Second-growth and small forest clearings have little effect on the temporal activity patterns of Amazonian phyllostomid bats

Secondary forests and human-made forest gaps are conspicuous features of tropical landscapes. Yet, behavioural responses to these aspects of anthropogenically-modified forests remain poorly investigated. Here, we analyse the effects of small human-made clearings and secondary forests on tropical bats by examining the guild- and species-level activity patterns of phyllostomids sampled in the Central Amazon, Brazil. Specifically, we contrast the temporal activity patterns and degree of temporal overlap of six frugivorous and four gleaning animalivorous species in old-growth forest and second-growth forest and of four frugivores in old-growth forest and forest clearings. The activity patterns of frugivores and gleaning animalivores did not change between old-growth forest and second-growth, nor did the activity patterns of frugivores between old-growth forest and clearings. However, at the species level we detected significant differences for Artibeus obscurus (old-growth forest vs. second-growth) and Artibeus concolor (old-growth forest vs.clearings). The degree of temporal overlap was greater than random in all sampled habitats. However, whereas for frugivorous species the degree of temporal overlap was similar between old-growth forest and second-growth, for gleaning animalivores it was lower in second-growth than in old-growth forest. On the other hand, forest clearings were characterized by increased temporal overlap between frugivores. Changes in activity patterns and temporal overlap may result from differential foraging opportunities and dissimilar predation risks. Yet, our analyses suggest that activity patterns of bats in second-growth and small forest clearings, two of the most prominent habitats in humanized tropical landscapes, varies little from the activity patterns in old-growth forest

Precision restoration: a necessary approach to foster forest recovery in the 21st century

We thank S. Tabik, E. Guirado, and Garnata Drone SL for fruitful debates about the application of remote sensing and artificial intelligence in restoration. E. McKeown looked over the English version of the manuscript. Original drawings were made by J. D. Guerrero. This work was supported by projects RESISTE (P18-RT-1927) from the Consejeria de Economia, Conocimiento, y Universidad from the Junta de Andalucia, and AVA201601.19 (NUTERA-DE I), DETECTOR (A-RNM-256-UGR18), and AVA2019.004 (NUTERA-DE II), cofinanced (80%) by the FEDER Program. F.M.-R. acknowledges the support of the Agreement 4580 between OTRI-UGR and the city council of La Zubia. We thank an anonymous reviewer for helpful comments that improved the manuscript.Forest restoration is currently a primary objective in environmental management policies at a global scale, to the extent that impressive initiatives and commitments have been launched to plant billions of trees. However, resources are limited and the success of any restoration effort should be maximized. Thus, restoration programs should seek to guarantee that what is planted today will become an adult tree in the future, a simple fact that, however, usually receives little attention. Here, we advocate for the need to focus restoration efforts on an individual plant level to increase establishment success while reducing negative side effects by using an approach that we term “precision forest restoration” (PFR). The objective of PFR will be to ensure that planted seedlings or sowed seeds will become adult trees with the appropriate landscape configuration to create functional and self-regulating forest ecosystems while reducing the negative impacts of traditional massive reforestation actions. PFR can take advantage of ecological knowledge together with technologies and methodologies from the landscape scale to the individual- plant scale, and from the more traditional, low-tech approaches to the latest high-tech ones. PFR may be more expensive at the level of individual plants, but will be more cost-effective in the long term if it allows for the creation of resilient forests able to providemultiple ecosystemservices. PFR was not feasible a few years ago due to the high cost and low precision of the available technologies, but it is currently an alternative that might reformulate a wide spectrum of ecosystem restoration activities.Junta de Andalucia P18-RT-1927European Commission AVA201601.19 A-RNM-256-UGR18 AVA2019.004OTRI-UGR 4580city council of La Zubia 458

The Potential of Regional Planning for the Centro de Investigacion Y Formacion Social (ITESO)

This report results of the work done for the Centro de Investigación y Formación Social (CIFS) - University ITESO. The purpose of the master’s project is to recommend regional planning as an alternative approach for projects developed at CIFS in the South area of Jalisco. In order to do that this project is divided in three main sections. The first one presents a general overview of the academic research Centre as the client, with an emphasis on the organizational structure. The next section provides a description of the projects undertaken in the last four year at CIFS, an it describes the context of the projects implemented in the South area of Jalisco. Lastly, the third section points out some values of regional planning, as a progressive approach for the Centre’s practice. The report concludes by stating that for the most part CIFS had no vision of the region as a whole and how the issues interrelated. Thus it is necessary to identify opportunities and advise changes for the Centre’s practice. In addition, several recommendations are suggested in terms of looking at the projects through a regional perspective, as opposed of having single and unconnected initiatives. This study was undertaken because of the lost potential that results from the Centre developing single and unconnected projects, when there are many opportunities to gain from an integrated regional approach.Applied Science, Faculty ofCommunity and Regional Planning (SCARP), School ofUnreviewedGraduat

Plant Attributes that Drive Dispersal and Establishment Limitation in Tropical Agricultural Landscapes

Factors that influence tropical-forest regeneration have been of interest across the tropics. We tested the degree of dispersal and establishment limitation of pioneer and non-pioneer tree species with different dispersal modes and seed sizes, using data on both seed fall and seedling establishment in primary forest, secondary forest, and pasture excluded from livestock. The study took place in a lowland tropical rain forest in southeastern Mexico. To calculate dispersal and establishment limitation, we used a density-weighted index that considers: (1) whether a seed or seedling of a given species has arrived in the sample area; and (2) the fraction of seeds or seedlings contributed by a given species relative to the total number of seeds or seedlings arriving at a sampling station. Dispersal limitation of non-pioneer species and animal-dispersed species decreased with succession. The secondary forest had less dispersal limitation for wind-dispersed pioneers than pasture, resulting in a dense aggregation of species with seeds dispersed by wind. Overall, establishment limitation differed between animal-dispersed and wind-dispersed species in the primary forest, and was negatively correlated with seed size. The low capacity of most species to arrive, germinate, and establish as seedlings in pastures slows succession back to forest. To overcome barriers to natural succession in pastures, transplanting seedlings of non-pioneer species is suggested because most of them show high dispersal and establishment limitation

Integrating Density into Dispersal and Establishment Limitation Equations in Tropical Forests

Plant recruitment in tropical forests reflects the chance that seeds arrive at a site resulting in seedling establishment. To inform tropical forest restoration, we ask how seed and seedling densities differentially affect dispersal and establishment limitation in successional habitats in a tropical agricultural landscape. Methods: In Los Tuxtlas Biosphere Reserve, we calculated indices of dispersal and establishment limitation using data on seed rain and seedling establishment in old-growth forest, secondary forest, and fenced pasture. We present an index that considers variations in dispersal- and establishment-limitation including density-weighted calculations. Results: There were greater dispersal and establishment limitations in pasture than in forests. Substantial differences in both dispersal and establishment limitation occurred among the 33 species for which seed and seedling data were available. Only 5% of all species had mid to low limitation in both dispersal and establishment. In contrast, 60% of all species showed high dispersal and establishment limitation. Plant recruitment in pastures is impeded by low seed arrival, given that 77% of the recorded species showed extremely high dispersal limitation (>90%). Conclusions: The low capacity of most species to arrive, seeds to germinate and seedlings to establish in pastures slow down succession back to forest

Tropical Dry Forest climatic edaphic and vegetation Database

Selection of sample pointsWe created a 1 km2 grid for each of the five biogeographic regions and identified the centroid of each cell of the grid, which we used as a sample point. We selected 100 sample points in a random block design from the full population of centroids for a given ecoregion and repeated this for each of the 80 studied ecoregions. We then used the HILDA+ Global Land Use Change layer (HILDAplus_vGLOB-1.0; Winkler, Fuchs, Rounsevell, & Herold, 2021) to evaluate the land use types for the resulting grid of 8,000 points. The HILDA+ layer contains land cover information from 1960 to 2019, which allowed us to mask points defined as urban, cropland, ocean, water, or as missing data. We used the remaining unmasked points, defined as forest, shrubland, pasture, rangeland, or unmanaged grassland, in our analyses. We used a randomized block design to assess relationships between NDVI and climatic and edaphic variations found across TDL. All spatial analyses were conducted in ArcGIS 10.2.1.Normalized difference vegetation indexWe used 12 years (2005 to 2016) of NDVI data (250 m pixel resolution) collected with Moderate Resolution Imaging Spectroradiometer (MODIS) instruments aboard US NASA’s Terra and Aqua satellites. The MODIS product was used because of its longevity, resolution, and frequent observations. Data analyses relied on 12-yr mean NDVI conditions for each of the 80 ecoregions sampled here. While data are collected sub-weekly, we used averaged monthly data to calculate mean annual NDVI for the 12-yr period. NDVI is dimensionless and ranges from -0.1 to 0.9, with higher values associated with denser vegetation and lower values associated with sparser vegetation. The lowest values represent bare ground. Climatic and edaphic metricsFor each sample point, we obtained rainfall, temperature, and evapotranspiration data from repositories including: NASA Earth Observations (https://neo.gsfc.nasa.gov/); Climatic Hazards Center - UC Santa Barbara (www.chc.ucsb.edu/about); Numerical Terradynamic Simulation Group (NTSG)-University of Montana (www.ntsg.umt.edu/); Oak Ridge National Laboratory Distributed Active Archive Center (ORNL DAAC) for Biogeochemical Dynamics (www.daac.ornl.gov/); and SOILGRID (www.soilgrids.org/). We examined a total of 17 climate variables: four temperature metrics, 10 precipitation metrics, an evapotranspiration metric, and two aridity metrics that integrate annual precipitation and temperature measures. All climate variables were summarized into mean annual averages representing from 1 up to 12 years of available data (Table S3). We also considered 17 edaphic metrics involving physical, chemical and biological properties of soils. These were all represented by a single point in time measurement, and so have no temporal averaging component. Each of the 34 metrics was processed and normalized to standard units.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

Growth rates

Data on growth for fifty tree species during the first seven years of a reforestation experiment. Growth rates (column "GROWTH" in data) represent the difference in individual size (basal diameter measured in mm) between consecutive censuses. The column "CENSUS" represents the numeric id of each census, "ID" represents the unique ID of each individual plant, "SPECIES" represents the species name, "PLOT" represents the identity of the 30 x 30 m plot, "SUBPLOT" represents the identity of 13 x 13 m plots within each plot, "TREATMENT" represents the planting treatment (A=animal-dispersed plantings, W=wind-dispersed plantings, C=Control), "SIZE" represents the basal diameter (in mm) of each individual (at the start of each growth interval), "YEAR" represents the year of each census, and "MONTH" represents months since surveys began

Presence/absence data

Presence/absence of natural recruits during the first seven years of a reforestation experiment. Presence is represented by 18 columns, each named "Presence_" followed by the numeric identity of each census. Prior to the first record of individual occurrence, presence is recorded as "NA." After first occurrence, each individual was recorded as "1" if it was present during that particular census and "0" if it was absent. The column "ID" represents the unique ID of each individual plant, "SPECIES" represents the species name, "PLOT" represents the identity of the 30 x 30 m plot, "SUBPLOT" represents the identity of 13 x 13 m plots within each plot, "TREATMENT" represents the planting treatment (A=animal-dispersed plantings, W=wind-dispersed plantings, C=Control), "FIRST_SIZE" represents the basal diameter (in mm) of each individual during the first census in which it was recorded

All natural recruits

Total number of natural recruits of each species that recruited in experimental plots during the course of the study. The column "COUNT" represents the total number of each species in each 13 x 13 m subplot. The column "SPECIES" represents the species name, "PLOT" represents the identity of the 30 x 30 m plot, "SUBPLOT" represents the identity of 13 x 13 m plots within each plot, and "TRT" represents the planting treatment (A=animal-dispersed plantings, W=wind-dispersed plantings, C=Control)