Selecting priority areas for the conservation of endemic trees species and their ecosystems in Madagascar considering both conservation value and vulnerability to human pressure

Abstract

Madagascar is one of the most biodiverse countries in Africa, due to its level of endemism and species diversity. However, the pressure of human activities threatens the last patches of natural vegetation in the country and conservation decisions are undertaken with limited data availability.In this study, we use free online datasets to generate distribution models of 1,539 endemic trees and prioritise for conservation and restoration considering threat, alongside conservation value and cost. Threats considered include illegal logging, forest degradation and agriculture or slash and burns activities. We found that the areas with the highest potential concentration of species are along the north and south-east of the country where more than 400 tree species can be found. Most scenarios identify a common conservation and restoration priority area along the north east of the country. Our findings guide managers, conservation organizations or governments in decisions about where to invest their limited conservation resources.

O uso de modelagem de distribuição de espécies para restauração florestal: Uma revisão sistemática

“…Foi feita a leitura a fim de selecionar àqueles que avaliavam o uso da modelagem de distribuição de espécies na restauração florestal, sendo selecionados 6 artigos (Tabela 1). O intervalo de publicações para os 6 artigos selecionados (Tabela 1) foi de 7 anos com o primeiro artigo publicado em 2013 (Gastón & García-Viñas, 2013) e o último em 2020 (Carrasco, Price, Tulloch & Mills, 2020) tendo como média de publicação 1,2 artigo por ano. Destes o artigo mais citado foi aquele desenvolvido no México (Gelviz-Gelvez, Pavn, Illoldi-Rangel & Ballesteros-Barrera, 2014), seguido dos que foram desenvolvidos na Espanha (Gastón & García-Viñas, 2013;Gaston et al 2014) conforme descrito na Tabela 1.…”

“…Os protocolos de modelagem envolvem a utilização de ferramentas de Sistemas de Informações Geográficas (SIGs), softwares e algoritmos de modelagem (Cancian, 2012). Neste sentido, para ajustarem os modelos de distribuição de espécies, 50% (n = 3) dos artigos selecionados utilizaram o software MaxEnt (Gelviz-Gelvez et al 2014;Heringer et al 2019;Carrasco et al 2020) por ser baseado no princípio da máxima entropia (Phillips et al 2006), a regressão logística penalizada (Harrell, 2001) foi a escolha de 33,3% dos artigos (Gastón & García-Viñas, 2013;Gaston et al 2014) e os modelos aditivos generalizados (GAMs) foi a opção de somente um (16,67%) dos artigos (Cai, Wang, Wang & Maclachlan, 2019).…”

The Diverse Assemblage of Fungal Endophytes from Orchids in Madagascar Linked to Abiotic Factors and Seasonality

“…Considering that 80% of Madagascar's natural vegetation has been cleared or permanently altered [2] due to illegal mining and other anthropogenic activities [3], documenting the nutrients (N, P, K) present in orchid substrata was another focus of our study. This is especially true in the Central Highlands, where the controlled burning of tapia forests and grasslands can have a profound negative impact on vegetation [39] and presumably soil nutrients.…”

“…More than 80% of Madagascar's natural vegetation has been cleared or permanently altered [2] due to illegal mining and other anthropogenic activities [3]. Despite that region's rugged terrain, many orchids in the Central Highlands are exposed to several threats.…”

Software for prioritizing conservation actions based on probabilistic information

“…It is one of several different developments of the Marxan software. Marxan with Probability is able to explicitly consider four types of spatial uncertainties: 1) Probability that a feature exists in a site (Marxan with Species Probability), estimated with species distribution models, a spatially explicit population model, or habitat mapping accuracy (Akçakaya et al 2004;Franklin & Miller 2009;Peterson et al 2011;Tulloch et al 2013); 2) Probability that features in a site are destroyed in the future due to a threatening process (Marxan with Threat Probability), such as climate change (Parmesan 2006;Powers et al 2016), or future anthropogenic activities such as resource extraction (Witt & Hammill 2018); 3) Probability that the feature does not exist at a site in the future due to natural successional processes (Marxan with Species or Threat Probability), such as a fire or flood disturbance (Drechsler et al 2009); 4) Probability that the feature exists but is degraded by threatening processes and cannot contribute towards conservation goals (Marxan with Species or Threat Probability), such as overfished or polluted marine ecosystems (Chung et al 2019;Klein et al 2013;Tulloch et al 2016), or logged forest or otherwise degraded land ecosystems (Carrasco et al 2020).…”