Forest phenoclusters for Argentina based on vegetation phenology and climate.

Forest biodiversity conservation and species distribution modeling greatly benefit from broad-scale forest maps depicting tree species or forest types rather than just presence and absence of forest, or coarse classifications. Ideally, such maps would stem from satellite image classification based on abundant field data for both model training and accuracy assessments, but such field data do not exist in many parts of the globe. However, different forest types and tree species differ in their vegetation phenology, offering an opportunity to map and characterize forests based on the seasonal dynamic of vegetation indices and auxiliary data. Our goal was to map and characterize forests based on both land surface phenology and climate patterns, defined here as forest phenoclusters. We applied our methodology in Argentina (2.8 million km2 ), which has a wide variety of forests, from rainforests to cold-temperate forests. We calculated phenology measures after fitting a harmonic curve of the enhanced vegetation index (EVI) time series derived from 30-m Sentinel 2 and Landsat 8 data from 2018-2019. For climate, we calculated land surface temperature (LST) from Band 10 of the thermal infrared sensor (TIRS) of Landsat 8, and precipitation from Worldclim (BIO12). We performed stratified X-means cluster classifications followed by hierarchical clustering. The resulting clusters separated well into 54 forest phenoclusters with unique combinations of vegetation phenology and climate characteristics. The EVI 90th percentile was more important than our climate and other phenology measures in providing separability among different forest phenoclusters. Our results highlight the potential of combining remotely sensed phenology measures and climate data to improve broad-scale forest mapping for different management and conservation goals, capturing functional rather than structural or compositional characteristics between and within tree species. Our approach results in classifications that go beyond simple forest-nonforest in areas where the lack of detailed ecological field data precludes tree species-level classifications, yet conservation needs are high. Our map of forest phenoclusters is a valuable tool for the assessment of natural resources, and the management of the environment at scales relevant for conservation actions.

Multi-taxon biodiversity assessment of Southern Patagonia: Supporting conservation strategies at different landscapes.

In the last years, different spatial analyses were developed to support multi-taxon biodiversity conservation strategies. In fact, the use of species distribution models as input allowed to create spatial decision-support maps. Of special interest are maps of potential biodiversity (MPB), which define distribution and ecological requirements of relevant species and maps of priority conservation areas (MPCA), which define priority areas considering endemism and richness. The objective of this paper was to assess multi-taxon biodiversity based on two different spatial analyses and to test their efficiency to support conservation decision at Patagonia. We computed 119 potential habitat suitability maps (one deer, birds, lizards, darkling-beetles, plants) with ENFA (Environmental Niche Factor Analysis) and 15 environmental variables, using Biomapper software. ENFA calculate two ecological indexes (marginality and specialization) which describe the narrowness of species niches and how extreme are the optimum environmental conditions related to the whole study area. These maps were combined obtaining a MPB and MPCA using Zonation software. Multivariate analyses were performed to compare methodologies, analysing environmental variables, ecological areas, forest types and protected areas. Multivariate and ecological indexes showed that deer, lizards and darkling-beetles presented a narrow range, while birds and plants presented a large range of marginality and specialization mainly related to vegetation and climate. At provincial level, highest potential biodiversity and conservation priority values were related to shrublands and humid steppes. However, MPCA showed higher values related to forests and alpine vegetation due to endemism, while MPB showed differences among forest types. These analyses showed that the most valuable areas were not represented in the protected areas, however, many higher conservation priority values were found inside the protected compared with unprotected areas. Different spatial decision-support maps presented similar outputs at provincial scale, but differed in the forest landscape matrix. Both methodologies can be used to plan conservation strategies depending on the specific objectives (e.g. highlighting richness or endemism).