possingham,et al.  Addressing longitudinal connectivity in the systematic conservation planning of fresh waters , 2011 .

[33]  C. Margules,et al.  Biological Models for Monitoring Species Decline: The Construction and Use of Data Bases , 1994 .

[34]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[35]  Jamie B. Kirkpatrick,et al.  An iterative method for establishing priorities for the selection of nature reserves: An example from Tasmania , 1983 .

[36]  Hugh P Possingham,et al.  Accounting for uncertainty in marine reserve design. , 2006, Ecology letters.