The annual deforestation rate of 0.8% observed in the study area for the period 1988−2018 is slightly higher than the annual average of 0.5% observed for the Cerrado between 1985 and 2017 (Alencar et al., 2020). If the period 2008–2018 is considered, the average annual deforestation rate in the area is 1.5%. This increase may be related to agricultural expansion for soy, rice, and cotton production in MATOPIBA, which may explain the expansion of agricultural areas in the study area (Alencar et al., 2020; Colli et al., 2020; Grande et al., 2020). Charcoal production may also have contributed to the increase in deforestation rates. The three municipalities are among the eight that concentrated 32% of the total charcoal production from native vegetation in the Tocantins State between 2009 and 2016, whose main consumer was the steel industry in the Minas Gerais State (Cachoeira et al., 2019).
The Weights of Evidence analysis identified a significant correlation between deforestation with shrublands and grasslands, which is possibly linked to a greater concentration of these phytophysiognomies in the region (Alencar et al., 2020). As we used only two classes (Natural and Anthropic) in the analysis, it was not possible to identify whether agricultural expansion resulted from the replacement of degraded areas or new deforestations. Subsequent studies can identify which pattern has been occurring in this and other regions of MATOPIBA.
New deforestation is related to the proximity to converted areas, access roads, and rivers, as well as to low slopes and clayey soils and Latosols (greater agricultural suitability) and also Planosols in Natividade municipality. The use of Planosols − poorly drained and hydromorphic soils − may be related to planted pastures or rice production (Manzatto et al., 2002). LR showed a repelling effect on deforestation, indicating their potential for protecting vegetation and biodiversity. However, these data should not be extrapolated, as there has been some degree of deforestation in these areas and the compliance with the law varies across the country (Pacheco et al., 2021; Vieira et al., 2018).
Despite the possible predominance of natural vegetation until 2038, a gradual process of vegetation loss and fragmentation is in a course. The continuous decrease of the IIC and EC values suggests a decrease in connectivity, especially from 2008. However, the observed and projected values can still be considered high, and, beyond the great natural cover, may be linked to a low density of patches and proximity between fragments, characteristics of landscapes with a strong presence of livestock (Carvalho et al., 2009; Grande et al., 2020). Considering the LPI and IIC deltas values and the close values of the relative variation in EC (dEC = ECt1-ECt0/ECt0) and the relative variation in the amount of native area in the landscape (dA = At1-At0/At0), the fragmentation may be related to the clearing of adjacent vegetation from a single large fragment (Grande et al., 2020).
If deforestation rates and trends are maintained until 2038, the fragmentation threshold identified by Grande et al. (2020) for the Cerrado as 40% of the original vegetation would not be reached. This is a breakpoint in which functional connectivity is drastically reduced and the survival of some species can be compromised. Below this threshold, additional vegetation losses have little impact on the quite low functional connectivity and the configuration of the remaining habitat becomes more important than its total amount (Grande et al., 2020; Saura and Pascual-Hortal, 2007; Villard and Metzger, 2014). However, economic and social factors and changes in the national environmental policy can result in higher rates of deforestation and fragmentation and more critical effects on biodiversity (Carvalho et al., 2019; Metzger et al., 2019).
There are, therefore, opportunities to plan the use and occupation of the landscape to ensure minimum vegetation cover and connectivity. Besides the encouragement of sustainable productive activities, CRA issuance and trade could be stimulated and supported in medium to very high importance areas in Natividade and Chapada da Natividade, enhancing environmental protection and also generating income for owners with vegetation surpluses (May et al., 2015; Soares-Filho et al., 2016). Conservation units could be established in the center-south and northeast of the study area, where there is high vegetation cover and a low probability of deforestation. Areas unsuitable for production, PPAs, and LR totally or partially preserved would play a significant role in maintaining connectivity (Grande et al., 2020; Metzger et al., 2019).
The methodology for prioritizing areas for conservation is satisfactory for territorial planning at the regional and local levels and can be adopted as a later stage of prioritization analysis carried out on a national scale (Fonseca and Venticinque, 2018). By using free and user-friendly data and software and a simple decision-making process, it is affordable for any state environmental agency (Oakleaf et al., 2017) and can be easily replicated and adapted to any region or biome. The combination of medium resolution data (30 m) and the IIC, a robust connectivity index suitable for conservation planning, allows the identification of remnants that may guarantee the maintenance of critical habitats and landscape connectivity (Castro et al., 2020; Saura and Pascual-Hortal, 2007).
The use of Jenks break to define classes for IIC deltas values seems to be more adequate than breaks with arbitrary values since they can vary according to the characteristics of the analyzed landscape. The inclusion of gap-species occurrence data can avoid the elimination of areas important for these species. However, considering the limitations in knowledge and data on the occurrence of species in Brazil (Veiga et al., 2017), the use of the IIC considering the patch area may be effective to balance the prioritization process, valuing intrapatch connectivity, and benefiting small-sized species or those with low dispersal capacity (Castro et al., 2020; Grande et al., 2020; Saura and Pascual-Hortal, 2007).
The CAR data, despite not covering the entire landholding network and, as self-declaratory information, require validation and topological corrections before their use (Santos et al., 2021), have been proved to be strategic for territorial planning. As they allow locating PPA, LR, and vegetation surpluses, vegetation recovery or compensation may be better oriented to reach greater environmental gains. The issuance of CRA close to PPA and LR may ensure greater protection of ecosystem services, connectivity, and a greater amount of available protected habitat, benefiting groups of native species (Metzger et al., 2019; Tubelis et al., 2004).
The inclusion of deforestation modeling allows for identifying where and how strong the deforestation pressure is. This can favor a more appropriate use of resources available for conservation, as it is possible to avoid the selection of areas that may be deforested in the short term - more suitable for production and more difficult to preserve - and of areas with "zero environmental additionality” – which are those passively protected by their productive ineptitude, environmental sensitivity or distance from consumer markets (May et al., 2015; Soares-Filho et al., 2016, 2014). Due to its absence in the study area, protected areas were not included in the deforestation model and multicriteria analysis. However, it is recommended to add conservation units, indigenous lands, and military areas in these analyzes when present, due to their role in protecting native vegetation and biodiversity (Osis et al., 2019; Paiva et al., 2015; Silva Arimoro et al., 2017).