Variable sea‐ice conditions influence trophic dynamics in an Arctic community of marine top predators

Sea‐ice coverage is a key abiotic driver of annual environmental conditions in Arctic marine ecosystems and could be a major factor affecting seabird trophic dynamics. Using stable isotope ratios of carbon (δ¹³C) and nitrogen (δ¹⁵N) in eggs of thick‐billed murres (Uria lomvia), northern fulmars (Fulmarus glacialis), glaucous gulls (Larus hyperboreus), and black‐legged kittiwakes (Rissa tridactyla), we investigated the trophic ecology of prebreeding seabirds nesting at Prince Leopold Island, Nunavut, and its relationship with sea‐ice conditions. The seabird community of Prince Leopold Island had a broader isotopic niche during lower sea‐ice conditions, thus having a more divergent diet, while the opposite was observed during years with more extensive sea‐ice conditions. Species' trophic position was influenced by sea ice; in years of lower sea‐ice concentration, gulls and kittiwakes foraged at higher trophic levels while the opposite was observed for murres and fulmars. For murres and fulmars over a longer time series, there was no evidence of the effect of sea‐ice concentration on species' isotopic niche. Results suggest a high degree of adaptation in populations of high Arctic species that cope with harsh and unpredictable conditions. Such different responses of the community isotopic niche also show that the effect of variable sea‐ice conditions, despite being subtle at the species level, might have larger implications when considering the trophic ecology of the larger seabird community. Species‐specific responses in foraging patterns, in particular trophic position in relation to sea ice, are critical to understanding effects of ecosystem change predicted for a changing climate.     

The use of an unsupervised learning approach for characterizing latent behaviors in accelerometer data

The recent increase in data accuracy from high resolution accelerometers offers substantial potential for improved understanding and prediction of animal movements. However, current approaches used for analysing these multivariable datasets typically require existing knowledge of the behaviors of the animals to inform the behavioral classification process. These methods are thus not well‐suited for the many cases where limited knowledge of the different behaviors performed exist. Here, we introduce the use of an unsupervised learning algorithm. To illustrate the method's capability we analyse data collected using a combination of GPS and Accelerometers on two seabird species: razorbills (Alca torda) and common guillemots (Uria aalge). We applied the unsupervised learning algorithm Expectation Maximization to characterize latent behavioral states both above and below water at both individual and group level. The application of this flexible approach yielded significant new insights into the foraging strategies of the two study species, both above and below the surface of the water. In addition to general behavioral modes such as flying, floating, as well as descending and ascending phases within the water column, this approach allowed an exploration of previously unstudied and important behaviors such as searching and prey chasing/capture events. We propose that this unsupervised learning approach provides an ideal tool for the systematic analysis of such complex multivariable movement data that are increasingly being obtained with accelerometer tags across species. In particular, we recommend its application in cases where we have limited current knowledge of the behaviors performed and existing supervised learning approaches may have limited utility.     

Behavioral classification of low‐frequency acceleration and temperature data from a free‐ranging small mammal

The miniaturization and affordability of new technology is driving a biologging revolution in wildlife ecology with use of animal‐borne data logging devices. Among many new biologging technologies, accelerometers are emerging as key tools for continuously recording animal behavior. Yet a critical, but under‐acknowledged consideration in biologging is the trade‐off between sampling rate and sampling duration, created by battery‐ (or memory‐) related sampling constraints. This is especially acute among small animals, causing most researchers to sample at high rates for very limited durations. Here, we show that high accuracy in behavioral classification is achievable when pairing low‐frequency acceleration recordings with temperature. We conducted 84 hr of direct behavioral observations on 67 free‐ranging red squirrels (200–300 g) that were fitted with accelerometers (2 g) recording tri‐axial acceleration and temperature at 1 Hz. We then used a random forest algorithm and a manually created decision tree, with variable sampling window lengths, to associate observed behavior with logger recorded acceleration and temperature. Finally, we assessed the accuracy of these different classifications using an additional 60 hr of behavioral observations, not used in the initial classification. The accuracy of the manually created decision tree classification using observational data varied from 70.6% to 91.6% depending on the complexity of the tree, with increasing accuracy as complexity decreased. Short duration behavior like running had lower accuracy than long‐duration behavior like feeding. The random forest algorithm offered similarly high overall accuracy, but the manual decision tree afforded the flexibility to create a hierarchical tree, and to adjust sampling window length for behavioral states with varying durations. Low frequency biologging of acceleration and temperature allows accurate behavioral classification of small animals over multi‐month sampling durations. Nevertheless, low sampling rates impose several important limitations, especially related to assessing the classification accuracy of short duration behavior.     

Taking a trip to the shelf: Behavioral decisions are mediated by the proximity to foraging habitats in the black‐legged kittiwake

For marine top predators like seabirds, the oceans represent a multitude of habitats regarding oceanographic conditions and food availability. Worldwide, these marine habitats are being altered by changes in climate and increased anthropogenic impact. This is causing a growing concern on how seabird populations might adapt to these changes. Understanding how seabird populations respond to fluctuating environmental conditions and to what extent behavioral flexibility can buffer variations in food availability can help predict how seabirds may cope with changes in the marine environment. Such knowledge is important to implement proper long‐term conservation measures intended to protect marine predators. We explored behavioral flexibility in choice of foraging habitat of chick‐rearing black‐legged kittiwakes Rissa tridactyla during multiple years. By comparing foraging behavior of individuals from two colonies with large differences in oceanographic conditions and distances to predictable feeding areas at the Norwegian shelf break, we investigated how foraging decisions are related to intrinsic and extrinsic factors. We found that proximity to the shelf break determined which factors drove the decision to forage there. At the colony near the shelf break, time of departure from the colony and wind speed were most important in driving the choice of habitat. At the colony farther from the shelf break, the decision to forage there was driven by adult body condition. Birds furthermore adjusted foraging behavior metrics according to time of the day, weather conditions, body condition, and the age of the chicks. The study shows that kittiwakes have high degree of flexibility in their behavioral response to a variable marine environment, which might help them buffer changes in prey distribution around the colonies. The flexibility is, however, dependent on the availability of foraging habitats near the colony.     

An evaluation of behavior inferences from Bayesian state‐space models: A case study with the Pacific walrus

State‐space models offer researchers an objective approach to modeling complex animal location data sets, and state‐space model behavior classifications are often assumed to have a link to animal behavior. In this study, we evaluated the behavioral classification accuracy of a Bayesian state‐space model in Pacific walruses using Argos satellite tags with sensors to detect animal behavior in real time. We fit a two‐state discrete‐time continuous‐space Bayesian state‐space model to data from 306 Pacific walruses tagged in the Chukchi Sea. We matched predicted locations and behaviors from the state‐space model (resident, transient behavior) to true animal behavior (foraging, swimming, hauled out) and evaluated classification accuracy with kappa statistics (κ) and root mean square error (RMSE). In addition, we compared biased random bridge utilization distributions generated with resident behavior locations to true foraging behavior locations to evaluate differences in space use patterns. Results indicated that the two‐state model fairly classified true animal behavior (0.06 ≤ κ ≤ 0.26, 0.49 ≤ RMSE ≤ 0.59). Kernel overlap metrics indicated utilization distributions generated with resident behavior locations were generally smaller than utilization distributions generated with true foraging behavior locations. Consequently, we encourage researchers to carefully examine parameters and priors associated with behaviors in state‐space models, and reconcile these parameters with the study species and its expected behaviors.     

Examination of Different Accelerometer Cut-Points for Assessing Sedentary Behaviors in Children

Background

Public health research on sedentary behavior (SB) in youth has heavily relied on accelerometers. However, it has been limited by the lack of consensus on the most accurate accelerometer cut-points as well as by unknown effects caused by accelerometer position (wrist vs. hip) and output (single axis vs. multiple axes). The present study systematically evaluates classification accuracy of different Actigraph cut-points for classifying SB using hip and wrist-worn monitors and establishes new cut-points to enable use of the 3-dimensional vector magnitude data (for both hip and wrist placement).

Methods

A total of 125 children ages 7–13 yrs performed 12 randomly selected activities (from a set of 24 different activities) for 5 min each while wearing tri-axial Actigraph accelerometers on both the hip and wrist. The accelerometer data were categorized as either sedentary or non-sedentary minutes using six previously studied cut-points: 100counts-per-minute (CPM), 200CPM, 300CPM, 500CPM, 800CPM and 1100CPM. Classification accuracy was evaluated with Cohen''s Kappa (κ) and new cut-points were identified from Receiver Operating Characteristic (ROC).

Results

Of the six cut-points, the 100CPM value yielded the highest classification accuracy (κ = 0.81) for hip placement. For wrist placement, all of the cut-points produced low classification accuracy (ranges of κ from 0.44 to 0.67). Optimal sedentary cut-points derived from ROC were 554.3CPM (ROC-AUC of 0.99) for vector magnitude for hip, 1756CPM (ROC-AUC of 0.94) for vertical axis for wrist, and 3958.3CPM (ROC-AUC of 0.93) for vector magnitude for wrist placement.

Conclusions

The 100CPM was supported for use with vertical axis for hip placement, but not for wrist placement. The ROC-derived cut-points can be used to classify youth SB with the wrist and with vector magnitude data.     

Wolverine behavior varies spatially with anthropogenic footprint: implications for conservation and inferences about declines

Understanding a species’ behavioral response to rapid environmental change is an ongoing challenge in modern conservation. Anthropogenic landscape modification, or “human footprint,” is well documented as a central cause of large mammal decline and range contractions where the proximal mechanisms of decline are often contentious. Direct mortality is an obvious cause; alternatively, human‐modified landscapes perceived as unsuitable by some species may contribute to shifts in space use through preferential habitat selection. A useful approach to tease these effects apart is to determine whether behaviors potentially associated with risk vary with human footprint. We hypothesized wolverine (Gulo gulo) behaviors vary with different degrees of human footprint. We quantified metrics of behavior, which we assumed to indicate risk perception, from photographic images from a large existing camera‐trapping dataset collected to understand wolverine distribution in the Rocky Mountains of Alberta, Canada. We systematically deployed 164 camera sites across three study areas covering approximately 24,000 km², sampled monthly between December and April (2007–2013). Wolverine behavior varied markedly across the study areas. Variation in behavior decreased with increasing human footprint. Increasing human footprint may constrain potential variation in behavior, through either restricting behavioral plasticity or individual variation in areas of high human impact. We hypothesize that behavioral constraints may indicate an increase in perceived risk in human‐modified landscapes. Although survival is obviously a key contributor to species population decline and range loss, behavior may also make a significant contribution.     

Separating Bedtime Rest from Activity Using Waist or Wrist-Worn Accelerometers in Youth

Recent interest in sedentary behavior and technological advances expanded use of watch-size accelerometers for continuous monitoring of physical activity (PA) over extended periods (e.g., 24 h/day for 1 week) in studies conducted in natural living environment. This approach necessitates the development of new methods separating bedtime rest and activity periods from the accelerometer recordings. The goal of this study was to develop a decision tree with acceptable accuracy for separating bedtime rest from activity in youth using accelerometer placed on waist or wrist. Minute-by-minute accelerometry data were collected from 81 youth (10–18 years old, 47 females) during a monitored 24-h stay in a whole-room indirect calorimeter equipped with a force platform covering the floor to detect movement. Receiver Operating Characteristic (ROC) curve analysis was used to determine the accelerometer cut points for rest and activity. To examine the classification differences, the accelerometer bedtime rest and activity classified by the algorithm in the development group (n = 41) were compared with actual bedtime rest and activity classification obtained from the room calorimeter-measured metabolic rate and movement data. The selected optimal bedtime rest cut points were 20 and 250 counts/min for the waist- and the wrist-worn accelerometer, respectively. The selected optimal activity cut points were 500 and 3,000 counts/min for waist and wrist-worn accelerometers, respectively. Bedtime rest and activity were correctly classified by the algorithm in the validation group (n = 40) by both waist- (sensitivity: 0.983, specificity: 0.946, area under ROC curve: 0. 872) and wrist-worn (0.999, 0.980 and 0.943) accelerometers. The decision tree classified bedtime rest correctly with higher accuracy than commonly used automated algorithm for both waist- and wrist-warn accelerometer (all p<0.001). We concluded that cut points developed and validated for waist- and wrist-worn uniaxial accelerometer have a good power for accurate separation of time spent in bedtime rest from activity in youth.     

Quantifying Tropical Dry Forest Type and Succession: Substantial Improvement with LiDAR

Improved technologies are needed to advance our knowledge of the biophysical and human factors influencing tropical dry forests, one of the world's most threatened ecosystems. We evaluated the use of light detection and ranging (LiDAR) data to address two major needs in remote sensing of tropical dry forests, i.e., classification of forest types and delineation of forest successional status. We evaluated LiDAR‐derived measures of three‐dimensional canopy structure and subcanopy topography using classification‐tree techniques to separate different dry forest types and successional stages in the Guánica Biosphere Reserve in Puerto Rico. We compared the LiDAR‐based results with classifications made from commonly used remote sensing data, including Landsat satellite imagery and radar‐based topographic data. The accuracy of the LiDAR‐based forest type classification (including native‐ and exotic‐dominated forest classes) was substantially higher than those from previously available data (kappa = 0.90 and 0.63, respectively). The best result was obtained when combining LiDAR‐derived metrics of canopy structure and topography, and adding Landsat spectral data did not improve the classification. For the second objective, we observed that LiDAR‐derived variables of vegetation structure were better predictors of forest successional status (i.e., mid‐secondary, late‐secondary, and primary forests) than was spectral information from Landsat. Importantly, the key LiDAR predictors identified within each classification‐tree model agreed with previous ecological knowledge of these forests. Our study highlights the value of LiDAR remote sensing for assessing tropical dry forests, reinforcing the potential for this novel technology to advance research and management of tropical forests in general.     

Estimating the energy expenditure of free‐ranging polar bears using tri‐axial accelerometers: A validation with doubly labeled water

Measures of energy expenditure can be used to inform animal conservation and management, but methods for measuring the energy expenditure of free‐ranging animals have a variety of limitations. Advancements in biologging technologies have enabled the use of dynamic body acceleration derived from accelerometers as a proxy for energy expenditure. Although dynamic body acceleration has been shown to strongly correlate with oxygen consumption in captive animals, it has been validated in only a few studies on free‐ranging animals. Here, we use relationships between oxygen consumption and overall dynamic body acceleration in resting and walking polar bears Ursus maritimus and published values for the costs of swimming in polar bears to estimate the total energy expenditure of 6 free‐ranging polar bears that were primarily using the sea ice of the Beaufort Sea. Energetic models based on accelerometry were compared to models of energy expenditure on the same individuals derived from doubly labeled water methods. Accelerometer‐based estimates of energy expenditure on average predicted total energy expenditure to be 30% less than estimates derived from doubly labeled water. Nevertheless, accelerometer‐based measures of energy expenditure strongly correlated (r² = 0.70) with measures derived from doubly labeled water. Our findings highlight the strengths and limitations in dynamic body acceleration as a measure of total energy expenditure while also further supporting its use as a proxy for instantaneous, detailed energy expenditure in free‐ranging animals.     

Inferring Behavioral States of Grazing Livestock from High-Frequency Position Data Alone

Studies of animal behavior are crucial to understanding animal-ecosystem interactions, but require substantial efforts in visual observation or sensor measurement. We investigated how classifying behavioral states of grazing livestock using global positioning data alone depends on the classification approach, the preselection of training data, and the number and type of movement metrics. Positions of grazing cows were collected at intervals of 20 seconds in six upland areas in Switzerland along with visual observations of animal behavior for comparison. A total of 87 linear and cumulative distance metrics and 15 turning angle metrics across multiple time steps were used to classify position data into the behavioral states of walking, grazing, and resting. Five random forest classification models, a linear discriminant analysis, a support vector machine, and a state-space model were evaluated. The most accurate classification of the observed behavioral states in an independent validation dataset was 83%, obtained using random forest with all available movement metrics. However, the state-specific accuracy was highly unequal (walking: 36%, grazing: 95%, resting: 58%). Random undersampling led to a prediction accuracy of 77%, with more balanced state-specific accuracies (walking: 68%, grazing: 82%, resting: 68%). The other evaluated machine-learning approaches had lower classification accuracies. The state-space model, based on distance to the preceding position and turning angle, produced a relatively low accuracy of 64%, slightly lower than a random forest model with the same predictor variables. Given the successful classification of behavioral states, our study promotes the more frequent use of global positioning data alone for animal behavior studies under the condition that data is collected at high frequency and complemented by context-specific behavioral observations. Machine-learning algorithms, notably random forest, were found very useful for classification and easy to implement. Moreover, the use of measures across multiple time steps is clearly necessary for a satisfactory classification.     

The use of DNA barcodes to estimate phylogenetic diversity in forest communities of southern China

To elucidate potential ecological and evolutionary processes associated with the assembly of plant communities, there is now widespread use of estimates of phylogenetic diversity that are based on a variety of DNA barcode regions and phylogenetic construction methods. However, relatively few studies consider how estimates of phylogenetic diversity may be influenced by single DNA barcodes incorporated into a sequence matrix (conservative regions vs. hypervariable regions) and the use of a backbone family‐level phylogeny. Here, we use general linear mixed‐effects models to examine the influence of different combinations of core DNA barcodes (rbcL, matK, ITS, and ITS2) and phylogeny construction methods on a series of estimates of community phylogenetic diversity for two subtropical forest plots in Guangdong, southern China. We ask: (a) What are the relative influences of single DNA barcodes on estimates phylogenetic diversity metrics? and (b) What is the effect of using a backbone family‐level phylogeny to estimate topology‐based phylogenetic diversity metrics? The combination of more than one barcode (i.e., rbcL + matK + ITS) and the use of a backbone family‐level phylogeny provided the most parsimonious explanation of variation in estimates of phylogenetic diversity. The use of a backbone family‐level phylogeny showed a stronger effect on phylogenetic diversity metrics that are based on tree topology compared to those that are based on branch lengths. In addition, the variation in the estimates of phylogenetic diversity that was explained by the top‐rank models ranged from 0.1% to 31% and was dependent on the type of phylogenetic community structure metric. Our study underscores the importance of incorporating a multilocus DNA barcode and the use of a backbone family‐level phylogeny to infer phylogenetic diversity, where the type of DNA barcode employed and the phylogenetic construction method used can serve as a significant source of variation in estimates of phylogenetic community structure.     

Outlier analyses to test for local adaptation to breeding grounds in a migratory arctic seabird

Investigating the extent (or the existence) of local adaptation is crucial to understanding how populations adapt. When experiments or fitness measurements are difficult or impossible to perform in natural populations, genomic techniques allow us to investigate local adaptation through the comparison of allele frequencies and outlier loci along environmental clines. The thick‐billed murre (Uria lomvia) is a highly philopatric colonial arctic seabird that occupies a significant environmental gradient, shows marked phenotypic differences among colonies, and has large effective population sizes. To test whether thick‐billed murres from five colonies along the eastern Canadian Arctic coast show genomic signatures of local adaptation to their breeding grounds, we analyzed geographic variation in genome‐wide markers mapped to a newly assembled thick‐billed murre reference genome. We used outlier analyses to detect loci putatively under selection, and clustering analyses to investigate patterns of differentiation based on 2220 genomewide single nucleotide polymorphisms (SNPs) and 137 outlier SNPs. We found no evidence of population structure among colonies using all loci but found population structure based on outliers only, where birds from the two northernmost colonies (Minarets and Prince Leopold) grouped with birds from the southernmost colony (Gannet), and birds from Coats and Akpatok were distinct from all other colonies. Although results from our analyses did not support local adaptation along the latitudinal cline of breeding colonies, outlier loci grouped birds from different colonies according to their non‐breeding distributions, suggesting that outliers may be informative about adaptation and/or demographic connectivity associated with their migration patterns or nonbreeding grounds.     

Quantifying aphid behavioral responses to environmental change

Aphids are the most common vector of plant viruses, and their feeding behavior is an important determinant of virus transmission. Positive effects of global change on aphid performance have been documented, but effects on aphid behavior are not known. We assessed the plant‐mediated behavioral responses of a generalist aphid, Myzus persicae (Sulzer) (Hemiptera: Aphididae), to increased CO₂ and nitrogen when feeding on each of three host species: Amaranthus viridis L. (Amaranthaceae), Polygonum persicaria L. (= Persicaria maculosa Gray) (Polygonaceae), and Solanum dulcamara L. (Solanaceae). Via a family of constrained Markov models, we tested the degree to which aphid movements demonstrate preference among host species or plants grown under varying environmental conditions. Entropy rates of the estimated Markov chains were used to further quantify aphid behavior. Our statistical methods provide a general tool for assessing choice and quantitatively comparing animal behavior under different conditions. Aphids displayed strong preferences for the same host species under all growth conditions, indicating that CO₂‐ and N‐induced changes in plant chemistry have minimal effects on host preference. However, entropy rates increased in the presence of non‐preferred hosts, even when preferred hosts were available. We conclude that the presence of a non‐preferred host species affected aphid‐feeding behavior more than changes in plant leaf chemistry when plants were grown under elevated CO₂ and increased N availability.     

Determining seabird body condition using nonlethal measures

Energy stores are critical for successful breeding, and longitudinal studies require nonlethal methods to measure energy stores ("body condition"). Nonlethal techniques for measuring energy reserves are seldom verified independently. We compare body mass, size-corrected mass (SCM), plasma lipids, and isotopic dilution with extracted total body lipid content in three seabird species (thick-billed murres Uria lomvia, all four measures; northern fulmars Fulmarus glacialis, three measures; and black-legged kittiwakes Rissa tridactyla, two measures). SCM and body mass were better predictors of total body lipids for the species with high percent lipids (fulmars; R2 = 0.5-0.6) than for the species with low percent lipids (murres and kittiwakes; R2 = 0.2-0.4). The relationship between SCM and percent body lipids, which we argue is often a better measure of condition, was also poor (R2 < 0.2) for species with low lipids. In a literature comparison of 17 bird species, percent lipids was the only predictor of the strength of the relationship between mass and total body lipids; we suggest that SCM be used as an index of energy stores only when lipids exceed 15% of body mass. Across all three species we measured, SCM based on the ordinary least squares regression of mass on the first principal component outperformed other measures. Isotopic dilution was a better predictor of both total body lipids and percent body lipids than were mass, SCM, or plasma lipids in murres. Total body lipids decreased through the breeding season at both sites, while total and neutral plasma lipid concentrations increased at one site but not another, suggesting mobilization of lipid stores for breeding. A literature review showed substantial variation in the reliability of plasma markers, and we recommend isotopic dilution (oxygen-18, plateau) for determination of energy reserves in birds where lipid content is below 15%.     

Climate change in the Arctic: Testing the poleward expansion of ticks and tick-borne diseases

Climate change is most strongly felt in the polar regions of the world, with significant impacts on the species that live there. The arrival of parasites and pathogens from more temperate areas may become a significant problem for these populations, but current observations of parasite presence often lack a historical reference of prior absence. Observations in the high Arctic of the seabird tick Ixodes uriae suggested that this species expanded poleward in the last two decades in relation to climate change. As this tick can have a direct impact on the breeding success of its seabird hosts and vectors several pathogens, including Lyme disease spirochaetes, understanding its invasion dynamics is essential for predicting its impact on polar seabird populations. Here, we use population genetic data and host serology to test the hypothesis that I. uriae recently expanded into Svalbard. Both black-legged kittiwakes (Rissa tridactyla) and thick-billed murres (Uria lomvia) were sampled for ticks and blood in Kongsfjorden, Spitsbergen. Ticks were genotyped using microsatellite markers and population genetic analyses were performed using data from 14 reference populations from across the tick's northern distribution. In contrast to predictions, the Spitsbergen population showed high genetic diversity and significant differentiation from reference populations, suggesting long-term isolation. Host serology also demonstrated a high exposure rate to Lyme disease spirochaetes (Bbsl). Targeted PCR and sequencing confirmed the presence of Borrelia garinii in a Spitsbergen tick, demonstrating the presence of Lyme disease bacteria in the high Arctic for the first time. Taken together, results contradict the notion that I. uriae has recently expanded into the high Arctic. Rather, this tick has likely been present for some time, maintaining relatively high population sizes and an endemic transmission cycle of Bbsl. Close future observations of population infestation/infection rates will now be necessary to relate epidemiological changes to ongoing climate modifications.     

Carbon emissions performance of commercial logging in East Kalimantan,Indonesia

Adoption of reduced‐impact logging (RIL) methods could reduce CO₂ emissions by 30–50% across at least 20% of remaining tropical forests. We developed two cost effective and robust indices for comparing the climate benefits (reduced CO₂ emissions) due to RIL. The indices correct for variability in the volume of commercial timber among concessions. We determined that a correction for variability in terrain slope was not needed. We found that concessions certified by the Forest Stewardship Council (FSC, N = 3), when compared with noncertified concessions (N = 6), did not have lower overall CO₂ emissions from logging activity (felling, skidding, and hauling). On the other hand, FSC certified concessions did have lower emissions from one type of logging impact (skidding), and we found evidence of a range of improved practices using other field metrics. One explanation of these results may be that FSC criteria and indicators, and associated RIL practices, were not designed to achieve overall emissions reductions. Also, commonly used field metrics are not reliable proxies for overall logging emissions performance. Furthermore, the simple distinction between certified and noncertified concessions does not fully represent the complex history of investments in improved logging practices. To clarify the relationship between RIL and emissions reductions, we propose the more explicit term ‘RIL‐C’ to refer to the subset of RIL practices that can be defined by quantified thresholds and that result in measurable emissions reductions. If tropical forest certification is to be linked with CO₂ emissions reductions, certification standards need to explicitly require RIL‐C practices.     

Detecting surface‐feeding behavior by rorqual whales in accelerometer data

The movement of marine animals feeding at the sea surface is restricted by wave drag and a reduction in propulsive efficiency. Many rorqual whale species lunge feed at the surface, yet existing methodologies for detecting lunges in accelerometer data have not been applied to surface‐feeding behavior. Our study aimed to develop a method to detect surface‐feeding behavior in accelerometer data and in doing so, determine whether wave drag influences the detection of surface‐feeding behavior. A new acceleration parameter is described that considers the forward acceleration of the animal relative to its pitch. The new parameter, along with information on the deceleration and pitch angle, was then used in an automatic lunge detecting algorithm followed by a visual classification method that detected approximately 70% of the lunges observed during focal follow sampling. The forward acceleration of lunges decreased significantly with increasing proximity to the surface. This lower acceleration at the surface may influence the ability to detect lunge feeding behavior close to the surface. Future research should attempt to determine the cause of this relationship, which may be the influence of changes in the forces acting on the whale or behavioral flexibility by the whale.     

Ecological niche modeling re‐examined: A case study with the Darwin's fox

Many previous studies have attempted to assess ecological niche modeling performance using receiver operating characteristic (ROC) approaches, even though diverse problems with this metric have been pointed out in the literature. We explored different evaluation metrics based on independent testing data using the Darwin's Fox (Lycalopex fulvipes) as a detailed case in point. Six ecological niche models (ENMs; generalized linear models, boosted regression trees, Maxent, GARP, multivariable kernel density estimation, and NicheA) were explored and tested using six evaluation metrics (partial ROC, Akaike information criterion, omission rate, cumulative binomial probability), including two novel metrics to quantify model extrapolation versus interpolation (E‐space index I) and extent of extrapolation versus Jaccard similarity (E‐space index II). Different ENMs showed diverse and mixed performance, depending on the evaluation metric used. Because ENMs performed differently according to the evaluation metric employed, model selection should be based on the data available, assumptions necessary, and the particular research question. The typical ROC AUC evaluation approach should be discontinued when only presence data are available, and evaluations in environmental dimensions should be adopted as part of the toolkit of ENM researchers. Our results suggest that selecting Maxent ENM based solely on previous reports of its performance is a questionable practice. Instead, model comparisons, including diverse algorithms and parameterizations, should be the sine qua non for every study using ecological niche modeling. ENM evaluations should be developed using metrics that assess desired model characteristics instead of single measurement of fit between model and data. The metrics proposed herein that assess model performance in environmental space (i.e., E‐space indices I and II) may complement current methods for ENM evaluation.