The use of species distribution models to predict the spatial distribution of deforestation in the western Brazilian Amazon

Souza, Rodrigo Antônio de; Marco, Paulo De

doi:10.1016/j.ecolmodel.2014.07.007

Cited by 31 publications

(14 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Deforestation prior to 2008 was derived from the Deforestation Monitoring Program (PRODES) of the National Institute for Space Research (INPE; www.obt.inpe.br/prodes). An analysis of this model showed that it provided lower omission rates than other models used to predict future deforestation in Amazon (De Souza and De Marco 2014). The predictions of this model are expected to hold as long as conditions remain unchanged, especially for the main drivers, such as road distribution.…”

Section: Deforestation Modelingmentioning

confidence: 90%

“…We use the recently derived predictions for future deforestation in Amazon based on a MaxEnt approach (De Souza and De Marco 2014). The model used as predictive variables previous deforestation, distance to roads, presence of agriculture, indigenous reserves, urban areas, existence of embargoes related to unlawful deforestation actions, and land reform settlements.…”

Section: Deforestation Modelingmentioning

confidence: 99%

See 1 more Smart Citation

Modeling the distribution of a rare Amazonian odonate in relation to future deforestation

et al. 2015

Self Cite

View full text Add to dashboard Cite

Section: Deforestation Modelingmentioning

confidence: 90%

Section: Deforestation Modelingmentioning

confidence: 99%

Modeling the distribution of a rare Amazonian odonate in relation to future deforestation

et al. 2015

Self Cite

View full text Add to dashboard Cite

“…SDM could be a quicker and less expensive tool to support in situ and ex situ plant conservation practices for endangered and rare species in comparison with methods that require costly and time-consuming field surveys (floristic and phytosociological studies) in regions affected by hydropower dam reservoirs (Guarino et al 2012). Also, SDM has been a valuable tool for supporting efforts to conserve tropical forests such as determining potential areas of future Amazon deforestation (Souza and De Marco 2014). Hence, as shown in this study, SDM can be effectively used as a predictive tool in the assessment of human impacts on rare tree species in tropical forests, including deforestation and artificial flooding together.…”

Section: Discussionmentioning

confidence: 99%

Species distribution models of rare tree species as an evaluation tool for synergistic human impacts in the Amazon rainforest

et al. 2017

View full text Add to dashboard Cite

In the present work, we have considered the vulnerability of some rare tree species to human disturbances in a high biodiversity tropical region. In this context, we aimed to evaluate the combined effect of deforestation and artificial flooding of the large Jirau hydroelectric reservoir on potential distribution areas of 13 locally rare tree species in the southwestern Brazilian Amazon. We performed species distribution modeling (SDM) by using the environmental distance algorithm. Based on these models, we found new sites and subsequently applied rapid ecological assessment to collect further species occurrence data. Additional SDMs were carried out using MaxEnt to determine the potential distribution areas of these rare species. We found that artificial flooding and deforestation caused combined losses of potential distribution areas of rare tree species between 8 and 39% of the total area. The most vulnerable species were Semaphyllanthe megistocaula (K. Krause) L. Andersson (Rubiaceae) (39%), Chrysophyllum colombianum (Aubrév.) T.D. Penn. (Sapotaceae) (34%), Lacunaria jenmanii (Oliv.) Ducke (Quiinaceae) (32%), Brosimum parinarioides Ducke (Moraceae) (32%) and Xylopia benthamii R.E. Fr. (Annonaceae) (30%). These results indicate an additive effect of human disturbances such that artificial flooding, when combined with deforestation, has an overall effect by orders of magnitude. SDMs can be effectively used as a predictive tool in the assessment of human impacts on rare tree species in tropical forests. The results also showed different vulnerability among the rare species, and these results may indicate that some species are more seriously threatened by the extreme loss of potential distribution areas.

show abstract

“…The LUCC literature focused on the Brazilian Amazon and Cerrado includes a range of different methods to simulate future scenarios, e.g., based on econometric analyses, Markov-chain matrices, dynamic systems, cellular automata, and agent-based approaches [24][25][26][27]. A relatively new approach to model LUCC relies on the Maximum Entropy Principle [28][29][30], extensively adopted for the identification of species niches in ecological studies and presence-only models [31,32]. This approach can be used for the identification of forest areas likely to experience conversion to alternative land uses given a set of environmental and socio-economic restrictions (e.g., [33]).…”

Section: Introductionmentioning

confidence: 99%

Simulated Impacts of Soy and Infrastructure Expansion in the Brazilian Amazon: A Maximum Entropy Approach

Frey

West

Hickler

et al. 2018

Forests

View full text Add to dashboard Cite

Historically, the expansion of soy plantations has been a major driver of land-use/cover change (LUCC) in Brazil. While a series of recent public actions and supply-chain commitments reportedly curbed the replacement of forests by soy, the expansion of the agricultural commodity still poses a considerable threat to the Amazonian and Cerrado biomes. Identification of areas under high risk of soy expansion is thus paramount to assist conservation efforts in the region. We mapped the areas suitable for undergoing transition to soy plantations in the Legal Amazon with a machine-learning approach adopted from the ecological modeling literature. Simulated soy expansion for the year 2014 exhibited favorable validation scores compared to other LUCC models. We then used our model to simulate how potential future infrastructure improvements would affect the 2014 probabilities of soy occurrence in the region. In addition to the 2.3 Mha of planted soy in the Legal Amazon in 2014, our model identified another 14.7 Mha with high probability of soy conversion in the region given the infrastructure conditions at that time. Out of those, pastures and forests represented 9.8 and 0.4 Mha, respectively. Under the new infrastructure scenarios simulated, the Legal Amazonian area under high risk of soy conversion increased by up to 2.1 Mha (14.6%). These changes led to up to 11.4 and 51.4% increases in the high-risk of conversion areas of pastures and forests, respectively. If conversion occurs in the identified high-risk areas, at least 4.8 Pg of CO2 could be released into the atmosphere, a value that represents 10 times the total CO2 emissions of Brazil in 2014. Our results highlight the importance of targeting conservation policies and enforcement actions, including the Soy Moratorium, to mitigate future forest cover loss associated with infrastructure improvements in the region.

show abstract

The use of species distribution models to predict the spatial distribution of deforestation in the western Brazilian Amazon

Cited by 31 publications

References 29 publications

Modeling the distribution of a rare Amazonian odonate in relation to future deforestation

Modeling the distribution of a rare Amazonian odonate in relation to future deforestation

Species distribution models of rare tree species as an evaluation tool for synergistic human impacts in the Amazon rainforest

Simulated Impacts of Soy and Infrastructure Expansion in the Brazilian Amazon: A Maximum Entropy Approach

Contact Info

Product

Resources

About