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Using individual-based demographic modelling to estimate the impacts of anthropogenic mortality on territorial predators

Publication . Marques, Ana Teresa; Crispim-Mendes, Tiago; Palma, Luís; Pita, Ricardo; Moreira, Francisco; Beja, Pedro

Wildlife anthropogenic mortality is increasing worldwide, yet there is limited understanding regarding its population-level impacts. Territorial species stand out in this context, as they possess distinctive characteristics that are often overlooked but may significantly affect their vulnerability. In particular, population impacts may depend on the level and spatial distribution of additional mortality risk across territories, and on the extent to which exposure to increased mortality varies across life stages (i.e., territorial and non-territorial individuals). In this study, we developed an Individual-Based Model (IBM) to explore these issues, using the Bonelli’s eagle (Aquila fasciata) and electrocution in powerline pylons as a model system. We used declines in annual population growth rates as a proxy for negative impacts, and conducted simulations to estimate the relative impacts of different levels of mortality risk, the spatial pattern of such risks, and the risk exposure of different life stages. Population-level impacts greatly increased with the mortality risks simulated, and they were lower when exposure to mortality risks was concentrated versus spread across territories. Impacts were highest when both territorial and non-territorial individuals were exposed to anthropogenic mortality risks, and they were higher when such exposure only affected non-territorial versus territorial individuals. Our results underscore that each breeding territory should be considered as a unit, where all existing pylons should be intervened whenever mitigation actions are put in place. Results also highlight the importance of considering both the territorial and non-territorial fractions of the population to prevent and mitigate the impacts of increased mortality. More generally, our study illustrates the value of IBM frameworks such as ours to explore population-level impacts resulting from anthropogenic mortality in territorial species, and to inform the development of conservation strategies to mitigate such impacts.

2024-07Journal article

Open access

GEE_xtract: High-quality remote sensing data preparation and extraction for multiple spatio-temporal ecological scaling

Publication . Valerio, Francesco; Godinho, Sérgio; Marques, Ana T.; Crispim-Mendes, Tiago; Pita, Ricardo; Silva, João Paulo

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.

2024-05Journal article

Open access

High‑resolution species distribution modelling reveals spatio‑temporal variability of habitat suitability in a declining grassland bird

Publication . Crispim‑Mendes, Tiago; Valerio, Francesco; Marques, Ana Teresa; Pita, Ricardo; Godinho, Sérgio; Silva, João Paulo

Context Species distribution models (SDMs) may provide accurate predictions of species occurrence across space and time, being critical for effective con- servation planning. Objectives Focusing on the little bustard (Tetrax tetrax), an endangered grassland bird, we aimed to: (i) characterise the drivers of the species distribution along its key phenological phases (winter, breeding, and post-breeding); and (ii) quantify spatio-temporal variation in habitat suitability across phenological phases and over the years 2005–2021. Methods Combining remotely sensed metrics at high temporal resolution (MODIS) with long-term (> 12 years) GPS telemetry data collected for 91 individuals at one of the species’ main strongholds within the Iberian Peninsula, we built SDMs (250 m resolution) for the species key phenological phases. Results The use of both dynamic and static pre- dictors unveiled previously unknown ecological responses by little bustards, revealing a marked change in the spatial distribution of suitable habitat among phenological phases. Long-term habitat suita- bility trends showed considerable fluctuations, mainly in the breeding and post-breeding phases. Overall, SDM projections into the past revealed that while the species’ winter and post-breeding habitats appar- ently increased since 2005, suitable habitat during the species’ most critical phenological phase, breeding, apparently reduced in area over time. Conclusions Our findings show that matching remotely sensed data with GPS tracking data results in accurate habitat suitability predictions throughout the yearly cycle. Additionally, our findings stress the importance of quantifying habitat loss and its poten- tial impact on little bustard decline over nearly 20 years. Spatio-temporal variations in habitat suitability are also identified in this work, which can help pri- oritize conservation areas, particularly the breeding areas that have remained stable over time, as this is a key requirement for little bustard lek breeding system.

2024-02Journal article

Open access