Edgar J. González

Predicting Tropical Dry Forest Successional Attributes from Space: Is the Key Hidden in Image Texture?

Biodiversity conservation and ecosystem-service provision will increasingly depend on the existence of secondary vegetation. Our success in achieving these goals will be determined by our ability to accurately estimate the structure and diversity of such communities at broad geographic scales. We examined whether the texture (the spatial variation of the image elements) of very high-resolution satellite imagery can be used for this purpose. In 14 fallows of different ages and one mature forest stand in a seasonally dry tropical forest landscape, we estimated basal area, canopy cover, stem density, species richness, Shannon index, Simpson index, and canopy height. The first six attributes were also estimated for a subset comprising the tallest plants. We calculated 40 texture variables based on the red and the near infrared bands, and EVI and NDVI, and selected the best-fit linear models describing each vegetation attribute based on them. Basal area (R 2 = 0.93), vegetation height and cover (0.89), species richness (0.87), and stand age (0.85) were the best-described attributes by two-variable models. Cross validation showed that these models had a high predictive power, and most estimated vegetation attributes were highly accurate. The success of this simple method (a single image was used and the models were linear and included very few variables) rests on the principle that image texture reflects the internal heterogeneity of successional vegetation at the proper scale. The vegetation attributes best predicted by texture are relevant in the face of two of the gravest threats to biosphere integrity: climate change and biodiversity loss. By providing reliable basal area and fallow-age estimates, image-texture analysis allows for the assessment of carbon sequestration and diversity loss rates. New and exciting research avenues open by simplifying the analysis of the extent and complexity of successional vegetation through the spatial variation of its spectral information.

Identifying the demographic processes relevant for species conservation in human-impacted areas: does the model matter?

The identification of the demographic processes responsible for the decline in population growth rate (λ) in disturbed areas would allow conservation efforts to be efficiently directed. Integral projection models (IPMs) are used for this purpose, but it is unclear whether the conclusions drawn from their analysis are sensitive to how functional structures (the functions that describe how survival, growth and fecundity vary with individual size) are selected. We constructed 12 IPMs that differed in their functional structure by combining two reproduction models and three functional expressions (generalized linear, cubic and additive models), each with and without simplification. Models were parameterized with data from two populations of two endangered cacti subject to different disturbance intensities. For each model, we identified the demographic processes that most affected λ in the presence of disturbance. Simulations were performed on artificial data and analyzed as above to assess the generality of the results. In both empirical and simulated data, the same processes were identified as making the largest contribution to changes in λ regardless of the functional structure. The major differences in the results were due to misspecification of the fecundity functions, whilst functional expression and model simplification had lesser effects. Therefore, as long as the demographic attributes of the species are well known and incorporated into the model, IPMs will robustly identify the processes that most affect the growth of populations subject to disturbance, making them a reliable tool for developing conservation strategies.

Predicting old-growth tropical forest attributes from very high resolution VHR-derived surface metrics

ABSTRACT Old-growth tropical forests are increasingly vanishing worldwide. Although the accurate quantification of tropical old-growth forests attributes is essential to understand, manage, and conserve their high diversity and biomass, conducting this task over large areas and at fine detail is not only expensive and time consuming, but also often practically impossible. This calls for the search for more efficient alternatives, particularly those based on remote sensing. In this study, we evaluate the potential of several surface metrics (tone and texture) extracted from very high resolution (VHR) satellite imagery to model the structural and diversity attributes of a tropical dry forest (TDF) in southern Mexico. We constructed simple linear models that used each forest attribute as dependent variables, and the tone and texture metrics extracted from several bands, the panchromatic (resolution = 0.5 m), red (R), infrared, and two vegetation indices (normalized difference vegetation index (NDVI), enhanced vegetation index (EVI); resolution = 2 m), of a VHR image (GeoEye-1) as predictive variables. The significance of the models including one, two, two and its interaction, and three image metrics was evaluated by comparing them with null models. The structural characteristics of the TDF (basal area (BA), mean height, stem density) showed the highest modelling potential, with the goodness-of-fit (R2) values ranging from 0.58 to 0.66. Conversely, no significant models were obtained for total crown area (TCA) and all diversity attributes. Our results show that remote-sensing metrics detect the spatial variation in the structural attributes of this old-growth TDF better than they detect the variation in its diversity. Our ability to model forest attributes at large scales at fine detail (sampling plots <0.2 ha) can be much improved by combining the use of VHR imagery with an array as wide as possible of the image surface metrics, including both tone and texture.

Using Google Earth Surface Metrics to Predict Plant Species Richness in a Complex Landscape

Google Earth provides a freely available, global mosaic of high-resolution imagery from different sensors that has become popular in environmental and ecological studies. However, such imagery lacks the near-infrared band often used in studying vegetation, thus its potential for estimating vegetation properties remains unclear. In this study, we assess the potential of Google Earth imagery to describe and predict vegetation attributes. Further, we compare it to the potential of SPOT imagery, which has additional spectral information. We measured basal area, vegetation height, crown cover, density of individuals, and species richness in 60 plots in the oak forests of a complex volcanic landscape in central Mexico. We modelled each vegetation attribute as a function of surface metrics derived from Google Earth and SPOT images, and selected the best-supported linear models from each source. Total species richness was the best-described and predicted variable: the best Google Earth-based model explained nearly as much variation in species richness as its SPOT counterpart (R2 = 0.44 and 0.51, respectively). However, Google Earth metrics emerged as poor predictors of all remaining vegetation attributes, whilst SPOT metrics showed potential for predicting vegetation height. We conclude that Google Earth imagery can be used to estimate species richness in complex landscapes. As it is freely available, Google Earth can broaden the use of remote sensing by researchers and managers in low-income tropical countries where most biodiversity hotspots are found.

Competition and facilitation determine dwarf mistletoe infection dynamics

Summary 1.Interspecific interactions have a fundamental role in plant population dynamics, as they may set the conditions for species coexistence. Parasitic plants, like dwarf mistletoes, offer the opportunity to study competition for resources that are different from those consumed by most plants, allowing for a better understanding of the interaction. 2.We explored how interspecific interactions between two dwarf mistletoe species (Arceuthobium), co-infecting the same host species (even sharing the same individual tree of Pinus hartwegii) affect their infection dynamics at two different stages of population development (colonization of new hosts and subsequent growth), and if heterogeneity in resource availability (host density and size structure) affects these interactions. For that purpose, we integrated these processes into a spatially-explicit model of density-dependent population growth. 3.We found that self-regulation (density-dependence) was strong for both species; however the intensity and sign of interspecific interactions changed depending on host size and demographic process. Population growth in A. globosum was reduced by competition, except for smaller hosts where A. globosum growth was facilitated by A. vaginatum. A. vaginatum was facilitated by A. globosum regardless of host size. Colonization of new hosts by A. globosum was enhanced by previous infection by the other species, showing intraguild facilitation. 4.Demographic importance of interactions depended on stand structure: in homogeneous, low-density forests, facilitation predominates, increasing the population sizes of both species, whereas the opposite occurs in heterogeneous and dense forests. Both species achieved stable coexistence, fulfilling the invasibility criterion because each mistletoe species can invade a forest that is already infected by the other species. 5.Synthesis. Despite the fundamentally different mechanisms underlying the interactions between mistletoes compared with non-parasitic plants, our results reveal that their behaviour at the population level is similar. Stabilizing mechanisms, like strong self-limiting population growth, allow dwarf mistletoe coexistence. Interactions shift as populations develop, and they depend largely on environmental factors such as forest structure. Intraguild mutualism is shown as a relevant process for colonization of new spaces, highlighting the complexity of competitive/facilitative interactions between parasitic plants, a formerly unexplored subject. Interactions can only be fully understood when integrating all their components at the population level. Analysing these interactions may contribute to the understanding of plant-plant interactions in general, and convey interesting implications for forest management. This article is protected by copyright. All rights reserved.