Jorge A. Ahumada

Local neighborhood effects on long-term survival of individual trees in a neotropical forest

The survival of approximately 235 000 individual tropical trees and saplings in the 50 ha permanent plot on Barro Colorado Island (BCI), Panama was analyzed over a 13-year interval (1982–1995) as a function of four biotic neighborhood variables: (i) total stem density; (ii) conspecific density; (iii) relative plant size; and (iv) relative species richness. These neighborhood variables were measured in annular rings of width 2.5 m, extending 30 m from a given focal plant, and in one more distant annulus at 47.5–50 m. Because survival was spatially autocorrelated, a Gibbs sampler and a Monte Carlo Markov chain method were used for fitting an autologistic regression model to obtain unbiased estimates of parameter variances for hypothesis testing. After pooling all species at the community level, results showed that all four variables had significant and often strong effects on focal plant survival. Three of the four variables had negative effects on focal plant survival; relative plant size was the only variable with a positive effect (18% increase in the survival odds ratio). The variables with a negative effect on the survival odds ratio, in order of their effect strength in the nearest annulus, were: stem density (a 70% reduction in the survival odds ratio), conspecific density (50% reduction) and species richness (13% reduction). A guild-level analysis revealed considerable heterogeneity among guilds in their responses to these variables. For example, survival of gap species showed a much larger positive response to relative plant size than did survival of shade-tolerant species. Survival of shrub species was positively affected by conspecific density, but canopy tree survival was negatively affected. Conspecific density negatively affected survival of rare species much more strongly than survival of common species. The neighborhood effects of conspecific density disappear within approximately 12–15 m of the focal plant. Although locally strong, the rapid spatial decay of these effects raises unanswered questions about their quantitative contribution to the maintenance of tree diversity on landscape scales in the BCI forest.

Community structure and diversity of tropical forest mammals: data from a global camera trap network

Terrestrial mammals are a key component of tropical forest communities as indicators of ecosystem health and providers of important ecosystem services. However, there is little quantitative information about how they change with local, regional and global threats. In this paper, the first standardized pantropical forest terrestrial mammal community study, we examine several aspects of terrestrial mammal species and community diversity (species richness, species diversity, evenness, dominance, functional diversity and community structure) at seven sites around the globe using a single standardized camera trapping methodology approach. The sites—located in Uganda, Tanzania, Indonesia, Lao PDR, Suriname, Brazil and Costa Rica—are surrounded by different landscape configurations, from continuous forests to highly fragmented forests. We obtained more than 51 000 images and detected 105 species of mammals with a total sampling effort of 12 687 camera trap days. We find that mammal communities from highly fragmented sites have lower species richness, species diversity, functional diversity and higher dominance when compared with sites in partially fragmented and continuous forest. We emphasize the importance of standardized camera trapping approaches for obtaining baselines for monitoring forest mammal communities so as to adequately understand the effect of global, regional and local threats and appropriately inform conservation actions.

Monitoring the status and trends of tropical forest terrestrial vertebrate communities from camera trap data: a tool for conservation.

Reducing the loss of biodiversity is key to ensure the future well being of the planet. Indicators to measure the state of biodiversity should come from primary data that are collected using consistent field methods across several sites, longitudinal, and derived using sound statistical methods that correct for observation/detection bias. In this paper we analyze camera trap data collected between 2008 and 2012 at a site in Costa Rica (Volcan Barva transect) as part of an ongoing tropical forest global monitoring network (Tropical Ecology Assessment and Monitoring Network). We estimated occupancy dynamics for 13 species of mammals, using a hierarchical modeling approach. We calculated detection-corrected species richness and the Wildlife Picture Index, a promising new indicator derived from camera trap data that measures changes in biodiversity from the occupancy estimates of individual species. Our results show that 3 out of 13 species showed significant declines in occupancy over 5 years (lowland paca, Central American agouti, nine-banded armadillo). We hypothesize that hunting, competition and/or increased predation for paca and agouti might explain these patterns. Species richness and the Wildlife Picture Index are relatively stable at the site, but small herbivores that are hunted showed a decline in diversity of about 25%. We demonstrate the usefulness of longitudinal camera trap deployments coupled with modern statistical methods and advocate for the use of this approach in monitoring and developing global and national indicators for biodiversity change.

Estimating Species Richness and Modelling Habitat Preferences of Tropical Forest Mammals from Camera Trap Data

Medium-to-large mammals within tropical forests represent a rich and functionally diversified component of this biome; however, they continue to be threatened by hunting and habitat loss. Assessing these communities implies studying species’ richness and composition, and determining a state variable of species abundance in order to infer changes in species distribution and habitat associations. The Tropical Ecology, Assessment and Monitoring (TEAM) network fills a chronic gap in standardized data collection by implementing a systematic monitoring framework of biodiversity, including mammal communities, across several sites. In this study, we used TEAM camera trap data collected in the Udzungwa Mountains of Tanzania, an area of exceptional importance for mammal diversity, to propose an example of a baseline assessment of species’ occupancy. We used 60 camera trap locations and cumulated 1,818 camera days in 2009. Sampling yielded 10,647 images of 26 species of mammals. We estimated that a minimum of 32 species are in fact present, matching available knowledge from other sources. Estimated species richness at camera sites did not vary with a suite of habitat covariates derived from remote sensing, however the detection probability varied with functional guilds, with herbivores being more detectable than other guilds. Species-specific occupancy modelling revealed novel ecological knowledge for the 11 most detected species, highlighting patterns such as ‘montane forest dwellers’, e.g. the endemic Sanje mangabey (Cercocebus sanjei), and ‘lowland forest dwellers’, e.g. suni antelope (Neotragus moschatus). Our results show that the analysis of camera trap data with account for imperfect detection can provide a solid ecological assessment of mammal communities that can be systematically replicated across sites.

The dynamics, transmission, and population impacts of avian malaria in native Hawaiian birds: a modeling approach

We developed an epidemiological model of avian malaria (Plasmodium relictum) across an altitudinal gradient on the island of Hawaii that includes the dynamics of the host, vector, and parasite. This introduced mosquito-borne disease is hypothesized to have contributed to extinctions and major shifts in the altitudinal distribution of highly susceptible native forest birds. Our goal was to better understand how biotic and abiotic factors influence the intensity of malaria transmission and impact on susceptible populations of native Hawaiian forest birds. Our model illustrates key patterns in the malaria-forest bird system: high malaria transmission in low-elevation forests with minor seasonal or annual variation in infection; episodic transmission in mid-elevation forests with site-to-site, seasonal, and annual variation depending on mosquito dynamics; and disease refugia in high-elevation forests with only slight risk of infection during summer. These infection patterns are driven by temperature and rainfall effects on parasite incubation period and mosquito dynamics across an elevational gradient and the availability of larval habitat, especially in mid-elevation forests. The results from our model suggest that disease is likely a key factor in causing population decline or restricting the distribution of many susceptible Hawaiian species and preventing the recovery of other vulnerable species. The model also provides a framework for the evaluation of factors influencing disease transmission and alternative disease control programs, and to evaluate the impact of climate change on disease cycles and bird populations.

Standardized Assessment of Biodiversity Trends in Tropical Forest Protected Areas: The End Is Not in Sight

Extinction rates in the Anthropocene are three orders of magnitude higher than background and disproportionately occur in the tropics, home of half the world’s species. Despite global efforts to combat tropical species extinctions, lack of high-quality, objective information on tropical biodiversity has hampered quantitative evaluation of conservation strategies. In particular, the scarcity of population-level monitoring in tropical forests has stymied assessment of biodiversity outcomes, such as the status and trends of animal populations in protected areas. Here, we evaluate occupancy trends for 511 populations of terrestrial mammals and birds, representing 244 species from 15 tropical forest protected areas on three continents. For the first time to our knowledge, we use annual surveys from tropical forests worldwide that employ a standardized camera trapping protocol, and we compute data analytics that correct for imperfect detection. We found that occupancy declined in 22%, increased in 17%, and exhibited no change in 22% of populations during the last 3–8 years, while 39% of populations were detected too infrequently to assess occupancy changes. Despite extensive variability in occupancy trends, these 15 tropical protected areas have not exhibited systematic declines in biodiversity (i.e., occupancy, richness, or evenness) at the community level. Our results differ from reports of widespread biodiversity declines based on aggregated secondary data and expert opinion and suggest less extreme deterioration in tropical forest protected areas. We simultaneously fill an important conservation data gap and demonstrate the value of large-scale monitoring infrastructure and powerful analytics, which can be scaled to incorporate additional sites, ecosystems, and monitoring methods. In an era of catastrophic biodiversity loss, robust indicators produced from standardized monitoring infrastructure are critical to accurately assess population outcomes and identify conservation strategies that can avert biodiversity collapse.

From plot to landscape scale: linking tropical biodiversity measurements across spatial scales

Quantitative measurements of changes in tropical biodiversity are sparse, despite wide agreement that maintaining biodiversity is a key conservation goal. Pan-tropical networks to systematically measure plot-level biodiversity are currently being developed to close this gap. We propose that a key component of such networks is the monitoring of human activities at broader scales around plots, to enable interpretation of biodiversity trends. This monitoring goal raises questions about the spatial extent and variables needed to capture interactions between human activities and biodiversity at multiple scales. We suggest a pragmatic approach to delineate and monitor a “zone of interaction” around biodiversity measurement sites to bridge across these scales. We identify the hydrologic, biological, and human interactions that connect local-scale measurements with broader-scale processes. We illustrate the concept with case studies in the Udzungwa Mountains in Tanzania and Ranomafana National Park in Madagascar; however, the framework applies to other biodiversity measurement sites and monitoring networks as well.

Consequences of harvesting for genetic diversity in American ginseng (Panax quinquefolius L.): a simulation study

American ginseng, Panax quinquefolius L., is one of the most heavily traded medicinal plants in North America. The effect of harvest on genetic diversity in ginseng was measured with a single generation culling simulation program. Culling scenarios included random harvest at varying levels, legal limit random harvest and legal limit mature plant harvest. The legal limit was determined by the proportion of legally harvestable plants per population (% mature plants per population). Random harvest at varying levels resulted in significant loss of genetic diversity, especially allelic richness. Relative to initial levels, average within-population genetic diversity (He) was significantly lower when plants were culled randomly at the legal limit (Mann–Whitney U = 430, p < 0.001) or when only mature plants were culled (Mann–Whitney U = 394, p < 0.01). Within-population genetic diversity was significantly higher with legal limit mature plant harvest (He = 0.068) than when plants were culled randomly at the legal limit (He = 0.064; U = 202, p < 0.01). Based on these simulations of harvest over one generation, we recommend that harvesting fewer than the proportion of mature plants could reduce the negative genetic effects of harvest on ginseng populations.

Building essential biodiversity variables (EBVs) of species distribution and abundance at a global scale

Much biodiversity data is collected worldwide, but it remains challenging to assemble the scattered knowledge for assessing biodiversity status and trends. The concept of Essential Biodiversity Variables (EBVs) was introduced to structure biodiversity monitoring globally, and to harmonize and standardize biodiversity data from disparate sources to capture a minimum set of critical variables required to study, report and manage biodiversity change. Here, we assess the challenges of a ‘Big Data’ approach to building global EBV data products across taxa and spatiotemporal scales, focusing on species distribution and abundance. The majority of currently available data on species distributions derives from incidentally reported observations or from surveys where presence‐only or presence–absence data are sampled repeatedly with standardized protocols. Most abundance data come from opportunistic population counts or from population time series using standardized protocols (e.g. repeated surveys of the same population from single or multiple sites). Enormous complexity exists in integrating these heterogeneous, multi‐source data sets across space, time, taxa and different sampling methods. Integration of such data into global EBV data products requires correcting biases introduced by imperfect detection and varying sampling effort, dealing with different spatial resolution and extents, harmonizing measurement units from different data sources or sampling methods, applying statistical tools and models for spatial inter‐ or extrapolation, and quantifying sources of uncertainty and errors in data and models. To support the development of EBVs by the Group on Earth Observations Biodiversity Observation Network (GEO BON), we identify 11 key workflow steps that will operationalize the process of building EBV data products within and across research infrastructures worldwide. These workflow steps take multiple sequential activities into account, including identification and aggregation of various raw data sources, data quality control, taxonomic name matching and statistical modelling of integrated data. We illustrate these steps with concrete examples from existing citizen science and professional monitoring projects, including eBird, the Tropical Ecology Assessment and Monitoring network, the Living Planet Index and the Baltic Sea zooplankton monitoring. The identified workflow steps are applicable to both terrestrial and aquatic systems and a broad range of spatial, temporal and taxonomic scales. They depend on clear, findable and accessible metadata, and we provide an overview of current data and metadata standards. Several challenges remain to be solved for building global EBV data products: (i) developing tools and models for combining heterogeneous, multi‐source data sets and filling data gaps in geographic, temporal and taxonomic coverage, (ii) integrating emerging methods and technologies for data collection such as citizen science, sensor networks, DNA‐based techniques and satellite remote sensing, (iii) solving major technical issues related to data product structure, data storage, execution of workflows and the production process/cycle as well as approaching technical interoperability among research infrastructures, (iv) allowing semantic interoperability by developing and adopting standards and tools for capturing consistent data and metadata, and (v) ensuring legal interoperability by endorsing open data or data that are free from restrictions on use, modification and sharing. Addressing these challenges is critical for biodiversity research and for assessing progress towards conservation policy targets and sustainable development goals.

Interdisciplinary Decision Support Dashboard: A New Framework for a Tanzanian Agricultural and Ecosystem Service Monitoring System Pilot

Landscape degradation, soil depletion, scarcity of water and fuel resources are common threats to ecosystem services, agricultural production and human livelihoods in the developing world. Observatory or monitoring networks which focus on the dynamics of coupled human-natural systems are challenged by scarce data, data heterogeneity across multiple domains and spatial scales, and complex models required to produce meaningful sustainability indicators. An additional challenge is to visualize these complex data and indicators in a unified, easily understandable framework. This paper presents an environmental sustainability dashboard that integrates GIS data with household and plot surveys, field data and remote sensing imagery to compute a variety of metrics of ecosystem stress. The dashboard, a web-based decision support tool, is a key cyberinfrastructure component designed to satisfy the objectives of a Tanzanian agricultural and ecosystem services monitoring pilot. Based on this experience we discuss our framework and how it can be generalized for building decision support tools, and their associated cyberinfrastructure, for multi-scale, interdisciplinary monitoring networks.