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Innovative techniques, such as environmental DNA (eDNA) metabarcoding, are now promoting broader biodiversity monitoring at unprecedented scales, because of the reduction in time, presumably lower cost, and methodological efficiency. Our goal was to assess the efficiency of established inventory techniques (live-trapping grids, pitfall traps, camera trapping, mist netting) as well as eDNA for detecting Amazonian mammals. For terrestrial small mammals, we used 32 live-trapping grids based on Sherman and Tomahawk traps (total effort of 10,368 trap-nights); in addition to 16 pitfall traps (1,408 trap-nights). For bats, we used mist nets at 8 sites (4,800 net hours). For medium and large mammals, we used 72 camera trap stations (5,208 camera-days). We identified vertebrate and mammal taxa based on eDNA analysis (12S region, with V05 and Mamm01 markers) from water samples, including a total of 11 3-km transects for stagnant water sampling and seven small streams for running water sampling. A total of 106 mammal species were recorded. Building on sample-based rarefaction and extrapolation curves, both trapping grids and pitfall were successful, recording 91.16% and 82.1% of the expected species for these techniques (~22 and ~9 species), and 16.98% and 6.60% of the total recorded mammal species, respectively. Mist nets recorded 83.2% of the expected bat species (~48), and 34.91% of the total recorded species. Camera trapping recorded 99.2% of the predicted large- and medium-sized species (~31), and 33.02% of the total recorded species. eDNA recorded 75.4% of the expected mammal species for this technique (~68), and 47.0% of the total recorded species. eDNA resulted in a useful tool that saves on effort and reduces sampling costs. This study is among the first to show the large potential of eDNA metabarcoding for assessing Amazonian mammal communities, providing, in combination with conventional techniques, a rapid overview of mammal diversity with broad applications to monitoring, management and conservation. By including appropriate genetic markers and updated reference databases, eDNA metabarcoding method can be extended to the whole vertebrate community.

Purpose of Review Our review provides an overview of forest attributes measurable by forest inventory that may support the integration of non-provisioning ecosystem services (ES) and biodiversity into forest planning. The review identifies appropriate forest attributes to quantify the opportunity for recreation, biodiversity promotion and carbon storage, and describes new criteria that future forest inventories may include. As a source of information, we analyse recent papers on forest inventory and ES to show if and how they address these criteria. We further discuss how mapping ES could benefit from such new criteria and conclude with three case studies illustrating the importance of selected criteria delivered by forest inventory. Recent Findings Recent studies on forest inventory focus mainly on carbon storage and biodiversity promotion, while very few studies address the opportunity of recreation. Field sampling still dominates the data collection, despite the fact that airborne laser scanning (ALS) has much improved the precision of large-scale estimates of the level of forest ES provision. However, recent inventory studies have hardly addressed criteria such as visible distance in stands, presence of open water bodies and soil damages (important for the opportunity of recreation) and naturalness (here understood as the similarity of the forest to its natural state) and habitat trees and natural clearings (important for biodiversity promotion). The problem of quantifying carbon stock changes with appropriate precision has not been addressed. In addition, the reviewed studies have hardly explored the potential of inventory information to support mapping of the demand for ES. Summary We identify challenges with estimating a number of criteria associated with rare events, relevant for both the opportunity of recreation and biodiversity promotion. These include deadwood, rare species and habitat trees. Such rare events require innovative inventory technology, such as point-transect sampling or ALS. The ALS technology needs relatively open canopies, to achieve reliable estimates for deadwood or understorey vegetation. For the opportunity of recreation, the diversity among forest stands (possibly quantified by geoinformatics) and information on the presence of open water bodies (provided by RADAR, ALS data or use of existing maps) may be important. Naturalness is a crucial criterion for native biodiversity promotion but hard to quantify and assess until now. Tree species identification would be crucial for this criterion, which is still a challenge for remote sensing techniques. Estimating carbon storage may build on biomass estimates from terrestrial samples or on remotely sensed data, but major problems exist with the precision of estimates for carbon stock changes. Recent approaches for mapping the supply side of forest ES are promising, while providing so far uncommon structural information by revised inventory concepts could be helpful also for mapping the demand for ES. We conclude that future studies must find holistic inventory management systems to couple various inventory technologies in support of the integration of non-provisioning ES and biodiversity into forest planning.

It is anticipated that many of the future forest mapping applications will be based on three-dimensional (3D) point clouds. A comparison study was conducted to verify the explanatory power and information contents of several 3D remote sensing data sources on the retrieval of above ground biomass (AGB), stem volume (VOL), basal area (G), basal-area weighted mean diameter (Dg) and Lorey’s mean height (Hg) at the plot level, utilizing the following data: synthetic aperture radar (SAR) Interferometry, SAR radargrammetry, satellite-imagery having stereo viewing capability, airborne laser scanning (ALS) with various densities (0.8–6 pulses/m2) and aerial stereo imagery. Laser scanning is generally known as the primary source providing a 3D point cloud. However, photogrammetric, radargrammetric and interferometric techniques can be used to produce 3D point clouds from space- and air-borne stereo images. Such an image-based point cloud could be utilized in a similar manner as ALS providing that accurate digital terrain model is available. In this study, the performance of these data sources for providing point cloud data was evaluated with 91 sample plots that were established in Evo, southern Finland within a boreal forest zone and surveyed in 2014 for this comparison. The prediction models were built using random forests technique with features derived from each data sources as independent variables and field measurements of forest attributes as response variable. The relative root mean square errors (RMSEs) varied in the ranges of 4.6% (0.97 m)–13.4% (2.83 m) for Hg, 11.7% (3.0 cm)–20.6% (5.3 cm) for Dg, 14.8% (4.0 m2/ha)–25.8% (6.9 m2/ha) for G, 15.9% (43.0 m3/ha)–31.2% (84.2 m3/ha) for VOL and 14.3% (19.2 Mg/ha)–27.5% (37.0 Mg/ha) for AGB, respectively, depending on the data used. Results indicate that ALS data achieved the most accurate estimates for all forest inventory attributes. For image-based 3D data, high-altitude aerial images and WorldView-2 satellite optical image gave similar results for Hg and Dg, which were only slightly worse than those of ALS data. As expected, spaceborne SAR data produced the worst estimates. WorldView-2 satellite data performed well, achieving accuracy comparable to the one with ALS data for G, VOL and AGB estimation. SAR interferometry data seems to contain more information for forest inventory than SAR radargrammetry and reach a better accuracy (relative RMSE decreased from 13.4% to 9.5% for Hg, 20.6% to 19.2% for Dg, 25.8% to 20.9% for G, 31.2% to 22.0% for VOL and 27.5% to 20.7% for AGB, respectively). However, the availability of interferometry data is limited. The results confirmed the high potential of all 3D remote sensing data sources for forest inventory purposes. However, the assumption of using other than ALS data is that there exist a high quality digital terrain model, in our case it was derived from ALS.

Tremendous effort is expended in counting migrant raptors in North America, where over 1,500 count sites exist. The Biological Resources Division of the United States Geological Survey is investigating whether migration counts can serve to monitor population trends. To that end, we investigated the effects of changes in survey duration on power to detect trends using various sample sizes, lengths of surveys, and sampling frequencies within a season based on raptor migration count data collected from 7 sites. As expected, power to detect trends increased as sample size (number of years of counts) increased; its magnitude depended on level of trend and variation in annual counts. Except for cases with extremely low or high power, 5 additional survey years increased power 20%-50%. Changing survey length affected power, but results varied and depended on the migration pattern of species at specific sites. Power to detect trends did not change appreciably when counts were conducted only during 90 days of peak migration (for all species combined). However, when annual counts were based on 30 or 60 consecutive days of peak migration, we found decreases in power for most site-species combinations. Changing the number of days counted during the week resulted in variable changes in power. Our results suggest the number of count days within weeks necessary for sufficient monitoring should be evaluated on a site- and species-specific basis. We determined that a coefficient of variation of 30% or less in annual counts is needed to detect a 3% average annual decrease in counts over 25 years with at least 80% power. This requirement was met at one or more sites for 14 of 20 raptor species. Power to detect increasing trends exceeded power to detect decreasing trends, which is unfortunate from a conservation perspective. Nevertheless, if count data are collected consistently, large-scale population trends for some raptor species may be monitored effectively using migration counts.

Studies on the food habits of brown bears (Ursus arctos) often have used subjective methods to evaluate diet. We compared 3 quantitative methods (point-frame, volumetric, and gravimetric) to evaluate the diet of 55 Hokkaido brown bears (U. a. yesoensis) using stomach contents. Compared to the volumetric method, the point-frame method underestimated the composition of berries as bulky items (R2= 91.3, P<0.001), whereas composition of forbs as flat items was overestimated (R2= 95.0, P<0.001). Evaluation of whole items, however, directly related to those of the volumetric method and the gravimetric method (the point-frame method vs. the volumetric method:$R^{2}=97.8$, P<0.001; the point-frame method vs. the gravimetric method:$R^{2}=95.4$, P<0.001). Time required for the point-frame method was only 16% of that of the volumetric method. We concluded that the point-frame method is a time-saving and accurate method to evaluate the diet of brown bears.

Although elf owls (Micrathene whitneyi) and western screech owls (Otus kennicottii) are of management interest because of their potential to serve as barometers of environmental change, factors affecting the detection of these species have not been quantified. We conducted point counts for elf owls and western screech owls from 1994 to 1996 in the Sonoran Desert, southwestern Arizona. We assessed whether owls were more detectable when broadcasts were used than when they were not, and how temporal, lunar, weather, and biological variables affected detection rates. We assessed factors that potentially varied within a night (weather, time, presence of other owls) separately from those that did not vary within a night (date, moon phase). Elf owls and western screech owls were more likely (P<0.001) to be detected when conspecific broadcasts were used than when broadcasts were not used. Within the breeding season, detection rates of elf owls were greatest during the late advertising period (11 April to 30 April) between the first-quarter and third-quarter moon phases. Detection rates of screech owls did not differ among moon phases or dates (P>0.40). Controlling for moon phase and date, elf owls were most frequently detected during calm (wind <5 mph), moonlit conditions. Increased detection rates of western screech owls were associated with decreased wind speed, temperature, and cloud cover. We recommend that conspecific broadcasts be used during surveys to increase detection rates for both species. Elf owls should be surveyed during the late advertising period between the first-quarter and third-quarter moon.

Population trends of small-game species as determined from rural mail carrier surveys (RMCSs) have been historically indexed as count/distance traveled by observers. However, this index might not accurately depict population trends if the number of participating observers changes over time. We examined 32 years of Kansas October RMCS data to determine the most appropriate index of population of the northern bobwhite (Colinus virginianus). Significant declines in the number of participating observers altered the relationship between the count/distance traveled and year and should be accounted for by incorporating it into the index of count/distance traveled.

We develop a modular and tractable framework for solving an adaptive distributionally robust linear optimization problem, where we minimize the worst-case expected cost over an ambiguity set of probability distributions. The adaptive distributionally robust optimization framework caters for dynamic decision making, where decisions adapt to the uncertain outcomes as they unfold in stages. For tractability considerations, we focus on a class of second-order conic (SOC) representable ambiguity set, though our results can easily be extended to more general conic representations. We show that the adaptive distributionally robust linear optimization problem can be formulated as a classical robust optimization problem. To obtain a tractable formulation, we approximate the adaptive distributionally robust optimization problem using linear decision rule (LDR) techniques. More interestingly, by incorporating the primary and auxiliary random variables of the lifted ambiguity set in the LDR approximation, we can significantly improve the solutions, and for a class of adaptive distributionally robust optimization problems, exact solutions can also be obtained. Using the new LDR approximation, we can transform the distributionally adaptive robust optimization problem to a classical robust optimization problem with an SOC representable uncertainty set. Finally, to demonstrate the potential for solving management decision problems, we develop an algebraic modeling package and illustrate how it can be used to facilitate modeling and obtain high-quality solutions for medical appointment scheduling and inventory management problems. The electronic companion is available at https://doi.org/10.1287/mnsc.2017.2952 . This paper was accepted by Noah Gans, optimization.

We analyze the joint inventory and pricing decisions of a firm when demand depends on not only the current selling price but also a memory-based reference price and customers are loss averse. The presence of reference price effect leads to a nonconcave one-period expected revenue in price and reference price. We introduce a transformation technique that allows us to prove under some mild assumptions the optimality of a reference-price-dependent base-stock list-price policy, which is characterized by a base-stock level and a target reference price. In addition, we show that the target reference price is increasing in the reference price, but except in the loss-neutral case, the base-stock level is not monotone in the reference price. We also show that in the steady state of the model with the reference price effect, the optimal price is lower while the optimal base-stock level is higher than their counterparts in the model without the reference price effect.

In recent years, there has been much interest in data-driven decision making. Although this can unlock tremendous value across industries, it is very important to remember that data-driven decisions are uncertain quantities with error bars associated with them. Despite the obvious importance, error bars of data-driven decisions have been underinvestigated. The paper “Confidence Intervals for Data-Driven Inventory Policies with Demand Censoring” bridges this gap in knowledge for inventory problems. Specifically, Gah-Yi Ban derives approximate analytic formulas for confidence intervals of data-driven solutions to the classical dynamic inventory management problem of Scarf (1959b) . Both censored and uncensored data scenarios are considered, and the analyses extend to other commonly studied problems, such as the repeated newsvendor problem and base stock policy problem. Extensive computations on realistic simulated data validate the approximate analytic formulas, which are based on asymptotic theory, establishing their practical value. We revisit the classical dynamic inventory management problem of Scarf [Scarf H (1959b) The optimality of ( s , S ) policies in the dynamic inventory problem. Arrow KJ, Karlin S, Suppes P, eds. Mathematical Methods in the Social Science (Stanford University Press, Stanford, CA), 196–202.] from the perspective of a decision maker who has n historical selling seasons of data and must make ordering decisions for the upcoming season. We develop a nonparametric estimation procedure for the (S, s) policy that is consistent and then characterize the finite sample properties of the estimated (S, s) levels by deriving their asymptotic confidence intervals. We also consider having at least some of the past selling seasons of data censored from the absence of backlogging and show that the intuitive procedure of first correcting for censoring in the demand data yields inconsistent estimates. We then show how to correctly use the censored data to obtain consistent estimates and derive asymptotic confidence intervals for this policy using Stein’s method. We further show the confidence intervals can be used to effectively bound the difference between the expected total cost of an estimated policy and that of the optimal policy. We validate our results with extensive computations on simulated data. Our results extend to the repeated newsvendor problem and the base stock policy problem by appropriate parameter choices.