Ecosystem Functioning Influences Species Fitness at Upper Trophic Levels

Abstract

Global change is severely affecting ecosystem functioning and biodiversity globally. Remotely sensed ecosystem functional attributes (EFAs) are integrative descriptors of the environmental change—being closely related to the processes directly affecting food chains via trophic cascades. Here we tested if EFAs can explain the species fitness at upper trophic levels. We took advantage of a long-term time series database of the reproductive success of the Golden Eagle (Aquila chrysaetos)—an apex predator at the upper trophic level—over a 17-year period across a bioclimatic gradient (NW Spain; c. 29,575 km2). We computed a comprehensive database of EFAs from three MODIS satellite-products related to the carbon cycle, heat dynamics and radiative balance. We also assessed possible time-lag in the response of the Golden Eagle to fire, a critical disruptor of the surface energy budget in our region. We explored the role of EFAs on the fitness of the Golden Eagle with logistic-exposure nest survival models. Our models showed that the reproductive performance of the Golden Eagle is influenced by spatiotemporal variations in land surface temperature, albedo and vegetation productivity (AUC values from 0.71 to 0.8; ΣWi EFAs from 0.66 to 1). Fire disturbance also affected ecological fitness of this apex predator—with a limited effect at 3 years after fire (a time-lagged response to surface energy budget disruptions; ΣWi Fire = 0.62). Our study provides evidence for the influence of the matter and energy fluxes between land surface and atmosphere on the reproductive success of species at upper trophic levels.

Complex changes in climatic suitability for Cassin’s Sparrow ( Peucaea cassinii ) revealed by retrospective ecological niche modeling

“…In one time series, we use 30 M2 variables selected to reflect key attributes of the microclimate that are known to relate directly or indirectly to the biological or ecological functioning of a species. This collection of predictors is intended to help us understand the influence of climate-related conditions in localized areas near the Earth's surface and includes variables, such as temperature near and on the ground, humidity, wind speed, cloud cover, soil moisture, evapotranspiration, vegetation coverage, and the incoming and reflected solar radiation that provides energy to drive ecosystem functioning at the micro scale (Schnase et al 1991, Cabello et al 2012, Bosilovich et al 2016, Gelaro et al 2017, Arenas-Castro et al 2018, Pettorelli et al 2018, Regos et al 2022a, 2022b. In the second time series, we use 19 M2-derived bioclimatic variables modeled after the classic bioclim predictors https://journal.afonet.org/vol95/iss1/art9/ commonly used in ENM (O'Donnell and Ignizio 2012).…”

Biology Learning Content Analysis on Aves Diversity: A Case Study at Kaduberureum Biodiversity Park’s PT Chandra Asri Petrochemical Tbk, Indonesia

“…Eagles are predators that have a vital role as controllers in the ecosystem. The existence of predatory birds in an ecosystem is very important because of their position as the top predator in the food chain pyramid (Regos et al, 2022), and as balance consumers in the food chain.…”

EVI and NDVI as proxies for multifaceted avian diversity in urban areas

“…The quality of images is adequate for the evaluation of various vegetation aspects such as canopy phenology, seasonal changes in the leaf area, and gross primary production (Liu et al, 2011; Muraoka et al, 2013; Turner et al, 2005), as well as the floristic composition, vegetation height, and structure, vitality and age (Lausch et al, 2016). So, the use of remote sensing tools largely improved the ability to monitor biodiversity and ecosystem functioning at large scales providing useful information on the species distribution, reproductive fitness (Regos et al, 2021), and population abundance (Arenas‐Castro et al, 2019) when facing spatial and temporal changes (Lausch et al, 2016). Among many vegetation indices, Normalized Difference Vegetation Index (NDVI hereafter) and Landsat‐derived Enhanced Vegetation Index (EVI hereafter) are the most commonly used to obtain vegetation information (Huete, Didan, Miura, & Rodriguez, 2002; Mildrexler et al, 2009; Peckham et al, 2008).…”