Traditional knowledge for climate change adaptation in Mesoamerica : a systematic review

This research was carried out during the first author's postdoctoral research (CVU number 292956 ) funded by the Consejo Nacional de Ciencia y Tecnologia (CONACYT) Mexico.Indigenous and rural peoples have developed close connections with land and nature for millennia. Traditional and local knowledge resulting from such human-environment interactions is embedded in ethnic, linguistic, and cultural contexts, and may assist local communities in adapting to global issues such as climate change. However, the extent to which traditional knowledge supports adaptation to local manifestations of severe socio-environmental changes, the traditional knowledge techniques that play an effective role in adaptation, and the dynamic yet integral aspect of traditional knowledge for indigenous and mestizo cultures remain unclear. Despite an extensive literature on climate change, adaptation, and traditional knowledge in the Global South, Mesoamerican countries are underrepresented. The aims of this systematic review were to address the main manifestations of climate change in Mesoamerican countries, to critically analyze relationships between traditional knowledge and contemporary climate change adaptation and to make recommendations regarding knowledge conservation, production, and exchange for climate change adaptation in the region. We systematically identified, reviewed, and coded 77 relevant papers. Our results show that: 1) most papers do not distinguish between local, traditional, and indigenous knowledge; 2) rainfall variability, droughts, and weather unpredictability are the most frequently expressed experiences of climate change; 3) the main adaptations undertaken by smallholders are changes to the agricultural calendar and crops cultivated, a shift to more sustainable agriculture, and labour diversification to generate off-farm income; and 4) many more articles are published on Mexico than the other Mesoamerican countries, and predominantly by authors from outside Mesoamerica. Local traditional knowledge makes important contributions to climate change actions and policy by observing changing climates, adapting to impacts, and contributing to global mitigation efforts. As a response to increasing climate change challenges, smallholders create new hybrid knowledge by combining traditional and western perspectives. This knowledge evolution will support greater resilience to climate change but may hasten cultural erosion and exacerbate social inequalities in the region unless efforts are taken to maintain cultural integrity.Publisher PDFPeer reviewe

Indication of a species in an extinction vortex: The ocellated turkey on the Yucatan peninsula, Mexico

The ocellated turkey Meleagris ocellata (OT) is a large, unmistakable endemic bird of the Yucatan peninsula. The species has suffered a considerable loss of distributional area as well as local abundance between 1980 and 2000 and is classified as endangered according to Mexican norms. We applied Classification Trees and Random Forests in order to determine the factors that most closely explain the observed patterns of distribution and abundance loss, and to develop hypotheses that may guide measures for the protection of the OT. Among the most important predictors of change were variables corresponding to aspects of forest cover and variables on human population and small settlements. OT abundance in 1980, however, was by far the most important predictor for OT abundance change. This is an indication that the OT dynamics are governed by internal rather than by external factors. Medium and low abundances in 1980 inevitably led to a further decrease during the following years, which gives rise to the conclusion that the OT might find itself in an extinction vortex. We suggest the following hypothetical scenario for OT decline: migrant people from other Mexican states colonise forested regions in Yucatan; they establish small settlements; bushmeat hunting is important for their survival; the naïve OT is easy prey; huntingdtogether with beginning deforestationdreaches a certain level, and local OT abundance falls below a critical threshold; OT continues declining regardless of current social and environmental changes except where there is total protection of both the species and its habitat.

Classification in conservation biology: A comparison of five machine-learning methods

Classification is one of the most widely applied tasks in ecology. Ecologists have to deal with noisy, highdimensional data that often are non-linear and do not meet the assumptions of conventional statistical procedures. To overcome this problem, machine-learning methods have been adopted as ecological classification methods. We compared five machine-learning based classification techniques (classification trees, random forests, artificial neural networks, support vector machines, and automatically induced rulebased fuzzy models) in a biological conservation context. The study case was that of the ocellated turkey (Meleagris ocellata), a bird endemic to the Yucatan peninsula that has suffered considerable decreases in local abundance and distributional area during the last few decades. On a grid of 10×10 km cells that was superimposed to the peninsula we analysed relationships between environmental and social explanatory variables and ocellated turkey abundance changes between 1980 and 2000. Abundance was expressed in three (decrease, no change, and increase) and 14 more detailed abundance change classes, respectively. Modelling performance varied considerably between methods with random forests and classification trees being the most efficient ones as measured by overall classification error and the normalised mutual information index. Artificial neural networks yielded the worst results along with linear discriminant analysis, which was included as a conventional statistical approach. We not only evaluated classification accuracy but also characteristics such as time effort, classifier comprehensibility and method intricacy—aspects that determine the success of a classification technique among ecologists and conservation biologists as well as for the communication with managers and decision makers. We recommend the combined use of classification trees and random forests due to the easy interpretability of classifiers and the high comprehensibility of the method

Classification in conservation biology: A comparison of five machine-learning methods

Classification is one of the most widely applied tasks in ecology. Ecologists have to deal with noisy, highdimensional data that often are non-linear and do not meet the assumptions of conventional statistical procedures. To overcome this problem, machine-learning methods have been adopted as ecological classification methods. We compared five machine-learning based classification techniques (classification trees, random forests, artificial neural networks, support vector machines, and automatically induced rulebased fuzzy models) in a biological conservation context. The study case was that of the ocellated turkey (Meleagris ocellata), a bird endemic to the Yucatan peninsula that has suffered considerable decreases in local abundance and distributional area during the last few decades. On a grid of 10×10 km cells that was superimposed to the peninsula we analysed relationships between environmental and social explanatory variables and ocellated turkey abundance changes between 1980 and 2000. Abundance was expressed in three (decrease, no change, and increase) and 14 more detailed abundance change classes, respectively. Modelling performance varied considerably between methods with random forests and classification trees being the most efficient ones as measured by overall classification error and the normalised mutual information index. Artificial neural networks yielded the worst results along with linear discriminant analysis, which was included as a conventional statistical approach. We not only evaluated classification accuracy but also characteristics such as time effort, classifier comprehensibility and method intricacy—aspects that determine the success of a classification technique among ecologists and conservation biologists as well as for the communication with managers and decision makers. We recommend the combined use of classification trees and random forests due to the easy interpretability of classifiers and the high comprehensibility of the method.

Competing pressures on populations: Long-term dynamics of food availability, food quality, disease, stress and animal abundance

Despite strong links between sociality and fitness that ultimately affect the size of animal populations, the particular social and ecological factors that lead to endangerment are not well understood. Here, we synthesize approximately 25 years of data and present new analyses that highlight dynamics in forest composition, food availability, the nutritional quality of food, disease, physiological stress and population size of endangered folivorous red colobus monkeys (Procolobus rufomitratus). There is a decline in the quality of leaves 15 and 30 years following two previous studies in an undisturbed area of forest. The consumption of a low-quality diet in one month was associated with higher glucocorticoid levels in the subsequent month and stress levels in groups living in degraded forest fragments where diet was poor was more than twice those in forest groups. In contrast, forest composition has changed and when red colobus food availability was weighted by the protein-to-fibre ratio, which we have shown positively predicts folivore biomass, there was an increase in the availability of high-quality trees. Despite these changing social and ecological factors, the abundance of red colobus has remained stable, possibly through a combination of increasing group size and behavioural flexibility

Fall fertilization of Pinus resinosa seedlings: nutrient uptake, cold hardiness, and morphological development

•  Fall fertilization may increase plant nutrient reserves, yet associated impacts on seedling cold hardiness are relatively unexplored. •  Bareroot red pine (Pinus resinosa Ait.) seedlings in north-central Minnesota, USA were fall fertilized at the end of the first growing season with ammonium nitrate (NH$_{4}$NO$_{3})$ at 0, 11, 22, 44, or 89 kg N ha$^{-1}$. Seedling morphology and cold hardiness [assessed by freeze induced electrolyte leakage (FIEL)] were evaluated six weeks after fertilization and following the second growing season. •  Seedling height and number of needle primordia increased with fertilizer rate for both sampling years. Seedlings fertilized with 44 and 89 kg N ha$^{-1}$ attained target height (15 cm) after the second growing season. Shoot and root N concentration increased after the first growing season in fall fertilized seedlings compared to controls. Fall fertilized seedlings had lower FIEL (i.e., increased cold hardiness) compared to controls when tested at –40 °C after the first growing season, but no significant differences in FIEL of control and fertilized seedlings were observed after the second growing season. •  Results suggest that fall fertilization of red pine seedlings can help render desired target height in the nursery, while maintaining or increasing cold hardiness levels.Fertilisation automnale des plants de Pinus resinosa : absorption des éléments nutritifs, rusticité au froid, et développement morphologique. • La fertilisation automnale peut augmenter les réserves en éléments nutritifs des plants, mais les répercussions sur la rusticité au froid des semis sont encore relativement inexplorées. •  Des plants à racines nues de Pinus resinosa Ait.dans le centre-nord du Minnesota (USA) ont été fertilisés à l'automne à la fin de la première saison de croissance avec du nitrate d'ammonium (NH$_{4}$NO$_{3})$ à 0, 11, 22, 44, ou 89 kg N ha$^{-1}$. La morphologie des plants et la rusticité au froid [estimée par la fuite d'électrolyte (FIEL) induite par le gel] ont été évaluées six semaines après la fertilisation et à la suite dans la deuxième saison de croissance. •  La hauteur des plants et le nombre de primordiums d'aiguilles ont augmenté avec le taux de fertilisation pour les deux années d'échantillonnage. Les plants fertilisés avec 44 et 89 kg N ha$^{-1}$ ont atteint l'objectif de hauteur (15 cm) après la deuxième saison de croissance. La concentration en N des tiges et des racines a augmenté après la première saison de croissance chez les plants fertilisés à l'automne par rapport aux témoins. Les plants fertilisés à l'automne ont eu un plus faible FIEL (c'est-à-dire, une augmentation de rusticité), comparativement aux témoins lors du test à –40 °C après la première saison de croissance, mais aucune différence significative de FIEL entre plants fertilisés et témoins n'a été observée après la deuxième saison de croissance. • Les résultats suggèrent que la fertilisation d'automne des plants de Pinus resinosa Ait. peut aider à obtenir l'objectif de hauteur souhaité dans la pépinière, tout en maintenant ou en augmentant les niveaux de rusticité au froid