Model-Assisted Bird Monitoring Based on Remotely Sensed Ecosystem Functioning and Atlas Data

Abstract

Urgent action needs to be taken to halt global biodiversity crisis. To be effective in the implementation of such action, managers and policy-makers need updated information on the status and trends of biodiversity. Here, we test the ability of remotely sensed ecosystem functioning attributes (EFAs) to predict the distribution of 73 bird species with different life-history traits. We run ensemble species distribution models (SDMs) trained with bird atlas data and 12 EFAs describing different dimensions of carbon cycle and surface energy balance. Our ensemble SDMs—exclusively based on EFAs—hold a high predictive capacity across 71 target species (up to 0.94 and 0.79 of Area Under the ROC curve and true skill statistic (TSS)). Our results showed the life-history traits did not significantly affect SDM performance. Overall, minimum Enhanced Vegetation Index (EVI) and maximum Albedo values (descriptors of primary productivity and energy balance) were the most important predictors across our bird community. Our approach leverages the existing atlas data and provides an alternative method to monitor inter-annual bird habitat dynamics from space in the absence of long-term biodiversity monitoring schemes. This study illustrates the great potential that satellite remote sensing can contribute to the Aichi Biodiversity Targets and to the Essential Biodiversity Variables framework (EBV class “Species distribution”).

Combining Citizen Science Data and Satellite Descriptors of Ecosystem Functioning to Monitor the Abundance of a Migratory Bird during the Non-Breeding Season

“…Our results allow us to verify that the abundance of Woodcock in autumn migration and winter periods is affected by several dimensions linked to primary productivity dynamics, the water cycle (water in the soil and vegetation), and ecosystems' energy balance. These results are consistent with those obtained in previous studies, highlighting the validity of these remotely sensed indicators for species distribution and abundance assessments [12,17,22,23,88,94,95]. Several studies have already demonstrated the advantages of using models calibrated with functional variables over those based on models calibrated with exclusively climatic variables since the former allowed for capture of the joint effect of changes in climate and land cover/use on the availability of habitat for a broad group of threatened species [22,95].…”

“…An example of such satellite products is the ecosystem functioning attributes (EFAs), which are biophysical descriptors of ecosystem functioning that describe exchanges of matter and energy between the biota and the environment [12,22]. EFAs are calculated from satellite recorded time series and offer a more integrated and faster assessment of ecosystem responses to environmental factors and changes than macroclimatic databases or structural attributes (e.g., vegetation height and density, landscape composition, or spatial configuration) [23]. Additionally, since EFAs are remotely detected in a standardized and synoptical fashion, species habitats' spatial and temporal (seasonal and interannual) variability can be easily included in SAM or SDM workflows [22].…”

A Framework for Multi-Dimensional Assessment of Wildfire Disturbance Severity from Remotely Sensed Ecosystem Functioning Attributes

“…Multi-dimensional approaches to wildfire disturbances using EFA-like metrics are, to our knowledge, scarce (e.g., [94]). Very few studies have adopted a multi-dimensional approach using EFAs, regardless of the specific application (e.g., [48,50,95]). More studies should be conducted in the future, with diverse contexts in terms of baseline environmental conditions and fire disturbance regimes, to further test and improve the proposed approach.…”

“…In recent decades, Ecosystem Functioning Attributes (EFAs) derived from satellite image time-series (SITS) have been increasingly used in a wide range of ecological applications due to their strong relation to biophysical properties and processes of ecosystems [5]. These include wildfire-related applications, such as improving the detection of wildfire disturbances in space and time [44], but also a wide range of other ecological applications, such as describing major ecological patterns [45][46][47], predicting and projecting species distributions [48][49][50], and supporting the definition of conservation priorities [51]. More specifically, EFAs extracted from SITS can provide information on interannual quantity (e.g., mean, minimum, maximum), seasonality (i.e., seasonal range or variability), and timing (e.g., dates of specific moments) components over multiple dimensions of ecosystem functioning.…”

“…These include frequently used satellite-derived descriptors of the intra-annual dynamics of carbon gains, such as primary productivity, vegetation seasonality, and phenology (e.g., [46,52,53]). However, analogous measures can also be extracted from RS-derived proxy descriptors related to other dimensions of ecosystem functioning, such as water content, albedo, and sensible heat (e.g., [50,54]). By combining these four complementary dimensions of ecosystem functioning across each component (i.e., quantity, seasonality, and timing), remotely sensed EFAs can enable indepth and integrative characterizations of fire severity [3,44].…”