Reliability of map accuracy assessments: A reply to Roff et al. (2016)

Roff et al. (Ecological Management and Restoration, 17 , 2016, 000) provide a discussion of the criteria expected for the best approach to validation of mapping programs and uses Hunter (Ecological Management & Restoration 17 , 2016, 40) to highlight issues involved. While we support the general principles outlined, we note that the review does not apply the same standards to Sivertsen et al. (Greater Hunter Native Vegetation Mapping Geodatabase Guide (Version 4.0). Office of Environment and Heritage, Department of the Premier and Cabinet, Sydney, Australia, 2011), the original document critiqued by Hunter (Ecological Management & Restoration 17 , 2016, 40). The Hunter (Ecological Management & Restoration 17 , 2016, 40) validation was based on a larger sample size, greater sampling within mapping units and greater representation of landscapes than Sivertsen et al. (Greater Hunter Native Vegetation Mapping Geodatabase Guide (Version 4.0). Office of Environment and Heritage, Department of the Premier and Cabinet, Sydney, Australia, 2011). Survey and validation sites being placed along public roads and lands are common to both the general Office of Environment and Heritage (OEH) and Hunter (Ecological Management & Restoration 17 , 2016, 40) validation methodologies. Thus, the criticisms of Roff et al. (Ecological Management and Restoration, 17 , 2016, 000) of the Hunter (Ecological Management & Restoration 17 , 2016, 40) approach apply equally, if not more, to Sivertsen et al. (Greater Hunter Native Vegetation Mapping Geodatabase Guide (Version 4.0). Office of Environment and Heritage, Department of the Premier and Cabinet, Sydney, Australia, 2011). We outline in the article how the Roff et al. (Ecological Management and Restoration, 17 , 2016, 000) critique was selective and in some cases incorrect in its analysis of issues presented in Hunter (Ecological Management & Restoration 17 , 2016, 40) and did not apply the same criteria to their own work. We conclude by discussing future directions for validating and mapping vegetation communities.     

Validation of the Greater Hunter Native Vegetation Mapping as it pertains to the Upper Hunter region of New South Wales

Reliable vegetation maps are an important component of any long‐term landscape planning initiatives. A number of approaches are available but one, in particular, pattern recognition (segmentation) combined with modelling from floristic site data, is currently being used to map vegetation across NSW. An independent assessment of this approach based on a review of the Greater Hunter Native Vegetation Mapping (GHM_v4) was undertaken in order to assess its ability to cater for regional, local, strategic and landscape planning. The validation process tested 2151 locations across the Upper Hunter Valley region of New South Wales (NSW), Australia. The results suggest that mapping at the coarsest level of NSW vegetation classification, the Formation, is generally poor, with only Dry Sclerophyll Forest and Woodland modelled with some level of reliability. The modelled mapping of individual plant community types (PCTs) was found to be highly inaccurate with only 17% of validation points attributed as ‘correct’ and a further 13% ‘essentially correct’. Therefore, a majority of PCTs were mapped with an accuracy of less than 30%. The results of this validation suggest that the GHM_v4 is of such a low level of accuracy within the upper Hunter as to be inherently unusable for broad‐scale regional and local landscape planning or environmental assessment, including locating compensatory offsets for the loss of native vegetation due to developments. The GHM_v4 methods of pattern recognition of mainly SPOT5 satellite imagery combined with modelling from plot data have not produced reliable vegetation maps of plant community types. Yet this mapping programme is extending across NSW and could be misused for environmental decisions or as a regulation.     

Host plant richness explains diversity of ectomycorrhizal fungi: Response to the comment of Tedersoo et al. (2014)

Exploring the relationships between the biodiversity of groups of interacting organisms yields insight into ecosystem stability and function (Hooper et al. 2000 ; Wardle 2006 ). We demonstrated positive relationships between host plant richness and ectomycorrhizal (EM) fungal diversity both in a field study in subtropical China (Gutianshan) and in a meta‐analysis of temperate and tropical studies (Gao et al. 2013 ). However, based on re‐evaluation of our data sets, Tedersoo et al. ( 2014 ) argue that the observed positive correlation between EM fungal richness and EM plant richness at Gutianshan and also in our metastudies was based mainly from (i) a sampling design with inconsistent species pool and (ii) poor data compilation for the meta‐analysis. Accordingly, we checked our data sets and repeated the analysis performed by Tedersoo et al. ( 2014 ). In contrast to Tedersoo et al. ( 2014 ), our re‐analysis still confirms a positive effect of plant richness on EM fungal diversity in Gutianshan, temperate and tropical ecosystems, respectively.     

Macrophyte effects on algal turbidity in subtropical versus temperate lakes: a comment on Wang et al. (2014)

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Context is key: A comment on Herczeg et al. 2019

In the last several years, there has been a surge in the number of studies addressing the causes and consequences of among‐individual variation in cognitive ability and behavioural plasticity. Here, we use a recent publication by Herczeg et al. (2019: 32(3), 218–226) to highlight three shortcomings common to this newly emerging field. In their study, Herczeg et al. attempted to link variation in cognitive ability and behavioural plasticity by testing whether selection lines of guppies (Poecilia reticulata) that differ in relative brain size also differ in behavioural plasticity, as might be expected if the costs to plasticity are predominantly derived from the cost of developing large brains. First, residual brain size may not be a suitable proxy for ‘cognitive ability’. Recent work has shown that intraspecific variation in cognitive ability can be better understood by considering variation in the specific brain areas responsible for the relevant behaviours as opposed to whole‐brain mass. Second, the measure of behavioural plasticity, habituation, is unlikely to fulfil the assumptions that plasticity is both adaptive and costly. Finally, we point out several misconceptions regarding animal personality that continue to contribute to the choice of traits that are not well aligned with study objectives. Elucidating the mechanisms underlying among‐individual variation in cognition and behavioural plasticity within populations requires integration between behavioural ecology and comparative cognition, and the study system developed by Herczeg et al. has the potential to provide important mechanistic insights. We hope that by articulating and critically appraising the underlying assumptions that are common in these traditionally separate disciplines, a strong foundation can emerge to allow for more fruitful integration of these fields.     

Marine reserve effects on fishery profits: a comment on White et al. (2008)

A recent study ( White et al. 2008 ) claimed that fishery profits will often be higher with management that employs no‐take marine reserves than conventional fisheries management alone. However, this conclusion was based on the erroneous assumption that all landed fish have equal value regardless of size, and questionable assumptions regarding density‐dependence. Examination of an age‐structured version of the White et al. (2008) model demonstrates that their results are not robust to these assumptions. Models with more realistic assumptions generally do not indicate increased fishery yield or profits from marine reserves except for overfished stocks.     

A vegetation-based method to map climatic variation in the arctic–boreal transition area of Finnmark, north-easternmost Norway

Aim To develop a new method for bioclimate mapping where the vegetation layer is the main source of climate information. Location The study area includes four subareas, all situated on the Varangerhalvøya peninsula in Finnmark, north‐easternmost Norway (70–71° N). The four subareas were chosen to represent most of the climatic, topographic, geomorphologic and botanic diversity along the arctic–boreal gradient in the area. The four meteorological stations in the area show a climatic gradient with mean July temperature ranging from 10.1 to 12.3 °C. Methods The new vegetation‐based method is based on the fact that most plant species and plant communities both in the Arctic and adjacent areas have a distribution pattern limited by temperature to some extent. The vegetation is mapped using Landsat TM data and a contextual correction process in a geographic information system. The mapped vegetation units are defined as temperature indicators based on their total distribution patterns and the temperature indicator value of their high frequency and dominant species. The indicator value and degree of cover of all thermophilous vegetation units, within each 500 × 500 m study unit, are combined in a Vegetation‐based Index of Thermophily, VI_tm. This new vegetation‐based method is based on the same basic idea as a recently published floristic‐based method for calculating a Floristic‐based Index of Thermophily, FI_tm. The VI_tm values are tested by comparison with the FI_tm values, and temperature data collected in the field during two growing seasons, and the differences are interpreted ecologically. Results Twenty‐one of the mapped vegetation units were defined as thermophilous and categorized in five groups of temperature indicators. The VI_tm values showed a strong positive linear relationship with the temperatures measured during the years 2001 and 2002, with r² values of 0.79 and 0.85, respectively. The VI_tm values show a high linear relationship (r² = 0.76) with the 71 study units where the FI_tm values were calculated. As interpreted from the relationship with temperature measurements and FI_tm values, the vegetation‐based method seems to work at a broad range of ecological conditions, with very dry, acidic sites being the most important exception. The VI_tm values are related to growing degree‐days of a normal year, and the four subareas are mapped, showing a diversity of 13 bioclimatic classes. The birch forest line is estimated to occur at about 980 °C‐days. The results show climatic gradients with temperatures increasing from the cold coast towards the interior, from wind‐exposed convex hills towards wind‐protected valleys, and from mountain plateaux towards south‐facing lowlands. The north‐easternmost study site at the coast is positioned within the arctic shrub tundra zone. Main conclusions The vegetation‐based method shows a strong positive correlation both with measured temperatures and the floristic‐based method within a broad range of different ecological conditions. The vegetation‐based method has the potential for bioclimatic mapping of large areas in a cost‐effective way. The floristic‐based method has higher accuracy and is more flexible than the vegetation‐based method, and the two methods seem to complement each other.