Soundscape components inform acoustic index patterns and refine estimates of bird species richness
Year: 2023
Type: article
Abstract: Ecoacoustic monitoring has proliferated as autonomous recording units (ARU) have become more accessible. ARUs provide a non-invasive, passive method to assess ecosystem dynamics related to vocalizing animal behavior and human activity. With the ever-increasing volume of acoustic data, the field has grappled with summarizing ecologically meaningful patterns in recordings. Almost 70 acoustic indices have been developed that offer summarized measurements of bioacoustic activity and ecosystem conditions. However, their systematic relationships to ecologically meaningful patterns in varying sonic conditions are inconsistent and lead to non-trivial interpretations. We used an acoustic dataset of over 725,000 min of recordings across 1,195 sites in Sonoma County, California, to evaluate the relationship between 15 established acoustic indices and sonic conditions summarized using five soundscape components classified using a convolutional neural network: anthropophony (anthropogenic sounds), biophony (biotic sounds), geophony (wind and rain), quiet (lack of emergent sound), and interference (ARU feedback). We used generalized additive models to assess acoustic indices and biophony as ecoacoustic indicators of avian diversity. Models that included soundscape components explained acoustic indices with varying degrees of performance (avg. adj-R2 = 0.61 ± 0.16; n = 1,195). For example, we found the normalized difference soundscape index was the most sensitive index to biophony while being less influenced by ambient sound. However, all indices were affected by non-biotic sound sources to varying degrees. We found that biophony and acoustic indices combined were highly predictive in modeling bird species richness (deviance = 65.8%; RMSE = 3.9 species; n = 1,185 sites) for targeted, morning-only recording periods. Our analyses demonstrate the confounding effects of non-biotic soundscape components on acoustic indices, and we recommend that applications be based on anticipated sonic environments. For instance, in the presence of extensive rain and wind, we suggest using an index minimally affected by geophony. Furthermore, we provide evidence that a measure of biodiversity (bird species richness) is related to the aggregate biotic acoustic activity (biophony). This established relationship adds to recent work that identifies biophony as a reliable and generalizable ecoacoustic measure of biodiversity.
Source: Frontiers in Remote Sensing
Language: English
Subfield: Developmental Biology
Domain: Life Sciences
SDG: Life on land
Open Access status: gold
Estimated APC paid: $2,125
Awards: NATIONAL AND INTERNATIONAL PROGRAMS HAVE AN INCREASING NEED FOR PRECISE AND ACCURATE ESTIMATES OF FOREST CARBON AND STRUCTURE TO SUPPORT GREENHOUSE G, REMOTE SENSING PLAYS A CRITICAL ROLE IN LONG-TERM MONITORING OF BIODIVERSITY ACROSS SPATIAL AND TEMPORAL SCALES AND TECHNOLOGICAL AND ANALYTICAL ADVANCES ARE COMBINING TO BROADEN POTENTIAL APPLICATIONS FOCUSED ON ANIMAL DIVERSITY. IN PARTICULAR LIDAR PROVIDES MEASUREMENTS OF 3D VERTICAL VEGETATION STRUCTURE RELATED TO ANIMAL HABITAT AND ORGANISMAL AND FUNCTIONAL DIVERSITY. IMAGING SPECTROSCOPY CAN DETECT CANOPY FOLIAR CHEMISTRY SPECTRAL-CHEMICAL DIVERSITY RELATED TO PLANT FUNCTIONAL AND FLORISTIC DIVERSITY AND MAP PLANT SPECIES AND LAND COVER VARIABLES RELATED TO ANIMAL HABITAT. THE SYNERGY OF SPACE-BASED IMAGING SPECTROMETER AND LIDAR SENSORS HAS GREAT POTENTIAL FOR GLOBAL BIODIVERSITY ASSESSMENT AND MONITORING LEADING THE 2017-2027 DECADAL SURVEY FOR EARTH SCIENCE AND APPLICATIONS FROM SPACE TO RECOMMEND THESE SENSORS AS HIGH-PRIORITY MISSIONS. SPATIAL MODELS THAT CHARACTERIZE ASPECTS OF BIODIVERSITY SUCH AS SPECIES DISTRIBUTIONS OR RICHNESS FROM REMOTE SENSING PREDICTOR VARIABLES NEED EXTENSIVE AND REPRESENTATIVE IN SITU SPECIES DATA FOR CALIBRATION AND VALIDATION. CITIZEN SCIENCE (CS) WHERE PUBLIC VOLUNTEERS ARE ENLISTED IN SCIENTIFIC INQUIRY HAS BEEN SUCCESSFUL IN COLLECTING LARGE AMOUNTS OF IN SITU SPECIES PRESENCE DATA WHILE ENGAGING THE PUBLIC IN SCIENCE AND ENVIRONMENTAL ISSUES. IN ADDITION NEW LOW-COST TECHNOLOGY FOR AUTOMATED SPECIES IDENTIFICATION THROUGH SOUND RECORDINGS (BIOACOUSTICS) IN COMBINATION WITH CS MAY FURTHER INCREASE SPECIES OBSERVATIONS. SOUNDSCAPES TO LANDSCAPES (S2L) SEEKS TO ADVANCE ANIMAL DIVERSITY MONITORING FROM NEXT-GENERATION AND EXISTING EARTH-OBSERVING SATELLITES. DRIVEN BY CS COMPOSED OF STUDENTS BIRDER WATCHERS AND LANDOWNERS THE PROJECT USES SOUND RECORDINGS FROM FIELD SITES AND A CLOUD-BASED BIOACOUSTICS ANALYSIS PLATFORM TO IDENTIFY BIRD SPECIES BY THEIR CALLS AND THEN PREDICT THEIR PRESENCE AT SITES. WE ENGAGE CS WITH FIELD CAMPAIGNS THAT DEPLOY RECORDERS TO SITES ON PUBLIC AND PRIVATE LANDS THROUGH A SERIES OF ?BIRD BLITZ? WORKSHOPS FOR BIOACOUSTICS WORK AND CALL IDENTIFICATION MODELING FOR MOTIVATED BIRD ENTHUSIASTS. THE S2L SCIENCE TEAM USES CS BIOACOUSTICS-BASED BIRD DATA IN A UNIFIED ANALYTICAL FRAMEWORK TO EXPLORE THE BENEFITS AND TRADE-OFFS IN SPACEBORNE IMAGING SPECTROMETER LIDAR AND EXISTING LAND IMAGERS FOR SPATIAL MODELING OF AVIAN DIVERSITY AND USE OF PRODUCTS IN CONSERVATION PLANNING. THE PROJECT HAS FOUR MAIN GOALS: 1) INVOLVE CITIZEN SCIENTISTS IN THE COLLECTION OF FIELD INFORMATION ON BIRD DIVERSITY THROUGH BIOACOUSTICS 2) ASSESS THE ACCURACY AND PRECISION OF BIOACOUSTICS FOR THE DETECTION AND MONITORING OF INDIVIDUAL SPECIES AND RICHNESS 3) TEST THE POTENTIAL OF SPACEBORNE HYPERSPECTRAL LIDAR SYNTHETIC APERTURE RADAR (SAR) AND MULTISPECTRAL OPTICAL SENSORS FOR SPATIAL MODELING OF SPECIES OCCUPANCY AND OVERALL SPECIES RICHNESS AND 4) USE OCCUPANCY/RICHNESS MAPS TO BETTER UNDERSTAND FACTORS RELATED TO CONSERVING ANIMAL DIVERSITY AND IDENTIFY THOSE AREAS IN THE LANDSCAPE WITH COMPLEMENTARY SOCIETAL VALUE (E.G. CARBON STORAGE). WE FOCUS OUR INVESTIGATION IN SONOMA COUNTY OF NORTHERN CALIFORNIA AN AREA WITH A DIVERSE RANGE OF NATURAL VEGETATION URBAN AND AGRICULTURAL GRADIENTS RECENT FIRE DISTURBANCE AS WELL AS A RICH SUITE OF REMOTE SENSING DATA. THE INVESTIGATION INCLUDES SPACEBORNE LIDAR (GLOBAL ECOSYSTEM DYNAMICS INVESTIGATION LIDAR - GEDI) AND IMAGING SPECTROMETER (HYPERSPECTRAL INFRARED IMAGER - HYSPIRI) SENSORS SIMULATED FROM AIRBORNE SENSORS AND MULTI-SEASONAL SAR AND MULTISPECTRAL OPTICAL SENSORS ON EXISTING SATELLITES (E.G. LANDSAT SENTINELS). WE USE DATA FROM THESE SENSORS ENVIRONMENTAL SPATIAL PREDICTORS BIOACOUSTICS-BASED SPECIES PRESENCE/ABSENCE AND STATISTICAL AND MODELING ANALYSES TO ASSESS THE RELATIVE STRENGTHS WEAKNESSES AND SYNERGIES OF CURRENT AND FUTURE REMOTE SENSING TECHNOLOGY FOR PREDICTING THE SPATIAL DISTRIBUTION OF INDIVIDUAL AVIAN SPECIES AND OVERALL RICHNESS ACROSS A RANGE OF ECOSYSTEMS.
OpenAlex ID: W4376609808