Robert J. Cooper

Illegal logging is a major driver of tropical deforestation, accounting for the majority of timber harvested in many tropical countries and degrading many protected areas, due to both weak law enforcement capacity and corruption. Commercial logging is illegal in Peru’s Allpahuayo-Mishana National Reserve, a state protected area, but clandestine logging operations persist and affect its biodiversity, including the endemic bird species associated with its rare Amazonian white-sand forests. We examined the effects of illegal logging operations on white-sand forest understory bird communities as sentinels of biodiversity. We sampled birds with mist nets at 12 study sites in unlogged forest and forest regenerating between 1 and 10 years after timber harvest, capturing and releasing 348 birds representing 54 species in 16 families. Forest structure differed significantly between forest treatments, with canopy cover in logged forest significantly lower than in unlogged forest. All avian foraging guilds tested (including ant followers, other insectivores, frugivores, granivores, and nectarivores) responded significantly to changes in one or more forest structure characteristics we measured. The abundance of ant followers and other insectivores was positively correlated with canopy cover, while granivore abundance was positively correlated with subcanopy cover, and both frugivore and nectarivore abundance was negatively correlated with the numbers of trees in white-forest stands. We also took a rare opportunity to compare avian foraging guilds and relative abundance using capture data collected at the same white-sand forest sites in both 2005 and 2023. Over this 18-year period, the total number of understory birds and ant followers in particular declined, whereas other insectivores increased with time since logging. Our results demonstrate that logging has significant influences on white-sand forest habitat structure and bird community dynamics for decades after logging events. Illegal logging threatens forests and wildlife in many tropical protected areas, and we recommend their managers prioritize both preventing illegal logging and mitigating its negative effects to effectively conserve biodiversity.

Brain-Computer Interfaces (BCI) and Neurofeedback (NFB) approaches, which both rely on real-time monitoring of brain activity, are increasingly being applied in rehabilitation, assistive technology, neurological diseases and behavioral disorders. Functional near-infrared spectroscopy (fNIRS) and diffuse optical tomography (DOT) are promising techniques for these applications due to their non-invasiveness, portability, low cost, and relatively high spatial resolution. However, real-time processing of fNIRS/DOT data remains a significant challenge as it requires establishing a baseline of the measurement, simultaneously performing real-time motion artifact (MA) correction across all channels, and (in the case of DOT) addressing the time-consuming process of image reconstruction. This study proposes a real-time processing system for fNIRS/DOT that integrates baseline calibration, denoising autoencoder (DAE) based MA correction model with a sliding window strategy, and a pre-calculated inverse Jacobian matrix to streamline the reconstructed 3D brain hemodynamics. The DAE model was trained on an extensive whole-head high-density DOT (HD-DOT) dataset and tested on separate motor imagery dataset augmented with artificial MA. The system demonstrated the capability to simultaneously process approximately 750 channels in real-time. Our results show that the DAE-based MA correction method outperformed traditional MA correction in terms of mean squared error and correlation to the known MA-free data while maintaining low latency, which is critical for effective BCI and NFB applications. The system's high-channel, real-time processing capability provides channel-wise oxygenation information and functional 3D imaging, making it well-suited for fNIRS/DOT applications in BCI and NFB, particularly in movement-intensive scenarios such as motor rehabilitation and assistive technology for mobility support.

Motor impairment has a profound impact on a significant number of individuals, leading to a substantial demand for rehabilitation services. Through brain–computer interfaces (BCIs), people with severe motor disabilities could have improved communication with others and control appropriately designed robotic prosthetics, so as to (at least partially) restore their motor abilities. BCI plays a pivotal role in promoting smoother communication and interactions between individuals with motor impairments and others. Moreover, they enable the direct control of assistive devices through brain signals. In particular, their most significant potential lies in the realm of motor rehabilitation, where BCIs can offer real-time feedback to assist users in their training and continuously monitor the brain’s state throughout the entire rehabilitation process. Hybridization of different brain-sensing modalities, especially functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG), has shown great potential in the creation of BCIs for rehabilitating the motor-impaired populations. EEG, as a well-established methodology, can be combined with fNIRS to compensate for the inherent disadvantages and achieve higher temporal and spatial resolution. This paper reviews the recent works in hybrid fNIRS-EEG BCIs for motor rehabilitation, emphasizing the methodologies that utilized motor imagery. An overview of the BCI system and its key components was introduced, followed by an introduction to various devices, strengths and weaknesses of different signal processing techniques, and applications in neuroscience and clinical contexts. The review concludes by discussing the possible challenges and opportunities for future development.

SignificanceTo effectively apply functional near-infrared spectroscopy (fNIRS)/diffuse optical tomography (DOT) devices, a three-dimensional (3D) model of the position of each optode on a subject’s scalp and the positions of that subject’s cranial landmarks are critical. Obtaining this information accurately in infants, who rarely stop moving, is an ongoing challenge.AimWe propose a smartphone-based registration system that can potentially achieve a full-head 3D scan of a 6-month-old infant instantly.ApproachThe proposed system is remotely controlled by a custom-designed Bluetooth controller. The scanned images can either be manually or automatically aligned to generate a 3D head surface model.ResultsA full-head 3D scan of a 6-month-old infant can be achieved within 2 s via this system. In testing on a realistic but static infant head model, the average Euclidean error of optode position using this device was 1.8 mm.ConclusionsThis low-cost 3D registration system therefore has the potential to permit accurate and near-instant fNIRS/DOT spatial registration.

Conservation planning for large ecosystems has multiple benefits but is often challenging to implement because of the multiple jurisdictions, species, and habitats involved. In addition, decision making at large spatial scales can be hampered because many approaches do not explicitly incorporate potentially competing values and concerns of stakeholders. After the Deepwater Horizon oil spill, establishing baselines was challenging because of (1) variation in study designs, (2) inconsistent use of explicit objectives and hypotheses, (3) inconsistent use of standardized monitoring protocols, and (4) variation in spatial and temporal scope associated with avian monitoring projects before the spill. Herein, we show how the Gulf of Mexico Avian Monitoring Network members used structured decision making to identify bird monitoring priorities. We used multiple tools and techniques to clearly define the problem and stakeholder objectives and to identify bird monitoring priorities at the scale of the entire northern Gulf of Mexico region. Although our example is specific to the northern Gulf of Mexico, this approach provides an example of how stakeholder values can be incorporated into the coordination process of broad-scale monitoring programs to address management, restoration, and scientific questions in other ecosystems and for other taxa. History: This paper was refereed. This paper was accepted for the Special Issue of INFORMS Journal on Applied Analytics—Decision Analysis. Funding: Thanks to the National Fish and Wildlife Foundation [Grant 324423], which supported A. Fournier as a postdoctoral research associate at Mississippi State University. M. Woodrey was supported by the U.S. Department of Agriculture, National Institute of Food and Agriculture, Hatch Project funds, the Mississippi Agricultural and Forestry Experiment Station, National Oceanographic and Atmospheric Administration [Grant NA16NOS4200088 to the Mississippi Department of Marine Resources’ Grand Bay National Estuarine Research Reserve], and the Mississippi Department of Marine Resources [Grant 8200025414]. This publication is a contribution of the Mississippi Agricultural and Forestry Experiment Station. T. Zenzal was supported by the National Oceanic and Atmospheric Administration RESTORE Act Science Program [Grant NA17NOS4510092].

Functional near-infrared spectroscopy (fNIRS) and diffuse optical imaging methods have attracted great interest because of their capacity to interrogate cerebral hemodynamics noninvasively while using devices that can be applied in a wide range of experimental circumstances and populations.^1,2

Caterpillars (larval Lepidoptera) are an essential link in trophic networks of forest ecosystems, as they serve as herbivores of vegetation and a food source for many organisms. Phenological mismatches between caterpillars, host plants, or predators may have negative effects across multiple trophic levels. Seasonal timing of caterpillar emergence and peak occurrence may be impacted by climate change, however, studying caterpillar phenology at broad spatial scales is challenging due to lack of data availability. Here, we examine two sources of caterpillar observations, opportunistic records from iNaturalist and structured surveys of forest caterpillars, and compare whether phenology patterns in these datasets are consistent across larval datasets and with more numerous records of adult butterflies. Despite substantial taxonomic differences between these three datasets, we found concurrence in patterns of early and late years in spring onset between datasets. However, the datasets do differ in how well they capture phenological responses to warmer spring temperatures. More data-rich iNaturalist caterpillar and adult butterfly records may provide a reasonable proxy of interannual deviations in forest caterpillars, however, expansions in structured survey efforts are needed to capture changing patterns in other ecologically important measures such as abundance and biomass.