Predicting the cover and richness of intertidal macroalgae in remote areas: a case study in the Antarctic Peninsula

Antarctica is an iconic region for scientific explorations as it is remote and a critical component of the global climate system. Recent climate change causes a dramatic retreat of ice in Antarctica with associated impacts to its coastal ecosystem. These anthropogenic impacts have a potential to increase habitat availability for Antarctic intertidal assemblages. Assessing the extent and ecological consequences of these changes requires us to develop accurate biotic baselines and quantitative predictive tools. In this study, we demonstrated that satellite‐based remote sensing, when used jointly with in situ ground‐truthing and machine learning algorithms, provides a powerful tool to predict the cover and richness of intertidal macroalgae. The salient finding was that the Sentinel‐based remote sensing described a significant proportion of variability in the cover and richness of Antarctic macroalgae. The highest performing models were for macroalgal richness and the cover of green algae as opposed to the model of brown and red algal cover. When expanding the geographical range of the ground‐truthing, even involving only a few sample points, it becomes possible to potentially map other Antarctic intertidal macroalgal habitats and monitor their dynamics. This is a significant milestone as logistical constraints are an integral part of the Antarctic expeditions. The method has also a potential in other remote coastal areas where extensive in situ mapping is not feasible.     

Seeing the forest beyond the trees

In a recent paper (Mitchard et al. 2014, Global Ecology and Biogeography, 23 , 935–946) a new map of forest biomass based on a geostatistical model of field data for the Amazon (and surrounding forests) was presented and contrasted with two earlier maps based on remote‐sensing data Saatchi et al. (2011; RS1) and Baccini et al. (2012; RS2). Mitchard et al. concluded that both the earlier remote‐sensing based maps were incorrect because they did not conform to Mitchard et al. interpretation of the field‐based results. In making their case, however, they misrepresented the fundamental nature of primary field and remote‐sensing data and committed critical errors in their assumptions about the accuracy of research plots, the interpolation methodology and the statistical analysis. By ignoring the large uncertainty associated with ground estimates of biomass and the significant under‐sampling and spatial bias of research plots, Mitchard et al. reported erroneous trends and artificial patterns of biomass over Amazonia. Because of these misrepresentations and methodological flaws, we find their critique of the satellite‐derived maps to be invalid.     

Spatial analysis of egg distribution and geographic changes in the spawning habitat of the Brazilian sardine Sardinella brasiliensis

This paper establishes the spawning habitat of the Brazilian sardine Sardinella brasiliensis and investigates the spatial variability of egg density and its relation with oceanographic conditions in the shelf of the south‐east Brazil Bight (SBB). The spawning habitats of S. brasiliensis have been defined in terms of spatial models of egg density, temperature–salinity plots, quotient (Q) analysis and remote sensing data. Quotient curves (Q_C) were constructed using the geographic distribution of egg density, temperature and salinity from samples collected during nine survey cruises between 1976 and 1993. The interannual sea surface temperature (SST) variability was determined using principal component analysis on the SST anomalies (SSTA) estimated from remote sensing data over the period between 1985 and 2007. The spatial pattern of egg occurrences in the SBB indicated that the largest concentration occurred between Paranaguá and São Sebastião. Spawning habitat expanded and contracted during the years, fluctuating around Paranaguá. In January 1978 and January 1993, eggs were found nearly everywhere along the inner shelf of the SBB, while in January 1988 and 1991 spawning had contracted to their southernmost position. The SSTA maps for the spawning periods showed that in the case of habitat expansion (1993 only) anomalies over the SBB were zero or slightly negative, whereas for the contraction period anomalies were all positive. Sardinella brasiliensis is capable of exploring suitable spawning sites provided by the entrainment of the colder and less‐saline South Atlantic Central Water onto the shelf by means of both coastal wind‐driven (to the north‐east of the SBB) and meander‐induced (to the south‐west of the SBB) upwelling.     

Evapotranspiration model comparison and an estimate of field scale Miscanthus canopy precipitation interception

The bioenergy crop Miscanthus × giganteus has a high water demand to quickly increase biomass with rapid canopy closure and effective rainfall interception, traits that are likely to impact on hydrology in land use change. Evapotranspiration (ET, the combination of plant and ground surface transpiration and evaporation) forms an important part of the water balance, and few ET models have been tested with Miscanthus. Therefore, this study uses field measurements to determine the most accurate ET model and to establish the interception of precipitation by the canopy (C_i). Daily ET estimates from 2012 to 2016 using the Hargreaves–Samani, Priestley–Taylor, Granger–Gray, and Penman–Monteith (short grass) models were calculated using data from a weather station situated in a 6 ha Miscanthus crop. Results from these models were compared to data from on‐site eddy covariance (EC) instrumentation to determine accuracy and calculate the crop coefficient (K_c) model parameter. C_i was measured from June 2016 to March 2017 using stem‐flow and through‐flow gauges within the crop and rain gauges outside the crop. The closest estimated ET to the EC data was the Penman‐Monteith (short grass) model. The K_c values proposed are 0.63 for the early season (March and April), 0.85 for the main growing season (May to September), 1.57 for the late growing season (October and November), and 1.12 over the winter (December to February). These more accurate K_c values will enable better ET estimates with the use of the Penman‐Monteith (short grass) model improving estimates of potential yields and hydrological impacts of land use change. C_i was 24% and remained high during the autumn and winter thereby sustaining significant levels of canopy evaporation and suggesting benefits for winter flood mitigation.     

Modeling leaf CO2 assimilation and Photosystem II photochemistry from chlorophyll fluorescence and the photochemical reflectance index

We present a simple model to assess the quantum yield of photochemistry (Φ_P) and CO₂ assimilation rate from two parameters that are detectable by remote sensing: chlorophyll (chl) fluorescence and the photochemical reflectance index (PRI). Φ_P is expressed as a simple function of the chl fluorescence yield (Φ_F) and nonphotochemical quenching (NPQ): Φ_P = 1–bΦ_F(1 + NPQ). Because NPQ is known to be related with PRI, Φ_P can be remotely assessed from solar‐induced fluorescence and the PRI. The CO₂ assimilation rate can be assessed from the estimated Φ_P value with either the maximum carboxylation rate (V_cmax), the intercellular CO₂ concentration (C_i), or parameters of the stomatal conductance model. The model was applied to experimental data obtained for Chenopodium album leaves under various environmental conditions and was able to successfully predict Φ_F values and the CO₂ assimilation rate. The present model will improve the accuracy of assessments of gas exchange rates and primary productivity by remote sensing.     

Estimating population abundance in plant species with dormant life-stages: Fire and the endangered plant Grevillea caleyi R. Br.

Summary Assessment of the conservation significance of a species at a particular site involves estimating the population size. Generally this is based on a single survey. However, where plant species vary greatly in abundance in response to disturbance regimes, there will be uncertainty associated with the use of single estimates of abundance. The interpretation of such estimates is dependent on an understanding of the ecology of the species and the disturbance regimes that impact on it. We examined the usefulness of abundance estimates in the endangered shrub Grevillea caleyi (a fire‐sensitive shrub with a persistent soil seed bank) from south‐eastern Australia, where fire is a major landscape disturbance. Comparisons of estimates of abundance before and after fire showed very large changes in the number of plants of G. caleyi above ground. Changes in abundance of over two orders of magnitude were observed. The longer the site was left unburnt, the greater the magnitude of change in abundance after the next fire. Above‐ground plants may be rare or absent at sites unburnt for over 15–20 years, but were abundant after fire, due to re‐establishment from the soil seed bank. Sites burnt by two fires in quick succession showed declines in population abundance, most likely due to the soil seed bank not being replenished between such short interval fires. Assessments of the conservation significance of remnant sites of G. caleyi and similar species based on a single sample of above‐ground plant abundance at one time are considered inappropriate. The amount of available habitat for G. caleyi, either as area of occupancy or preferably extent of available habitat, was a moderate predictor of the likely magnitude of abundance in the species after fire. However, the usefulness of these measures for species whose biology is comparable to Grevillea caleyi, will be limited due to factors relating to the degree of species‐specific habitat requirements, local site fire history and the impact of any one fire on resultant post‐fire germination levels. Any assessment of conservation significance will require the interpretation of available information in relation to the ecology of a species.     

A Model to Estimate Potential Submersed Aquatic Vegetation Habitat Based on Studies in Lake Pontchartrain, Louisiana

The depth distribution of submersed aquatic vegetation (SAV) was studied in Lake Pontchartrain, Louisiana, to develop a model to predict changes in SAV abundance from changes in environmental quality. We conducted annual line‐intercept surveys from 1997 through 2001 and monitored monthly photosynthetically active radiation at four sites with different shoreface slopes. The following relationships between SAV distribution and environmental factors were used as model parameters: (1) water clarity controls SAV colonization depth; (2) fluctuation in annual mean water level and wave mixing determines SAV minimum colonization depth; and (3) site differences in SAV areal coverage under the comparable water quality conditions are due to shoreface slope differences. These parameters expressed as mathematical components of the model are as follows: mean water clarity determines SAV colonization depth (Z_max= 2.3/K_d); mean water level and wave mixing controls SAV minimum depth (Z_min= 0.3 m); and shoreface slope angle (θ) determines the distance from Z_min to Z_max. The equation developed for the potential SAV habitat (PSAV) model is PSAV = (2.3 ? 0.3 ×K_d)/(sinθ×K_d). The model was validated by comparing empirical values from the dataset to values predicted by the model. Although the model was developed to predict the PSAV in Lake Pontchartrain, it can be applied to other coastal habitats if local SAV light requirements are substituted for Lake Pontchartrain values. This model is a useful tool in selecting potential restoration sites and in predicting the extent of SAV habitat gain after restoration.     

Modelling the distribution of badgers Meles meles: comparing predictions from field-based and remotely derived habitat data

• 1 Environmental heterogeneity is important in determining the distribution and abundance of organisms at various spatial scales. The ability to understand and predict distribution patterns is important for solving many management problems in conservation biology and wildlife epidemiology.
• 2 The badger Meles meles is a highly adaptable, medium‐sized carnivore, distributed throughout temperate Eurasia, which shows a wide diversity of social and spatial organization. Within Britain, badgers are not only legally protected, but they also serve as a wildlife host for bovine tuberculosis Mycobacterium bovis. An evaluation of the role of badgers in the dynamics of this infection depends on understanding the responses of badgers to the environment at different spatial scales.
• 3 The use of digital data to provide information on habitats for distribution models is becoming common. Digital data are increasingly accessible and are generally cheaper than field surveys. There has been little research, however, to compare the accuracy of models based on field‐derived and remotely derived data.
• 4 In this paper, we make quantified comparisons between large‐scale presence/absence models for badgers in Britain, based on field‐surveyed habitat data and remotely derived digital data, comprising elevation, geology and soil.
• 5 We developed four models: 1980s badger survey data using field‐based and digital data, and 1990s badger survey data using field‐based and digital data. We divided each of the four datasets into two subsets and used one subset for training (developing) the model and the other for testing it.
• 6 All four training models had classification accuracies in excess of 69%. The models generated from digital data were slightly more accurate than those generated from field‐derived habitat data.
• 7 The high classificatory ability of the digital‐based models suggests that the use of digital data may overcome many of the problems associated with field data in wildlife‐habitat modelling, such as cost and restricted geographical coverage, without any significant impact on model performance for some species. The more widespread use of digital data in wildlife‐habitat models should enhance their accuracy, repeatability and applicability and make them better‐suited as tools to aid policy‐ and decision‐making processes.

     

Inversion of net ecosystem CO2 flux measurements for estimation of canopy PAR absorption

The fractional absorption of photosynthetically active radiation (f_PAR) is frequently a key variable in models describing terrestrial ecosystem–atmosphere interactions, carbon uptake, growth and biogeochemistry. We present a novel approach to the estimation of the fraction of incident photosynthetically active radiation absorbed by the photosynthetic components of a plant canopy (f_Chl). The method uses micrometeorological measurements of CO₂ flux and incident radiation to estimate light response parameters from which canopy structure is deduced. Data from two Ameriflux sites in Oklahoma, a tallgrass prairie site and a wheat site, are used to derive 7‐day moving average estimates of f_Chl during three years (1997–1999). The inverse estimates are compared to long‐term field measurements of PAR absorption. Good correlations are obtained when the field‐measured f_PAR is scaled by an estimate of the green fraction of total leaf area, although the inverse technique tends to be lower in value than the field measurements. The inverse estimates of f_Chl using CO₂ flux measurements are different from measurements of f_PAR that might be made by other, more direct, techniques. However, because the inverse estimates are based on observed canopy CO₂ uptake, they might be considered more biologically relevant than direct measurements that are affected by non‐physiologically active components of the canopy. With the increasing number of eddy covariance sites around the world the technique provides the opportunity to examine seasonal and inter‐annual variation in canopy structure and light harvesting capacity at individual sites. Furthermore, the inverse f_Chl provide a new source of data for development and testing of f_PAR retrieval using remote sensing. New remote sensing algorithms, or adjustments to existing algorithms, might thus become better conditioned to ‘biologically significant’ light absorption than currently possible.     

A Comparison of Remote Sensing and Ground-Based Methods for Monitoring Wetland Restoration Success

Abstract Efficient and accurate vegetation sampling techniques are essential for the assessment of wetland restoration success. Remotely acquired data, used extensively in many locations, have not been widely used to monitor restored wetlands. We compared three different vegetation sampling techniques to determine the accuracy associated with each method when used to determine species composition and cover in restored Pacific coast wetlands dominated by Salicornia virginica (perennial pickleweed). Two ground‐based techniques, using quadrat and line intercept sampling, and a remote sensing technique, using low altitude, high resolution, color and color infrared photographs, were applied to estimate cover in three small restoration sites. The remote technique provided an accurate and efficient means of sampling vegetation cover, but individual species could not be identified, precluding estimates of species density and distribution. Aerial photography was determined to be an effective tool for vegetation monitoring of simple (i.e., single‐species) habitat types or when species identities are not important (e.g., when vegetation is developing on a new restoration site). The efficiency associated with these vegetation sampling techniques was dependent on the scale of the assessment, with aerial photography more efficient than ground‐based sampling methods for assessing large areas. However, the inability of aerial photography to identify individual species, especially mixed‐species stands common in southern California salt marshes, limits its usefulness for monitoring restoration success. A combination of aerial photography and ground‐based methods may be the most effective means of monitoring the success of large wetland restoration projects.     

Development and field validation of a regional,management‐scale habitat model: A koala Phascolarctos cinereus case study

Species distribution models have great potential to efficiently guide management for threatened species, especially for those that are rare or cryptic. We used MaxEnt to develop a regional‐scale model for the koala Phascolarctos cinereus at a resolution (250 m) that could be used to guide management. To ensure the model was fit for purpose, we placed emphasis on validating the model using independently‐collected field data. We reduced substantial spatial clustering of records in coastal urban areas using a 2‐km spatial filter and by modeling separately two subregions separated by the 500‐m elevational contour. A bias file was prepared that accounted for variable survey effort. Frequency of wildfire, soil type, floristics and elevation had the highest relative contribution to the model, while a number of other variables made minor contributions. The model was effective in discriminating different habitat suitability classes when compared with koala records not used in modeling. We validated the MaxEnt model at 65 ground‐truth sites using independent data on koala occupancy (acoustic sampling) and habitat quality (browse tree availability). Koala bellows (n = 276) were analyzed in an occupancy modeling framework, while site habitat quality was indexed based on browse trees. Field validation demonstrated a linear increase in koala occupancy with higher modeled habitat suitability at ground‐truth sites. Similarly, a site habitat quality index at ground‐truth sites was correlated positively with modeled habitat suitability. The MaxEnt model provided a better fit to estimated koala occupancy than the site‐based habitat quality index, probably because many variables were considered simultaneously by the model rather than just browse species. The positive relationship of the model with both site occupancy and habitat quality indicates that the model is fit for application at relevant management scales. Field‐validated models of similar resolution would assist in guiding management of conservation‐dependent species.     

Nest site selection of a primary hole‐nesting passerine reveals means to developing sustainable forestry

Anthropogenic habitat loss and fragmentation affect populations worldwide. For example, many bird populations of boreal forests have declined due to intensive forestry. To target conservation actions for such species, determining the key factors that affect their habitat selection is essential. Remote sensing methods provide highly potential means to measure habitat variables over large areas. We aim at identifying the key‐features of habitats by utilizing remote sensing data. As a case example, we study the nest site selection of a primary hole‐nesting passerine, the willow tit Poecile montanus, in a managed forest landscape. Using presence–absence data, we determine the most important habitat characteristics of the nest sites for three spatial scales by generalized linear mixed effect models. Our results highlight the importance of the availability of nesting sites – standing decaying deciduous trees – in the nest site selection of P. montanus. It seems to prefer moist habitats with high densities of deciduous trees and to avoid open areas, but does not require mature or intact habitats. Most of the nest site selection seems to occur within small scales. In this case, remote sensing data alone was insufficient for producing reliable models, but adding information of an ecologically important feature from direct field surveys greatly improved model performances. For the conservation and maintenance of dead wood dependent species, changes in forestry practices are necessary to keep the key characteristics of the habitat. Most importantly, continuous availability of standing decaying wood should be secured.     

Wide-Area Estimates of Stand Structure and Water Use of Tamarix spp. on the Lower Colorado River: Implications for Restoration and Water Management Projects

Tamarix spp. removal has been proposed to salvage water and allow native vegetation to recolonize western U.S. riparian corridors. We conducted wide‐area studies on the Lower Colorado River to answer some of the scientific questions about Tamarix water use and the consequences of removal, combining ground surveys with remote sensing methods. Tamarix stands had moderate rates of evapotranspiration (ET), based on remote sensing estimates, averaging 1.1 m/yr, similar to rates determined for other locations on the river and other rivers. Leaf area index values were also moderate, and stands were relatively open, with areas of bare soil interspersed within stands. At three Tamarix sites in the Cibola National Wildlife Refuge, groundwater salinity at the site nearest to the river (200 m) was relatively low (circa 2,250 mg/L) and was within 3 m of the surface. However, 750 and 1,500 m from the river, the groundwater salinity was 5,000–10,000 mg/L due to removal of water by the Tamarix stands. Despite the high groundwater salinity, the sites away from the river did not have saline surface soils. Only 1% of the mean annual river flow is lost to Tamarix ET on the Lower Colorado River in the United States, and the opportunities for water salvage through Tamarix removal are constrained by its modest ET rates. A possible alternative to Tamarix removal is to intersperse native plants among the stands to improve the habitat value of the riparian zone.     

遥感GPP模型在中亚干旱区4个典型生态系统的适用性评价

总初级生产力（GPP）是全球生态系统碳循环的重要组成部分,对全球气候变化有重要影响。目前有多种遥感模型可以模拟总初级生产力,比较不同遥感模型在中亚干旱区上的适用性对推进全球干旱区碳收支估算具有重要意义。基于涡度协相关技术观测的四个地面站数据验证MOD17、VODCA2、VPM、TG、SANIRv五种模型的模拟精度。结果表明：（1）基于光能利用率理论的MOD17、VPM模型模拟咸海荒漠植被和阜康荒漠植被GPP的精度最高（R²分别为0.52和0.80）,但在模拟草地、农田生态系统生产力时存在较明显的低估（RE>20%）;基于植被指数的遥感模型TG模型、SANIRv模型模拟巴尔喀什湖草地生态系统和乌兰乌苏农田生态系统GPP的精度最高（R²分别为0.91和0.81）,同时模拟值与实测值的相对误差也较低;基于微波的VODCA2模型模拟各生态系统生产力的效果最差。（2）水分亏缺是限制植被GPP的主要因素,因此是否合理考虑水分胁迫是影响GPP模型在中亚干旱区适用性的重要因素。研究揭示了遥感GPP模型在中亚干旱区的应用潜力,为推进全球植被碳通量的准确估算提供参考。     

Photosynthesis of ground vegetation in different aged pine forests: Effect of environmental factors predicted with a process‐based model

Question: How do stand age and environmental factors affect the species‐specific photosynthesis of ground vegetation? Location: Five different aged pine forests in Southern Finland. Methods: We measured photosynthesis of common species of ground vegetation during the growing season of 2006. Results: The measured vascular species, especially those with annual leaves, had a clear seasonal cycle in their measured photosynthetic activity (P_maxⁱ). A simple model that uses site‐specific temperature history, soil moisture and recent frost as input data was able to predict the changes in photosynthetic activity in dwarf shrubs with perennial leaves. The P_maxⁱ values of mosses did not have a clear seasonal cycle, but low values occurred after rain‐free periods and high values after precipitation. We modified the model for mosses and included temporary rain events. The model was able to predict most of the large changes in P_maxⁱ of mosses resulting from varying weather events but there was still some uncertainty, which was probably due to difficulties in measuring fluxes over a moss population. Conclusions: Temperature history, recent frosts and soil moisture determine the changes in P_maxⁱ of dwarf shrubs with perennial leaves. The P_maxⁱ of mosses depends mostly on recent precipitation.     

Sequence‐dependent folding and unfolding of ligand‐bound purine riboswitches

Riboswitch regulation of gene expression requires ligand‐mediated RNA folding. From the fluorescence lifetime distribution of bound 2‐aminopurine ligand, we resolve three RNA conformers (C_o, C_i, C_c) of the liganded G‐ and A‐sensing riboswitches from Bacillus subtilis. The ligand binding affinities, and sensitivity to Mg²⁺, together with results from mutagenesis, suggest that C_o and C_i are partially unfolded species compromised in key loop‐loop interactions present in the fully folded C_c. These data verify that the ligand‐bound riboswitches may dynamically fold and unfold in solution, and reveal differences in the distribution of folded states between two structurally homologous purine riboswitches: Ligand‐mediated folding of the G‐sensing riboswitch is more effective, less dependent on Mg²⁺, and less debilitated by mutation, than the A‐sensing riboswitch, which remains more unfolded in its liganded state. We propose that these sequence‐dependent RNA dynamics, which adjust the balance of ligand‐mediated folding and unfolding, enable different degrees of kinetic discrimination in ligand binding, and fine‐tuning of gene regulatory mechanisms. © 2009 Wiley Periodicals, Inc. Biopolymers 91: 953–965, 2009. This article was originally published online as an accepted preprint. The “Published Online” date corresponds to the preprint version. You can request a copy of the preprint by emailing the Biopolymers editorial office at biopolymers@wiley.com     

Modelling relationships between birds and vegetation structure using airborne LiDAR data: a review with case studies from agricultural and woodland environments

Airborne LiDAR (Light Detection and Ranging) is a remote sensing technology that offers the ability to collect high horizontal sampling densities of high vertical resolution vegetation height data, over larger spatial extents than could be obtained by field survey. The influence of vegetation structure on the bird is a key mechanism underlying bird–habitat models. However, manual survey of vegetation structure becomes prohibitive in terms of time and cost if sampling needs to be of sufficient density to incorporate fine-grained heterogeneity at a landscape extent. We show that LiDAR data can help bridge the gap between grain and extent in organism–habitat models. Two examples are provided of bird–habitat models that use structural habitat information derived from airborne LiDAR data. First, it is shown that data on crop and field boundary height can be derived from LiDAR data, and so have the potential to predict the distribution of breeding Sky Larks in a farmed landscape. Secondly, LiDAR-retrieved canopy height and structural data are used to predict the breeding success of Great Tits and Blue Tits in broad-leaved woodland. LiDAR thus offers great potential for parameterizing predictive bird–habitat association models. This could be enhanced by the combination of LiDAR data with multispectral remote sensing data, which enables a wider range of habitat information to be derived, including both structural and compositional characteristics.     

A historical meta‐analysis of global terrestrial net primary productivity: are estimates converging?

Net primary productivity (NPP) is one of the most important ecosystem parameters, representing vegetation activity, biogeochemical cycling, and ecosystem services. To assess how well the scientific community understands the biospheric function, a historical meta‐analysis was conducted. By surveying the literature from 1862 to 2011, I extracted 251 estimates of total terrestrial NPP at the present time (NPP_T) and calculated their statistical metrics. For all the data, the mean±standard deviation and median were 56.2±14.3 and 56.4 Pg C yr^–1, respectively. Even for estimates published after 2000, a substantial level of uncertainty (coefficient of variation by ±15%) was inevitable. The estimates were categorized on the basis of methodology (i.e., inventory analysis, empirical model, biogeochemical model, dynamic global vegetation model, and remote sensing) to examine the consistency among the statistical metrics of each category. Chronological analysis revealed that the present NPP_T estimates were directed by extensive field surveys in the 1960s and 1970s (e.g., the International Biological Programme). A wide range of uncertainty remains in modern estimates based on advanced biogeochemical and dynamic vegetation models and remote‐sensing techniques. Several critical factors accounting for the estimation uncertainty are discussed. Ancillary analyses were performed to derive additional ecological and human‐related parameters related to NPP. For example, interannual variability, carbon‐use efficiency (a ratio of NPP to gross photosynthesis), human appropriation, and preindustrial NPP_T were assessed. Finally, I discuss the importance of improving NPP_T estimates in the context of current global change studies and integrated carbon cycle research.     

Mapping of a rare plant species (Boronia deanei) using hyper‐resolution remote sensing and concurrent ground observation

Boronia deanei is one of many declared rare, and potentially threatened, species of the Australian flora that occur within freshwater swamps and riparian margins. However, a lack of knowledge regarding its ecological distribution and habitat requirements may result in vulnerability to current and planned anthropogenic activities. Identification of population boundaries, numbers and locations should be highly beneficial for developing appropriate conservation approaches. This study demonstrates (i) the potential of converging hyper‐resolution remote sensing imagery with targeted ground observations to identify individual population extents and densities and (ii) proposes methods of defining and mapping suitable habitat and potential extents for rare plant species in areas potentially exposed to multiple anthropogenic impacts.