The adoption of cost efficient approaches to species-based management is essential to
ebb the tide of extinctions. Species-based conservation is popular and has proven to
be effective for both the conservation of single species and ecosystems as a whole.
However, recently, the full extent of the biodiversity crisis and the financial
requirements to manage each species has become evident. This has led to the
realisation that the financial and human resources available for conservation are
inadequate for the task of protecting all species. Only a small fraction of the species
that are officially recognised as threatened with extinction are managed for recovery.
Many of these species require substantially greater attention to improve their chances
of persisting. The shortfall in resources available to conservation of biodiversity has
driven the pursuit for cost efficient species management and monitoring approaches.
Considering management benefits and budgetary constraints is essential at every stage
of species management. Specifically, cost efficient methods should be adopted while
collecting and synthesising data (i.e. monitoring and evaluation), effectively utilising
the data to design effective management, selecting management actions (i.e. priority
setting), and implementing management interventions. In this thesis, I develop and
test cost efficient methods for species-based management. Specifically, I focus on
methods for monitoring and priority setting.
In the first section of this thesis, I focus on optimal monitoring under financial
constraints. In chapter 2, I present an overview of the emerging literature on applying
decision theory and optimisation methods to conservation monitoring problems. The
review proceeds in two sections: the first covering analyses that ignore financial
constraints or assume the budget to be fixed; and the second dealing with methods
that address the problem of deciding the optimal balance between monitoring and
management resulting in a flexible monitoring budget. In chapter 3, I demonstrate
that the best monitoring method will depend on the budget available for monitoring
and the species ecology (i.e. probability of detection). Specifically, I compare
presence-absence survey methods with abundance survey methods with respect to
their ability to detect declines in a species. I discover that presence-absence methodsare preferable to abundance surveys when budgets and the probability of detecting the
species are small. I uncover a rule of thumb that describes the conditions under which
each method is optimal: the presence-absence survey is optimal when the expected
number of abundance surveys in which the species is detected is less than 16 per year.
In chapter 4, I show that the ability to detect a decline using grid-based methods under
financial constraints is sensitive to the spatial scale of measurement, the species
probability of detection of the species and the spatial pattern of decline (i.e. spatially
correlated or uncorrelated). I provide guidelines for designing grid surveys to
measure changes in area of occupancy.
In the middle section of the thesis, I focus on increasing the effectiveness of
monitoring data by employing appropriate and effective data analysis methods. In
chapter 4, I investigate the effects of employing a method for correcting the error in
estimating area of occupancy caused through inappropriate scale of grid-based
methods (i.e. scale correction). I demonstrate the scale correction may be unnecessary
and possibly deleterious in some situations. In chapter 5, I develop a novel technique
for predicting population parameters (i.e. abundance, occupancy and probability of
detection). I compare models using Akaike Information Criterion (AIC) and assess
the ecological realism by comparing the parameter estimates with expected values
derived from literature, ecological theory and expert opinion. I demonstrate that
despite being frequently ranked the ‘best model’ according to AIC, some models
produce ecologically unrealistic parameter estimates. My results emphasize the need
to use ecological reasoning when choosing appropriate models.
In the final section of this thesis, I focus on optimising the allocation of resources
among threatened species. In chapter 6, I examine the current resource allocation to
threatened species by New Zealand’s Department of Conservation. I demonstrate that
an informal and unsystematic approach to selecting projects to manage threatened
species may result in the misallocation of limited resources and, potentially,
unnecessary declines and extinctions. In chapter 7, I propose a new framework for
setting cost efficient priorities under financial constraints for New Zealand’s
threatened species. This framework simultaneously considers the management costs,
benefits (including species values) and the likelihood of success. I compare this
method with setting priorities using species threat status and species value alone. Idemonstrate that efficiency in spending is substantially improved through
incorporating management costs, benefits and likelihood of management success, and,
consequently, the number of species managed and expected overall benefit to
threatened species is increased remarkably.
In the final chapter, I summarise the management recommendations for cost efficient
management of species developed throughout the thesis and I describe some potential
future research directions. This thesis represents a significant contribution to the
literature by providing protocols, guidelines and new ways of thinking about cost
efficient species management.