Julian C. Fox

Bias in species range estimates from minimum convex polygons: implications for conservation and options for improved planning

Minimum convex polygons (convex hulls) are an internationally accepted, standard method for estimating species’ ranges, particularly in circumstances in which presence-only data are the only kind of spatially explicit data available. One of their main strengths is their simplicity. They are used to make area statements and to assess trends in occupied habitat, and are an important part of the assessment of the conservation status of species. We show by simulation that these estimates are biased. The bias increases with sample size, and is affected by the underlying shape of the species habitat, the magnitude of errors in locations, and the spatial and temporal distribution of sampling effort. The errors affect both area statements and estimates of trends. Some of these errors may be reduced through the application of αhulls, which are generalizations of convex hulls, but they cannot be eliminated entirely. α-hulls provide an explicit means for excluding discontinuities within a species range. Strengths and weaknesses of alternatives including kernel estimators were examined. Convex hulls exhibit larger bias than α-hulls when used to quantify habitat extent and to detect changes in range, and when subject to differences in the spatial and temporal distribution of sampling effort and spatial accuracy. α-hulls should be preferred for estimating the extent of and trends in species’ ranges.

Stochastic structure and individual-tree growth models

The majority of past and current individual-tree growth modelling methodologies have failed to characterise and incorporate structured stochastic components. Rather, they have relied on deterministic predictions or have added an unstructured random component to predictions. In particular, spatial stochastic structure has been neglected, despite being present in most applications of individual-tree growth models. Spatial stochastic structure (also called spatial dependence or spatial autocorrelation) eventuates when spatial influences such as competition and micro-site effects are not fully captured in models. Temporal stochastic structure (also called temporal dependence or temporal autocorrelation) eventuates when a sequence of measurements is taken on an individual-tree over time, and variables explaining temporal variation in these measurements are not included in the model. Nested stochastic structure eventuates when measurements are combined across sampling units and differences among the sampling units are not fully captured in the model. This review examines spatial, temporal, and nested stochastic structure and instances where each has been characterised in the forest biometry and statistical literature. Methodologies for incorporating stochastic structure in growth model estimation and prediction are described. Benefits from incorporation of stochastic structure include valid statistical inference, improved estimation efficiency, and more realistic and theoretically sound predictions. It is proposed in this review that individual-tree modelling methodologies need to characterise and include structured stochasticity. Possibilities for future research are discussed.

Tree hollow incidence in Victorian state forests

Summary The availability of tree hollows in timber production forests is a contentious issue facing forest and wildlife managers in Australia. To integrate conservation priorities for hollow-dependent fauna in forest stewardship, public land managers need information on the quantity and spatial distribution of hollow-bearing trees. This information has previously been lacking, but an extensive hollows database exists in the Victorian Statewide Forest Resource Inventory (SFRI). We use the SFRI to estimate simple stand-level models for the density of hollow-bearing trees, and the density of hollow size classes. Models were of borderline predictive ability but were statistically significant. This is consistent with previous models of hollow incidence that have found hollow formation to be intrinsically stochastic. We then applied these models in a geographic information system (GIS) to generate spatial predictions of hollow availability in Victorian state forests. The resulting GIS layers are available from the Department of Sustainability and Environment (DSE) and are a valuable resource for forest and wildlife managers, researchers and the interested public. We also created tables describing hollow abundance for different forest types, and important stand-level trends in hollow availability emerged. We found that hollow density in ash forests (Eucalyptus regnans, Eucalyptus delegatensis) was consistently low and strongly influenced by the presence of non-ash species that are more susceptible to hollow formation. Hollows occurred in E. regnans forest at particularly low density, with less than 37% of trees having hollows until diameter exceeded 125 cm. The density of hollows in non-ash forests was comparatively greater, with more than 49% of trees containing hollows when their diameter exceeded 75 cm.

Improving the productivity of mechanised harvesting systems using remote sensing

Summary Mechanised harvesting operations are popular in Australia because of their productivity and efficiency, improved worker safety and reduced cost of operations. Most research has found that the productivity and efficiency of a mechanised harvesting system is affected by a number of factors such as forest stand characteristics, terrain variables, operator skill and machinery limitations. However, current studies did not quantify these factors sufficiently to evaluate the productivity and efficiency effects that can guide allocation of different harvesting equipment. This article reviews the literature on how major forest stand characteristics such as tree size and undergrowth affect the productivity and efficiency of a harvesting machine and/or system in clearfelling operations, and explores the application of remote sensing technology including multi-spectral imagery and LiDAR (light detection and ranging) to identify and quantify these characteristics to allow for better harvest planning and harvest system allocation. It is concluded that by evaluating the interactions between each of these factors and different types of harvesting equipment, an empirical model could be developed to optimise the use of current harvesting systems and assist the selection of more cost-effective harvesting machinery, using remote sensing.

The potential availability of plantation roundwood

Summary The history of plantation establishment in Australia is briefly reviewed, highlighting the rapid increase in establishment of hardwood plantations by the private sector over the past decade and the privatisation of some publicly owned softwood plantations. Data collected regionally for the National Plantation Inventory were used to prepare forecasts of future availability for two species groups (hardwood and softwood) and two product groups (pulpwood and sawlog), both groups being broadly defined. The paper draws on these detailed forecasts. The forecasts represent an amalgam of grower-supplied forecasts of availability and estimates based on average yield tables applied to the annual areas planted for the remaining growers. Two alternative scenarios were used to model future planting. ‘No New Planting’ assumed no additional area was planted after 2001. ‘New Planting’ assumed that additional areas were planted on cleared agricultural land until 2019, but not thereafter. The latter forecasts were based on Bureau of Rural Sciences medium projections of future planting rates. An irregular pattern of availability resulted, due to the fluctuations in past planting rates, and required smoothing to achieve a practicable pattern over time. The forecasts of availability represent approximations of future supply, assuming that all costs and technologies remain unchanged and that all volumes available will be sold at that time. Hardwood pulpwood availability will increase from 2.4 to 15.8 million m3 y−1 by 2015. The ‘New Planting’ scenario will provide even larger impacts after 2015. Australian exports of pulpwood products are likely to increase substantially. Most of the hardwoods are fast-growing eucalypts established for pulpwood production under short rotations (12–15 y). There is increasing interest in their potential to produce sawlogs and other high value products, but considerable uncertainty as to the outcomes and economics. While progressive modest increases in softwood sawlog availability will assist domestic industry to replace imports of sawn timber and ease the demands on native forests, they will not replace all imported and native appearance and specialty timbers, nor will they supplant entirely those native timbers with geographic advantages relative to local and regional markets. Hence exports of softwood sawlogs and/or timber are expected to increase. Softwood pulpwood availability changes little, but domestic processing is likely to utilise some present exports.

Protocols for field sampling of forest carbon pools for Monitoring, Reporting and Verification Of REDD

Monitoring, Reporting and Verification (MRV) of forest carbon (C) stocks, changes in these stocks (ΔC) due to land use and land use change and net greenhouse gas emissions are critical technical challenges of climate change mitigation initiatives aiming to reduce emissions from deforestation and forest degradation in developing countries (REDD) [1,2]. MRV systems for emission reporting for voluntary carbon agreements and REDD will require the careful integration of remotely sensed (RS) data and ground measurements for the retrieval of C and ΔC with sufficient precision and accuracy to be marketable and tradable [3]. MRV systems will also need to be designed specific to scale; from project level monitoring for voluntary carbon agreements to national level REDD reporting. Field sampling will be an expensive component of MRV systems and efficiency and cost effectiveness are paramount. Here we present protocols that can guide the design of field measurement campaigns for estimating C and ΔC for MRV systems in developing tropical countries. We will use examples from Papua New Guinea (PNG).

A remote sensing study in support of the Kokoda Track conservation initiative

The Kokoda Track area of Papua New Guinea is geographically diverse, with terrain varying from coastal areas to steep mountains, and biomes that extend from mangroves, to grasslands and savannas, and primary forests. The area is the subject of conservation measures using remotely sensed data in a collaboration between the Australian and Papua New Guinea governments. We describe this Kokoda Track initiative and the land cover mapping to be used as a basis for land use planning. We discuss hydromorphological analysis and land cover classification extracted from Interferometric Synthetic Aperture Radar (SAR) imagery. Our land cover analysis has employed two classification techniques, both object-based, and has included texture measures calculated from SAR imagery. The output is a high resolution classification product, to be used by local government stakeholders.