Environmental changes in biodiversity hotspot ecosystems of
South Ecuador: RESPonse and feedback effECTs (FOR2730)

Abstract:

Biodiversity monitoring is a global need due to environmental degradation and climate change. Birds are often monitored indicators for environmental health because they are easy assessable, fast reacting species, which supply important ecosystem services. But conventional biodiversity monitoring can be timeconsuming, invasive and it often needs trained specialists, thus appropriate alternatives, especially for largescale assessments, like acoustic monitoring are needed. While numerous acoustic indices were generated no studies occur about their ability to reflect conventional alpha-diversity measures such as species richness, abundance, diversity or evenness under complex conditions like a gradient of elevation and degradation. We choose bird communities along an elevation gradient in natural and degraded rainforests in a highdiverse region in the Ecuadorian Andes to investigate the association among five conventional (total abundance, species richness, Shannon Index, evenness, Gini Index) and three acoustic (Acoustic Diversity Index [ADI], Bioacoustic Index [BI], Acoustic Evenness Index [AEI]) alpha-diversity measures. Furthermore, we investigated the influence of higher resolutions (FFT window size, frequency band size) on the acoustic indices. Further, we tested a new acoustic beta-diversity measure. With help of multivariate analyzes we found acoustic diversity measures were not significantly correlated with conventional diversity measures in high-diverse rainforests. The acoustic indices were correlated between another. The values of the acoustic indices increased with higher resolutions and were strongly positively correlated with their next higher resolution. The NMDS of the conventionally assessed bird communities resulted an elevation gradient and a distinct separation between degraded and natural forest communities. The NMDS of the acoustically assessed bird communities resulted no distinct gradients. The conventional and acoustic alpha-diversity measures were not significantly associated with the elevation or habitat type. ADI, BI and AEI seem not appropriate to reflect conventional diversity measures in high-diverse ecosystems, but BI can reflect avian abundance in less diverse ecosystems. Avian acoustic activity was driven by noisy species and not by species richness or abundance. Higher resolutions of the acoustic indices did not resolute acoustic activity more exactly in this case, thus for communities where birds with long or complex calls (relevant for BI) or narrow frequency ranges (relevant for ADI/AEI) are not dominating the acoustic activity the default resolutions of the acoustic indices seem sufficient. Acoustic beta-diversity assessment seems problematic because the analyzes of frequencies does not reflect species turnover among communities due to the reason that several species can occupy the same frequencies with their calls.

Abstract:

Ecosystems worldwide are threatened by the increasing impact of land use and climate change. To protect their diversity and functionality, spatially explicit monitoring systems are needed. In remote areas, monitoring is difficult and recurrent field surveys are costly. By using Lidar or themore cost-effective and repetitive optical satellite data, remote sensing could provide proxies for habitat structure supporting measures for the conservation of biodiversity. Here we compared the explanatory power of both, airborne Lidar and optical satellite data in modeling the spatial distribution of biodiversity of birds across a complex tropical mountain forest ecosystem in southeastern Ecuador. Weused data fromfield surveys of birds and chose three measures as proxies for different aspects of diversity: (i) Shannon diversity as a measure of ?-diversity that also includes the relative abundance of species, (ii) phylodiversity as a first proxy for functional diversity, and (iii) community composition as a proxy for combined ?- and ?-diversity.We modeled these diversity estimates using partial least-square regression of Lidar and optical texturemetrics separately and compared themodels using a leave-one-out validated R2 and rootmean square error. Bird community informationwas best predicted by both remote sensing datasets, followed by Shannon diversity and phylodiversity. Our findings reveal a high potential of optical texture metrics for predicting Shannon diversity and ameasure of community composition, but not for modeling phylodiversity. Generalizing from the investigated tropical mountain ecosystem, we conclude that texture information retrieved frommultispectral data of operational satellite systems could replace costly airborne laser-scanning formodeling certain aspects of biodiversity.