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Global sustainability rests on a myriad of benefits provided by natural ecosystems that support human livelihoods and well-being, from biodiversity persistence to climate regulation. The undeniable importance of conserving tropical forests has drawn most of the conservation spotlight towards it. However, open ecosystems such as the Brazilian Campo Rupestre (rupestrian grassland), have been historically overlooked despite their high diversity and key associated ecosystem services. We highlight major current threats to the persistence of the Campo Rupestre emphasizing its ecological, social, cultural, geo-environmental, and economic importance. We call attention to the importance of the Campo Rupestre as a reservoir of biodiversity and ecosystem services and offer priority actions that resulted from discussions involving scientists, industry representatives, environmental managers, and other members of civil society. Proposed actions include efforts related to ecological restoration, sustainable ecotourism, protection of traditional ecological knowledge, identification of emerging research questions, and development of tailored public policies. Such issues are integrated into a framework that collectively represents a road map to safeguard the Campo Rupestre from further degradation and steer its historical overexploitation towards sustainable management. Safeguarding the future of non-forest biomes like this poses a challenge to current paradigms of nature conservation. By establishing priorities and guidelines, we propose an actionable plan, which we hope can support informed decision-making policy towards a sustainable use of the Campo Rupestre.

Premise Increasingly, researchers studying plant phenology are exploring novel technologies to remotely observe plant changes over time. The increasing use of phenocams to monitor leaf phenology, based on the analysis of indices extracted from sequences of daily digital vegetation images, has demanded the development of appropriate tools for data visualization and analysis. Here, we describe RadialPheno, a tool that uses radial layouts to represent time series from digital repeat photographs, and applies them to the analysis of leafing patterns and leaf exchange strategies of different vegetations. Methods and Results We developed a web tool, RadialPheno, provided with the R and Shiny environments, which uses radial visual structures to represent cyclical multidimensional temporal data associated with digital image time series. We demonstrate the application of our methods and tool for a savanna vegetation phenology in the Brazilian Cerrado. We visually represented the greenness index extracted from sequential imagery using the RadialPheno tool. Conclusions RadialPheno was successfully applied for the visualization and interpretation of individual, species, and community long‐term leafing phenology data associated with near‐surface phenological observations of Cerrado vegetation. RadialPheno was also effective for intercomparisons of ground‐based direct visual observations and camera‐derived phenology observations.

As comunidades arbóreas de florestas tropicais localizadas em regiões com clima sazonal tendem a ajustar a fenologia da queda de folhas e da produção de flores e frutos à sazonalidade climática. Neste estudo monitoramos a comunidade arbórea de uma floresta semidecidual no período de quatro anos (2001 a 2004), a fim de analisar a relação entre o número de espécies perdendo folhas, florescendo e frutificando e a variação anual do comprimento do dia, precipitação e temperatura durante o período de observação. Além disso, avaliamos se essas três fenofases repetem os mesmos padrões em anos sucessivos. Regressões lineares simples indicaram que o número de espécies perdendo folhas foi negativamente relacionado com as três variáveis ambientais para os quatro anos de estudo, sendo o comprimento do dia e a temperatura os melhores preditores para esta fenofase. Houve também relação significativa do comprimento do dia com o número de espécies florescendo e frutificando nos quatro anos, porém, precipitação e temperatura não foram relacionadas com estas fenofases em todos os anos de estudo. Os testes de estatística circular indicaram forte sazonalidade para queda foliar, com data média durante o mês de julho independente do ano, ao passo que para floração e frutificação não foi detectada sazonalidade significativa. Como resultado, o número de espécies perdendo folhas foi correlacionado entre todos os anos sucessivos. Entretanto, não houve correlação do número de espécies florescendo e frutificando em 2003 e 2004, provavelmente devido às altas temperaturas ocorridas neste último ano. Os resultados deste estudo sugerem que a queda de folhas é uma fenofase altamente previsível temporalmente. Ao contrário, a floração e a frutificação podem variar ano a ano como conseqüência de anomalias climáticas. Essas variações podem ser relevantes para o entendimento dos efeitos em longo prazo de mudanças climáticas sobre as florestas sazonais.Tropical tree communities in regions with seasonal climate usually adjust leaf-fall, flowering and fruiting phenology to climate seasonality. Here we monitored a tree community over a 4-year period in order to assess the relationship between the number of species presenting leaf fall, flower and fruit and annual variation in day length, precipitation and temperature. Moreover, we asked whether the three phenological variables have the same patterns in successive years. Simple regression analyses showed that the number of species shedding leaves was negatively related to the three environmental variables during the four years, with day length and temperature as the best predictors. There was also a relationship between day length and the number of species flowering and fruiting, while rainfall and temperature were only related to these phenophases in some years. The circular statistics indicate highly significant seasonality only for leaf fall, whereas for flowering and fruiting significant seasonal patterns were not detected. The number of species in leaf fall was correlated between all successive years. However, the number of species in flower and fruit was not significantly correlated between 2003 and 2004. Overall, our results suggest that leaf fall is highly predictable over time. On the contrary, flowering and fruiting may present year-to-year variation as a result of climatic anomalies such as the high temperatures detected in 2004. These variations may be relevant to understand the effects of climate change on seasonal forests in long-term studies.

Background and aims – Plant phenology, defined as the timing of recurring life events like leaf flushing, flowering, or fruiting, is highly sensitive to environmental factors such as photoperiod, temperature, and moisture. Phenological synchrony between interacting species – such as plants and their pollinators – is of major importance to the structure and functioning of ecosystems. Plant phenology might also be affected by changes in edaphic conditions. However, whether former agricultural activities may shift phenological patterns of plant communities remains poorly understood. In this study, we evaluated the impact of past agricultural practices on herbaceous plant community phenology in the protected Mediterranean xeric grassland of La Crau (France). Material and methods – We compared (1) species composition, and (2) phenological patterns of annuals, perennials, Bromus rubens (annual), and Lobularia maritima (perennial), in formerly-cultivated plots – abandoned for 30 years – and intact native grassland plots (steppe), both subjected to itinerant sheep grazing. Key results and conclusion – Our results suggest that former agricultural activities can affect species composition of Mediterranean xeric grassland communities with differences visible after 30 years of abandonment, but only altered phenological patterns slightly. We suggest that climatic factors and sheep grazing acted as strong habitat filters constraining community assembly at the phenological level.

One of the challenges in remote phenology studies lies in how to efficiently manage large volumes of data obtained as long-term sequences of high-resolution images. A promising approach is known as image foveation, which is able to reduce the computational resources used (i.e., memory storage) in several applications. In this paper, we propose an image foveation approach towards plant phenology tracking where relevant changes within an image time series guide the creation of foveal models used to resample unseen images. By doing so, images are taken to a space-variant domain where regions vary in resolution according to their contextual relevance for the application. We performed our validation on a dataset of vegetation image sequences previously used in plant phenology studies.