Lab
Steppe Birds MOVE
About the lab
Our group is within CIBIO/ BIOPOLIS/ University of Porto. We study the ecology of endangered steppe birds in Iberia, a hotspot for steppe bird diversity currently vulnerable to environmental and climate change. Most of our research combines GPS tracking data from tagged birds with high-resolution environmental and climatic data from remote sensing, to provide an in-depth understanding into a wide range of ecological processes. Overall, we aim to give insight into the functioning of these unique and important ecosystems.
Our main areas of research include:
• Habitat use and selection
• Migration and seasonal movements
• Impacts of infrastructures
• Reproduction and breeding success
Our main areas of research include:
• Habitat use and selection
• Migration and seasonal movements
• Impacts of infrastructures
• Reproduction and breeding success
Featured research (32)
Careful planning for renewable infrastructure is necessary to mitigate significant impacts on animal wildlife, especially when focusing on preventing mortality, habitat loss, and barrier effects. Importantly, evaluating the cumulative effects arising from multiple projects, including powerlines and renewables, is crucial for assessing the overall impacts comprehensively.
Previous methods addressing ecological aspects relevant for impact assessment, such as animal mortality risk and habitat loss, faced limitations including inadequate temporal and spatial resolution and susceptibility to successive bias arising from correction factors. With the advent of the technological revolution, movement ecology has emerged as a critically important field in science. The increased affordability of technology has facilitated the acquisition of larger datasets, more representative of populations, thereby enhancing our understanding of animal ecology and behaviour.
With this symposium, we will explore how movement ecology has been applied to address pressing issues regarding energy projects. We will demonstrate its use in enhancing the understanding of the effects of these infrastructures, such as estimating mortality caused by collisions with power lines and the avoidance behaviour next to wind turbines. Additionally, we will highlight its role in supporting mitigation efforts, including the development of high-resolution collision risk maps to predict areas with high hazard risk.
Inland freshwaters are essential in maintaining ecological balance and supporting human development. However, comprehensive water data cataloguing remains insufficient, especially for small water bodies (i.e., ponds), which are overlooked despite their ecological importance. To address this gap, remote sensing has emerged as a possible solution for understanding ecohydrological characteristics of water bodies, particularly in water-stressed areas. Here, we propose a novel framework based on a Sentinel-1&2 local surface water (SLSW) model targeting very small (<0.5 ha, Mdn ≈ 0.031 ha) and seasonal water bodies. We tested this framework in three semiarid regions in SW Iberia, subjected to distinct seasonality and bioclimatic changes. Surface water attributes, including surface water occurrence and extent, were modelled using a Random Forests classifier, and SLSW time series forecasts were generated from 2020 to 2021. Model reliability was first verified through comparative data completeness analyses with the established Landsat-based global surface water (LGSW) model, considering both intra-annual and inter-annual variations. Further, the performance of the SLSW and LGSW models was compared by examining their correlations for specific periods (dry and wet seasons) and against a validation dataset. The SLSW model demonstrated satisfactory results in detecting surface water occurrence (μ ≈ 72 %), and provided far greater completeness and reconstructed seasonality patterns than the LGSW model. Additionally, SLSW model exhibited a stronger correlation with LGSW during wet seasons (R2 = 0.38) than dry seasons (R2 = 0.05), and aligned more closely with the validation dataset (R2 = 0.66) compared to the LGSW model (R2 = 0.24). These findings underscore the SLSW model’s potential to effectively capture surface characteristics of very small and seasonal water bodies, which are challenging to map over broad regions and often beyond the capabilities of conventional global products. Also, given the vulnerability of water resources in semiarid regions to climate fluctuations, the present framework offers advantages for the local reconstruction of continuous, high-resolution time series, useful for identifying surface water trends and anomalies. This information has the potential to better guide regional water management and policy in support of Sustainable Development Goals, focusing on ecosystem resilience and water sustainability.
Background
Modern biologging technologies allow researchers to gain a better understanding of animal movements, offering opportunities to measure survival and remotely study the breeding success of wild birds, i.e., by locating nests. This is particularly useful for species whose nests are difficult to find or access, or when disturbances can impact the breeding outcome. We developed and validated, with field data, a framework to detect nesting events by two sandgrouse species, the black-bellied (Pterocles orientalis) and pin-tailed sandgrouse (Pterocles alchata), using GPS and Overall Dynamic Body Acceleration (ODBA) data. Sandgrouses are ground-nesting, cryptic, and elusive birds with biparental incubation efforts. Because both sexes take turns to incubate, a novel framework considering when tagged individuals are on incubation duty or not needs to be designed to detect nests.
Results
We tagged 52 birds with high-resolution GPS devices to monitor their breeding during 2021–24. Using remote tracking and field data from the first 2 years (2021–22), we first determined sex-specific time windows for incubation to maximise differentiation between incubation and non-incubation behaviours. We then used a threshold-based classification to identify incubation days and inferred the minimum number of successive incubation days needed to correctly identify a nesting event. We show how ODBA and GPS data can be used to successfully detect nests incubated for only 2 or 3 days. GPS-only data or combined GPS-ODBA data had a success rate of around 95%, whereas ODBA-only data had a success rate of 100%. Cross-validation using data from 2023 to 2024 confirmed the model’s performance, showing an overall success > 90% for GPS-only and ODBA-only data and of 85% for combined GPS–ODBA data.
Conclusions
By accurately identifying nesting events, our framework offers new opportunities to study the breeding of conservation-dependent species. Besides its applicability to ground-nesting species with biparental care and sex-specific incubation schedules, the framework can be adapted to other bird species sensitive to disturbances or with inaccessible nesting sites. By doing so, it reduces the need for nest visits and associated disturbances, as well as the carbon footprint and expenses associated with fieldwork.
Environmental sensing via Earth Observation Satellites (EOS) is critically important for understanding Earth’ biosphere. The last decade witnessed a “Klondike Gold Rush” era for ecological research given a growing multidisciplinary interest in EOS. Presently, the combination of repositories of remotely sensed big data, with cloud infrastructures granting exceptional analytical power, may now mark the emergence of a new paradigm in understanding spatio-temporal dynamics of ecological systems, by allowing appropriate scaling of environmental data to ecological phenomena at an unprecedented level.
However, while some efforts have been made to combine remotely sensed data with (near) ground ecological observations, virtually no study has focused on multiple spatial and temporal scales over long time series, and on integrating different EOS sensors. Furthermore, there is still a lack of applications offering flexible approaches to deal with the scaling limits of multiple sensors, while ensuring high-quality data extraction at high resolution.
We present GEE_xtract, an original EOS-based (Sentinel-2, Landsat, and MODIS) code operational within Google Earth Engine (GEE) to allow for straightforward preparation and extraction of remote sensing data matching the multiple spatio-temporal scales at which ecological processes occur. The GEE_xtract code consists of three main customisable operations: (1) time series imageries filtering and calibration; (2) calculation of comparable metrics across EOS sensors; (3) scaling of spatio-temporal remote sensing time series data from ground-based data.
We illustrate the value of GEE_xtract with a complex case concerning the seasonal distribution of a threatened elusive bird and highlight its broad application to a myriad of ecological phenomena. Being user-friendly designed and implemented in a widely used cloud platform (GEE), we believe our approach provides a major contribution to effectively extracting high-quality data that can be quickly computed for metrics time series, converted at any scale, and extracted from ground information. Additionally, the framework was prepared to facilitate comparative research initiatives and data-fusion approaches in ecological research.