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Concept: Baja California peninsula



The Baja California peninsula is the second longest, most geographically isolated peninsula on Earth. Its physiography and the presence of many surrounding islands has facilitated studies of the underlying patterns and drivers of genetic structuring for a wide spectrum of organisms. Chaetodipus spinatus is endemic to the region and occurs on 12 associated islands, including 10 in the Gulf of California and two in the Pacific Ocean. This distribution makes it a model species for evaluating natural historical barriers. We test hypotheses associated with the relationship between the range of the species, patterns in other species, and its relationship to Pleistocene-Holocene climatic changes. We analyzed sequence data from mtDNA genes encoding cytochrome b (Cytb) and cytochrome c oxidase subunits I (COI) and III (COIII) in 26 populations including all 12 islands. The matrilineal genealogy, statistical parsimony network and Bayesian skyline plot indicated an origin of C. spinatus in the southern part of the peninsula. Our analyses detected several differences from the common pattern of peninsular animals: no mid-peninsula break exists, Isla Carmen hosts the most divergent population, the population on an ancient southern Midriff island does not differ from peninsular populations, and a mtDNA peninsular discordance occurs near Loreto.

Concepts: Biology, Pacific Ocean, Australia, Mexico, Baja California peninsula, Baja California Sur, Gulf of California, Baja California


Environmental governance is more effective when the scales of ecological processes are well matched with the human institutions charged with managing human-environment interactions. The social-ecological systems (SESs) framework provides guidance on how to assess the social and ecological dimensions that contribute to sustainable resource use and management, but rarely if ever has been operationalized for multiple localities in a spatially explicit, quantitative manner. Here, we use the case of small-scale fisheries in Baja California Sur, Mexico, to identify distinct SES regions and test key aspects of coupled SESs theory. Regions that exhibit greater potential for social-ecological sustainability in one dimension do not necessarily exhibit it in others, highlighting the importance of integrative, coupled system analyses when implementing spatial planning and other ecosystem-based strategies.

Concepts: Scientific method, Dimension, Management, Space, Baja California peninsula, Baja California Sur, Gulf of California, Baja California


Small-scale fisheries are an important source of food and livelihoods to coastal communities around the world. Understanding the seasonality of fisheries catch and composition is crucial to fisheries management, particularly in the context of changing environmental and socioeconomic conditions. While seasonal variability directly impacts the lives of fishers, most fisheries studies focus on longer-term change. Here we examine seasonal variability in the small-scale fisheries of Baja California Sur, Mexico based on 13 years of government fisheries data. We investigate how four fisheries indicators with direct relevance to ecological resilience-magnitude and variance of landed fish biomass, taxon richness and the proportion of top-trophic-level taxa in total catch-vary within and among years and at multiple spatial scales. We find that these resilience indicators vary both seasonally and spatially. These results highlight the value of finer-scale monitoring and management, particularly for data-poor fisheries.

Concepts: California, Pacific Ocean, Baja California peninsula, Baja California Sur, Gulf of California, Baja California, Municipalities of Mexico, La Paz, Baja California Sur


Historical forest conditions are often used to inform contemporary management goals because historical forests are considered to be resilient to ecological disturbances. The General Land Office (GLO) surveys of the late nineteenth and early twentieth centuries provide regionally quasi-contiguous datasets of historical forests across much of the Western United States. Multiple methods exist for estimating tree density from point-based sampling such as the GLO surveys, including distance-based and area-based approaches. Area-based approaches have been applied in California mixed-conifer forests but their estimates have not been validated. To assess the accuracy and precision of plotless density estimators with potential for application to GLO data in this region, we imposed a GLO sampling scheme on six mapped forest stands of known densities (159-784 trees ha(-1) ) in the Sierra Nevada in California, US, and Baja California Norte, Mexico. We compared three distance-based plotless density estimators (Cottam, Pollard, and Morisita) as well as two Voronoi area (VA) estimators - the Delincé and mean harmonic Voronoi density (MHVD) - to the true densities. We simulated sampling schemes of increasing intensity to assess sampling error. The relative error (RE) of density estimates for the GLO sampling scheme ranged from 0.36 to 4.78. The least biased estimate of tree density in every stand was obtained with the Morisita estimator and the most biased was obtained with the MHVD estimator. The MHVD estimates of tree density were 1.2-to-3.8 times larger than the true densities and performed best in stands subject to fire exclusion for 100 years. The Delincé approach obtained accurate estimates of density, implying that the Voronoi approach is theoretically sound but that its application in the MHVD was flawed. The misapplication was attributed to two causes: 1) the use of a crown scaling factor that does not correct for the number of trees sampled; and 2) the persistent underestimate of the true VA due to a weak relationship between tree size and VA. The magnitude of differences between true densities and MHVD estimates suggest caution in using results based on the MHVD to inform management and restoration practices in the conifer forests of the American West. This article is protected by copyright. All rights reserved.

Concepts: Sample size, Estimator, California, Accuracy and precision, Trees, Baja California peninsula, Baja California Sur, Sierra Nevada


Dengue is a common and growing problem worldwide, with an estimated 70-140 million cases per year. Traditional, healthcare-based, government-implemented dengue surveillance is resource intensive and slow. As global Internet use has increased, novel, Internet-based disease monitoring tools have emerged. Google Dengue Trends (GDT) uses near real-time search query data to create an index of dengue incidence that is a linear proxy for traditional surveillance. Studies have shown that GDT correlates highly with dengue incidence in multiple countries on a large spatial scale. This study addresses the heterogeneity of GDT at smaller spatial scales, assessing its accuracy at the state-level in Mexico and identifying factors that are associated with its accuracy. We used Pearson correlation to estimate the association between GDT and traditional dengue surveillance data for Mexico at the national level and for 17 Mexican states. Nationally, GDT captured approximately 83% of the variability in reported cases over the 9 study years. The correlation between GDT and reported cases varied from state to state, capturing anywhere from 1% of the variability in Baja California to 88% in Chiapas, with higher accuracy in states with higher dengue average annual incidence. A model including annual average maximum temperature, precipitation, and their interaction accounted for 81% of the variability in GDT accuracy between states. This climate model was the best indicator of GDT accuracy, suggesting that GDT works best in areas with intense transmission, particularly where local climate is well suited for transmission. Internet accessibility (average ∼36%) did not appear to affect GDT accuracy. While GDT seems to be a less robust indicator of local transmission in areas of low incidence and unfavorable climate, it may indicate cases among travelers in those areas. Identifying the strengths and limitations of novel surveillance is critical for these types of data to be used to make public health decisions and forecasting models.

Concepts: Statistics, Climate, Approximation, Estimation, Mexico, Internet, Baja California peninsula, Baja California


The Californian single-leaf pinyon (Pinus monophylla var. californiarum), a subspecies of the single-leaf pinyon (the world’s only one-needled pine), inhabits semi-arid zones of the Mojave Desert (southern Nevada and southeastern California, US) and also of northern Baja California (Mexico). This tree is distributed as a relict subspecies, at elevations of between 1,010 and 1,631 m in the geographically isolated arid Sierra La Asamblea, an area characterized by mean annual precipitation levels of between 184 and 288 mm. The aim of this research was (i) to estimate the distribution of P. monophylla var. californiarum in Sierra La Asamblea by using Sentinel-2 images, and (ii) to test and describe the relationship between the distribution of P. monophylla and five topographic and 18 climate variables. We hypothesized that (i) Sentinel-2 images can be used to predict the P. monophylla distribution in the study site due to the finer resolution (×3) and greater number of bands (×2) relative to Landsat-8 data, which is publically available free of charge and has been demonstrated to be useful for estimating forest cover, and (ii) the topographical variables aspect, ruggedness and slope are particularly important because they represent important microhabitat factors that can determine the sites where conifers can become established and persist. An atmospherically corrected a 12-bit Sentinel-2A MSI image with 10 spectral bands in the visible, near infrared, and short-wave infrared light region was used in combination with the normalized differential vegetation index (NDVI). Supervised classification of this image was carried out using a backpropagation-type artificial neural network algorithm. Stepwise multiple linear binominal logistical regression and Random Forest classification including cross validation were used to model the associations between presence/absence of P. monophylla and the five topographical and 18 climate variables. Using supervised classification of Sentinel-2 satellite images, we estimated that P. monophylla covers 6,653 ± 319 ha in the isolated Sierra La Asamblea. The NDVI was one of the variables that contributed most to the prediction and clearly separated the forest cover (NDVI > 0.35) from the other vegetation cover (NDVI < 0.20). Ruggedness was the most influential environmental predictor variable, indicating that the probability of occurrence of P. monophylla was greater than 50% when the degree of ruggedness terrain ruggedness index was greater than 17.5 m. The probability of occurrence of the species decreased when the mean temperature in the warmest month increased from 23.5 to 25.2 °C. Ruggedness is known to create microclimates and provides shade that minimizes evapotranspiration from pines in desert environments. Identification of the P. monophylla stands in Sierra La Asamblea as the most southern populations represents an opportunity for research on climatic tolerance and community responses to climate variability and change.

Concepts: Precipitation, Climate, California, Neural network, Baja California peninsula, Baja California, Nevada, Pinyon pine


The 16SrXIII group from phytoplasma bacteria were identified in salivary glands from Homalodisca liturata, which were collected in El Comitán on the Baja California peninsula in Mexico. We were able to positively identify 15 16S rRNA gene sequences with the corresponding signature sequence of ‘CandidatusPhytoplasma’ (CAAGAYBATKATGTKTAGCYGGDCT) and in silico restriction fragment length polymorphism (RFLP) profiles (F value estimations) coupled with a phylogenetic analysis to confirm their relatedness to ‘CandidatusPhytoplasma hispanicum’, which in turn belongs to the 16SrXIII group. A restriction analysis was carried out with AluI and EcoRI to confirm that the five sequences belongs to subgroup D. The rest of the sequences did not exhibit any known RFLP profile related to a subgroup reported in the 16SrXIII group.

Concepts: Ribosomal RNA, 16S ribosomal RNA, Mexico, Restriction fragment length polymorphism, Baja California peninsula, Baja California Sur, Gulf of California, Baja California


Smoking methamphetamine is associated with increased risk of HIV among female sex workers (FSW). The structural context of substance use is an important shaper of individual behaviour; however, structural determinants of methamphetamine use among FSWs are largely unknown. We identified individual, structural and neighbourhood factors associated with smoking methamphetamine among FSWs in the border city of Tijuana, Baja California, Mexico.

Concepts: California, Mexico, San Diego, Baja California peninsula, Gulf of California, Baja California, Mexicali, Tijuana


Anemia is a public health concern among women in rural Baja California, Mexico. The purpose of this study was to identify the individual and community factors contributing to the disproportionately high prevalence of anemia among women in this region.

Concepts: Public health, California, Pacific Ocean, Mexico, Baja California peninsula, Gulf of California, Baja California, Mexicali