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

Resumen:

Texture information from passive remote sensing images provides surrogates for habitat structure, which is relevant for modeling biodiversity across space and time and for developing effective ecological indicators. However, the applicability of this information might differ among taxa and diversity measures. We compared the ability of indicators developed from texture analysis of remotely sensed images to predict species richness and species turnover of six taxa (trees, pyraloid moths, geometrid moths, arctiinae moths, ants, and birds) in a megadiverse Andean mountain rainforest ecosystem. Partial least-squares regression models were fitted using 12 predictors that characterize the habitat and included three topographical metrics derived from a high-resolution digital elevation model and nine texture metrics derived from very high-resolution multi-spectral orthophotos. We calculated image textures derived from mean, correlation, and entropy statistics within a relatively broad moving window (102 m × 102 m) of the near infra-red band and two vegetation indices. The model performances of species richness were taxon dependent, with the lowest predictive power for arctiinae moths (4%) and the highest for ants (78%). Topographical metrics sufficiently modeled species richness of pyraloid moths and ants, while models for species richness of trees, geometrid moths, and birds benefited from texture metrics. When more complexity was added to the model such as additional texture statistics calculated from a smaller moving window (18 m × 18 m), the predictive power for trees and birds increased significantly from 12% to 22% and 13% to 27%, respectively. Gradients of species turnover, assessed by non-metric two-dimensional scaling (NMDS) of Bray-Curtis dissimilarities, allowed the construction of models with far higher predictability than species richness across all taxonomic groups, with predictability for the first response variable of species turnover ranging from 64% (birds) to 98% (trees) of the explained change in species composition, and predictability for the second response variable of species turnover ranging from 33% (trees) to 74% (pyraloid moths). The two NMDS axes effectively separated compositional change along the elevational gradient, explained by a combination of elevation and texture metrics, from more subtle, local changes in habitat structure surrogated by varying combinations of texture metrics. The application of indicators arising from texture analysis of remote sensing images differed among taxa and diversity measures. However, these habitat indicators improved predictions of species diversity measures of most taxa, and therefore, we highly recommend their use in biodiversity research.

Resumen:

Facing the ongoing loss of natural ecosystems, worldwide monitoring of biodiversity across different spatial scales is essential for conservation planning. Remote sensing (RS) has proven to be a cost-efficient tool to access environmental characteristics such as vegetation structure and associated distributions of animal species on a broad scale. Special emphasis is put on birds as indicators for biodiversity owing to their strong species–habitat relationship. So far, bird diversity was modeled ignoring that species–habitat relationships differ among feeding guilds. This is surprising, since habitat preferences strongly depend on diet specialization. Therefore, I investigated RS texture image based vegetation metrics to test whether the predictability of specialized avian feeding guilds including insectivores, frugivores and nectarivores is higher than of the less specialized omnivore guild and overall bird diversity. I used point count data of bird communities among 30 study sites in a complex tropical mountain forest ecosystem in south-eastern Ecuador to estimate (i) Shannon index and (ii) community composition as measures of ?-diversity and combined ?- and ?-diversity, respectively. In order to relate both diversity measures to RS metrics, I compared two high dimensional predictor sets – satellite images and airborne orthophotos – with structural indices derived from a discrete return airborne Lidar sensor. Partial least squares regression was used to unveil the predictive power of all fitted feeding guild models. For the comparability of all models, a sample size correction on species number per guild was applied. Shannon index predictability ranged between 37 % and 65 %; and best predictions were achieved for insectivores using metrics from satellite or Lidar images and nectarivores species using metrics from orthophotos. Community composition was generally better predicted than Shannon index with explained variations from 65 % to 85 %. Frugivore and nectarivore community compositions were best predicted using metrics from orthophotos, whereas the two other sensors best predicted omnivores. For both diversity measures, performance of satellite derived metrics revealed slightly better model results compared to other sensors emphasizing its applicability for the regarded study area. In conclusion, specialized feeding guilds were not consistently better predicted than omnivore or overall bird diversity; rather the study showed that model performances depended on the regarded diversity measure and RS image type. However, insectivores might be the best surrogate for overall diversity with high predictability in all compared models. In addition, the high explanatory power for community composition suggests that the measure should considered in avian diversity modeling for conservation planning.

Resumen:

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 tropicalmountain ecosystem,we conclude that texture information retrieved frommultispectral data of operational satellite systems could replace costly airborne laser-scanning formodeling certain aspects of biodiversity.

Resumen:

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.