Conservation assessment of the Peruvian Andes and Amazon based on mapped forest functional diversity

Asner, Gregory P.; Martin, Robert E.; Tupayachi, Raul; Llactayo, William

doi:10.1016/j.biocon.2017.04.008

Cited by 16 publications

(14 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Remote sensing data provide spatially and temporally continuous information useful for biodiversity assessments [5][6][7]. Therefore, combining existing biological field data with remote sensing is the method of choice to obtain an overview and to make spatial predictions of various biological patterns over large areas [5][6][7][8][9][10][11][12][13][14]. This can significantly improve the information needed for land use planning, reduce knowledge gaps, and help to identify priority areas for the conservation of Amazonian biodiversity.…”

Section: Introductionmentioning

confidence: 99%

Mapping Floristic Patterns of Trees in Peruvian Amazonia Using Remote Sensing and Machine Learning

et al. 2020

View full text Add to dashboard Cite

Recognition of the spatial variation in tree species composition is a necessary precondition for wise management and conservation of forests. In the Peruvian Amazonia, this goal is not yet achieved mostly because adequate species inventory data has been lacking. The recently started Peruvian national forest inventory (INFFS) is expected to change the situation. Here, we analyzed genus-level variation, summarized through non-metric multidimensional scaling (NMDS), in a set of 157 INFFS inventory plots in lowland to low mountain rain forests (<2000 m above sea level) using Landsat satellite imagery and climatic, edaphic, and elevation data as predictor variables. Genus-level floristic patterns have earlier been found to be indicative of species-level patterns. In correlation tests, the floristic variation of tree genera was most strongly related to Landsat variables and secondly to climatic variables. We used random forest regression, under varying criteria of feature selection and cross-validation, to predict the floristic composition on the basis of Landsat and environmental data. The best model explained >60% of the variation along NMDS axes 1 and 2 and 40% of the variation along NMDS axis 3. We used this model to predict the three NMDS dimensions at a 450-m resolution over all of the Peruvian Amazonia and classified the pixels into 10 floristic classes using k-means classification. An indicator analysis identified statistically significant indicator genera for 8 out of the 10 classes. The results are congruent with earlier studies, suggesting that the approach is robust and can be applied to other tropical regions, which is useful for reducing research gaps and for identifying suitable areas for conservation.

show abstract

Section: Introductionmentioning

confidence: 99%

Mapping Floristic Patterns of Trees in Peruvian Amazonia Using Remote Sensing and Machine Learning

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Forecasting the two major components of functional composition, functional trait means (FM), and functional diversity (FD) (Villéger, Mason, & Mouillot, ), is therefore of central interest. Insights into geographic variation in the contemporary functional composition of plant communities (Violle, Reich, Pacala, Enquist, & Kattge, ) comes from field surveys (Asner et al, ; Baraloto et al, ; De Bello, Lepš, & Sebastià, ), macroecological approaches (Campbell & McAndrews, ; Lamanna et al, ; Šímová et al, ; Swenson et al, ), and remote sensing approaches (Asner, Martin, Knapp, et al, ; Asner, Martin, Tupayachi, & Llactayo, ; Jetz et al, ). However, little is known about changes in these functional trait patterns over longer time scales (Blonder, Royer, Johnson, Miller, & Enquist, ; Polly et al, ; Thuiller et al, ).…”

Section: Introductionmentioning

confidence: 99%

Late Quaternary climate legacies in contemporary plant functional composition

Blonder

Enquist

Graae

et al. 2018

Global Change Biology

View full text Add to dashboard Cite

The functional composition of plant communities is commonly thought to be determined by contemporary climate. However, if rates of climate-driven immigration and/or exclusion of species are slow, then contemporary functional composition may be explained by paleoclimate as well as by contemporary climate. We tested this idea by coupling contemporary maps of plant functional trait composition across North and South America to paleoclimate means and temporal variation in temperature and precipitation from the Last Interglacial (120 ka) to the present. Paleoclimate predictors strongly improved prediction of contemporary functional composition compared to contemporary climate predictors, with a stronger influence of temperature in North America (especially during periods of ice melting) and of precipitation in South America (across all times). Thus, climate from tens of thousands of years ago influences contemporary functional composition via slow assemblage dynamics.

show abstract

“…Збезлісення і деградація лісів, в поєднанні зі зміною клімату, вимагають картування поліпшення лісів функціонального і біологічного різноманіття для підтримки більш тактичних рішень та дій по збереженню природного біорізноманіття лісів. Авторами (Gregory et al, 2017) була зроблена карта, для збереження типологічного різноманіття лісів для Анд і Амазонії, в період швидких змін навколишнього природного середовища. Проведені нами дослідження ставили за мету проведення типологічного аналізу лісів на лівому і правому березі річки Сіверський Донець на території Луганської області.…”

Section: типологічне різноманіття лісів на водозборіunclassified

Типологічне Різноманіття Лісів На Водозборі Річки Сіверський Донець На Території Луганської Області

et al. 2017

View full text Add to dashboard Cite

Typological analysis of watershed forests of the Seversky Donets River was carried out on the basis of forest management data with electronic databases of "Ukrderzhlisproekt" Production Association. More than 27 types of forest were analyzed with total size of 228.2 thousand hectares. The classification of forest growth conditions, forest types and tree species on the left and right banks of the Seversky Donets River was done for the Luhansk region. We determined that the forest fund of the Luhansk region consists from the oak forests -46.4 %, sudubrava -21.1 %, conifer forest -17.5 %. The proportion of coniferous forests makes up to 15.0 % of total forest area. We calculated that the area of common oak and common pine is 86.9 and 70.9·10 3 ha respectively. The following types of forests are prevailing: dry birchbark-marple sudubrava (30.8 %), fresh oak-pine sudubrava (13.1%), dry Tatarian maple sudubrava (12.9%), and fresh pine forest (10.6%). In the forest area of the Luhansk region we registered 70 tree species, of which 19 species -in oak-pine forest; 37 -in conifer forest; 55 -in sudubrava; 59 -in oak forest. By categories of protection the recreation forests (51.7 %) and protective forests (42.0 %) are the dominant. The forests of nature protection, forests of scientific, historical and cultural significance have only 6.4 % from the total forest area. The structure of forests by age groups was: middle-aged forests -41.3 %, young growths -17.8 %, ripening forests -15.5 %, mature forests -16.7 %, and old growth forests -13.3 % from total forest area of Luhansk region.

show abstract

Conservation assessment of the Peruvian Andes and Amazon based on mapped forest functional diversity

Cited by 16 publications

References 22 publications

Mapping Floristic Patterns of Trees in Peruvian Amazonia Using Remote Sensing and Machine Learning

Mapping Floristic Patterns of Trees in Peruvian Amazonia Using Remote Sensing and Machine Learning

Late Quaternary climate legacies in contemporary plant functional composition

Типологічне Різноманіття Лісів На Водозборі Річки Сіверський Донець На Території Луганської Області

Contact Info

Product

Resources

About