SPATIAL SCALE EFFECTS OF SAMPLING ON THE INTERPOLATION OF SPECIES DISTRIBUTION MODELS IN THE SOUTHWESTERN AMAZON

ABSTRACT Knowledge of the geographical distribution of timber tree species in the Amazon is still scarce. This is especially true at the local level, thereby limiting natural resource management actions. Forest inventories are key sources of information on the occurrence of such species. However, areas with approved forest management plans are mostly located near access roads and the main industrial centers. The present study aimed to assess the spatial scale effects of forest inventories used as sources of occurrence data in the interpolation of potential species distribution models. The occurrence data of a group of six forest tree species were divided into four geographical areas during the modeling process. Several sampling schemes were then tested applying the maximum entropy algorithm, using the following predictor variables: elevation, slope, exposure, normalized difference vegetation index (NDVI) and height above the nearest drainage (HAND). The results revealed that using occurrence data from only one geographical area with unique environmental characteristics increased both model overfitting to input data and omission error rates. The use of a diagonal systematic sampling scheme and lower threshold values led to improved model performance. Forest inventories may be used to predict areas with a high probability of species occurrence, provided they are located in forest management plan regions representative of the environmental range of the model projection area.

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Some refinements on species distribution models using tree-level National Forest Inventories for supporting forest management and marginal forest population detection

iForest - Biogeosciences and Forestry ◽

10.3832/ifor2441-011 ◽

2018 ◽

Vol 11 (2) ◽

pp. 291-299 ◽

Cited By ~ 14

Author(s):

M Marchi ◽

F Ducci

Keyword(s):

Forest Management ◽

Species Distribution ◽

Species Distribution Models ◽

Tree Level ◽

National Forest ◽

Distribution Models ◽

Forest Inventories ◽

National Forest Inventories ◽

Marginal Forest

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Do species distribution models explain spatial structure within tree species ranges?

Global Ecology and Biogeography ◽

10.1111/j.1466-8238.2009.00478.x ◽

2009 ◽

Vol 18 (6) ◽

pp. 662-673 ◽

Cited By ~ 21

Author(s):

Daniel Montoya ◽

Drew W. Purves ◽

Itziar R. Urbieta ◽

Miguel A. Zavala

Keyword(s):

Spatial Structure ◽

Species Distribution ◽

Tree Species ◽

Species Distribution Models ◽

Distribution Models ◽

Species Ranges

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Spatial species distribution models: Using Bayes inference with INLA and SPDE to improve the tree species choice for important European tree species

Forest Ecology and Management ◽

10.1016/j.foreco.2021.119983 ◽

2022 ◽

Vol 507 ◽

pp. 119983

Author(s):

Markus Engel ◽

Tobias Mette ◽

Wolfgang Falk

Keyword(s):

Species Distribution ◽

Tree Species ◽

Species Distribution Models ◽

Distribution Models ◽

Bayes Inference

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Use of Anecdotal Occurrence Data in Species Distribution Models: An Example Based on the White-Nosed Coati (Nasua narica) in the American Southwest

Animals ◽

10.3390/ani3020327 ◽

2013 ◽

Vol 3 (2) ◽

pp. 327-348 ◽

Cited By ~ 6

Author(s):

Jennifer Frey ◽

Jeremy Lewis ◽

Rachel Guy ◽

James Stuart

Keyword(s):

Species Distribution ◽

Species Distribution Models ◽

American Southwest ◽

Distribution Models ◽

Nasua Narica ◽

Occurrence Data

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Hotspots of change in major tree species under climate warming

10.5194/egusphere-egu2020-22325 ◽

2020 ◽

Author(s):

Flurin Babst ◽

Richard L. Peters ◽

Rafel O. Wüest ◽

Margaret E.K. Evans ◽

Ulf Büntgen ◽

...

Keyword(s):

Climate Change ◽

Climate Sensitivity ◽

Tree Growth ◽

Species Distribution ◽

Tree Species ◽

Habitat Suitability ◽

Species Distribution Models ◽

Sensitivity Index ◽

Distribution Models ◽

Bioclimatic Variables

Warming alters the variability and trajectories of tree growth around the world by intensifying or alleviating energy and water limitation. This insight from regional to global-scale research emphasizes the susceptibility of forest ecosystems and resources to climate change. However, globally-derived trends are not necessarily meaningful for local nature conservation or management considerations, if they lack specific information on present or prospective tree species. This is particularly the case towards the edge of their distribution, where shifts in growth trajectories may be imminent or already occurring.Importantly, the geographic and bioclimatic space (or &#8220;niche&#8221;) occupied by a tree species is not only constrained by climate, but often reflects biotic pressure such as competition for resources with other species. This aspect is underrepresented in many species distribution models that define the niche as a climatic envelope, which is then allowed to shift in response to changes in ambient conditions. Hence, distinguishing climatic from competitive niche boundaries becomes a central challenge to identifying areas where tree species are most susceptible to climate change.Here we employ a novel concept to characterize each position within a species&#8217; bioclimatic niche based on two criteria: a climate sensitivity index (CSI) and a habitat suitability index (HSI). The CSI is derived from step-wise multiple linear regression models that explain variability in annual radial tree growth as a function of monthly climate anomalies. The HSI is based on an ensemble of five species distribution models calculated from a combination of observed species occurrences and twenty-five bioclimatic variables. We calculated these two indices for 11 major tree species across the Northern Hemisphere.The combination of climate sensitivity and habitat suitability indicated hotspots of change, where tree growth is mainly limited by competition (low HSI and low CSI), as well as areas that are particularly sensitive to climate variability (low HSI and high CSI). In the former, we expect that forest management geared towards adjusting the competitive balance between several candidate species will be most effective under changing environmental conditions. In the latter areas, selecting particularly drought-tolerant accessions of a given species may reduce forest susceptibility to the predicted warming and drying.

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Do joint species distribution models reliably detect interspecific interactions from co-occurrence data in homogenous environments?

Ecography ◽

10.1111/ecog.03315 ◽

2018 ◽

Vol 41 (11) ◽

pp. 1812-1819 ◽

Cited By ~ 30

Author(s):

Damaris Zurell ◽

Laura J. Pollock ◽

Wilfried Thuiller

Keyword(s):

Species Distribution ◽

Species Distribution Models ◽

Interspecific Interactions ◽

Distribution Models ◽

Occurrence Data

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Impact of spatial configuration of training data on the performance of Amazonian tree species distribution models

Forest Ecology and Management ◽

10.1016/j.foreco.2021.119838 ◽

2022 ◽

Vol 504 ◽

pp. 119838

Author(s):

Pablo Pérez Chaves ◽

Kalle Ruokolainen ◽

Jasper Van doninck ◽

Hanna Tuomisto

Keyword(s):

Species Distribution ◽

Tree Species ◽

Species Distribution Models ◽

Spatial Configuration ◽

Training Data ◽

Distribution Models

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Exploring snake occurrence records: Spatial biases and marginal gains from accessible social media

PeerJ ◽

10.7717/peerj.8059 ◽

2019 ◽

Vol 7 ◽

pp. e8059 ◽

Cited By ~ 2

Author(s):

Benjamin M. Marshall ◽

Colin T. Strine

Keyword(s):

Social Media ◽

North America ◽

South America ◽

Species Distribution ◽

Species Distribution Models ◽

Conservation Status ◽

Model Performance ◽

Distribution Models ◽

Occurrence Data ◽

Occurrence Records

A species’ distribution provides fundamental information on: climatic niche, biogeography, and conservation status. Species distribution models often use occurrence records from biodiversity databases, subject to spatial and taxonomic biases. Deficiencies in occurrence data can lead to incomplete species distribution estimates. We can incorporate other data sources to supplement occurrence datasets. The general public is creating (via GPS-enabled cameras to photograph wildlife) incidental occurrence records that may present an opportunity to improve species distribution models. We investigated (1) occurrence data of a cryptic group of animals: non-marine snakes, in a biodiversity database (Global Biodiversity Information Facility (GBIF)) and determined (2) whether incidental occurrence records extracted from geo-tagged social media images (Flickr) could improve distribution models for 18 tropical snake species. We provide R code to search for and extract data from images using Flickr’s API. We show the biodiversity database’s 302,386 records disproportionately originate from North America, Europe and Oceania (250,063, 82.7%), with substantial gaps in tropical areas that host the highest snake diversity. North America, Europe and Oceania averaged several hundred records per species; whereas Asia, Africa and South America averaged less than 35 per species. Occurrence density showed similar patterns; Asia, Africa and South America have roughly ten-fold fewer records per 100 km2than other regions. Social media provided 44,687 potential records. However, including them in distribution models only marginally impacted niche estimations; niche overlap indices were consistently over 0.9. Similarly, we show negligible differences in Maxent model performance between models trained using GBIF-only and Flickr-supplemented datasets. Model performance appeared dependent on species, rather than number of occurrences or training dataset. We suggest that for tropical snakes, accessible social media currently fails to deliver appreciable benefits for estimating species distributions; but due to the variation between species and the rapid growth in social media data, may still be worth considering in future contexts.

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