Refining Ixodes scapularis (Acari: Ixodidae) distribution models: a comparison of current methods to an established protocol

Blacklegged ticks (Ixodes scapularis Say) pose an enormous public health risk in eastern North America as the vector responsible for transmitting 7 human pathogens, including those causing the most common vector-borne disease in the United States, Lyme disease. Species distribution modeling is an increasingly popular method for predicting the potential distribution and subsequent risk of blacklegged ticks, however, the development of such models thus far is highly variable and would benefit from the use of standardized protocols. To identify where standardized protocols would most benefit current distribution models, we completed the "Overview, Data, Model, Assessment, and Prediction" (ODMAP) distribution modeling protocol for 21 publications reporting 22 blacklegged tick distribution models. We calculated an average adherence of 73.4% (SD ± 29%). Most prominently, we found that authors could better justify and connect their selection of variables and associated spatial scales to blacklegged tick ecology. In addition, the authors could provide clearer descriptions of model development, including checks for multicollinearity, spatial autocorrelation, and plausibility. Finally, authors could improve their reporting of variable effects to avoid undermining the models' utility in informing species-environment relationships. To enhance future model rigor and reproducibility, we recommend utilizing several resources including the ODMAP protocol, and suggest that journals make protocol compliance a publication prerequisite.

Oaks enhance early life stage longleaf pine growth and density in a subtropical xeric savanna

The interplay of positive and negative species interactions controls species assembly in communities. Dryland plant communities, such as savannas, are important to global biodiversity and ecosystem functioning. Sandhill oaks in xeric savannas of the southeastern United States can facilitate longleaf pine by enhancing seedling survival, but the effects of oaks on recruitment and growth of longleaf pine have not been examined. We censused, mapped, and monitored nine contiguous hectares of longleaf pine in a xeric savanna to quantify oak-pine facilitation, and to examine other factors impacting recruitment, such as vegetation cover and longleaf pine tree density. We found that newly recruited seedlings and grass stage longleaf pines were more abundant in oak-dominated areas where densities were 230% (newly recruited seedlings) and 360% (grass stage) greater from lowest to highest oak neighborhood densities. Longleaf pine also grew faster under higher oak density. Longleaf pine recruitment was lowest under longleaf pine canopies. Mortality of grass stage and bolt stage longleaf pine was low (~1.0% yr-1) in the census interval without fire. Overall, our findings highlight the complex interactions between pines and oaks-two economically and ecologically important genera globally. Xeric oaks should be incorporated as a management option for conservation and restoration of longleaf pine ecosystems.

Risk of spread of the Asian citrus psyllid Diaphorina citri Kuwayama (Hemiptera: Liviidae) in Ghana

The impact of invasive species on biodiversity, food security and economy is increasingly noticeable in various regions of the globe as a consequence of climate change. Yet, there is limited research on how climate change affects the distribution of the invasive Asian citrus psyllid Diaphorina citri Kuwayama (Hemiptera:Liviidae) in Ghana. Using maxnet package to fit the Maxent model in R software, we answered the following questions; (i) what are the main drivers for D. citri distribution, (ii) what are the D. citri-specific habitat requirements and (iii) how well do the risk maps fit with what we know to be correctly based on the available evidence?. We found that temperature seasonality (Bio04), mean temperature of warmest quarter (Bio10), precipitation of driest quarter (Bio17), moderate resolution imaging spectroradiometer land cover and precipitation seasonality (Bio15), were the most important drivers of D. citri distribution. The results follow the known distribution records of the pest with potential expansion of habitat suitability in the future. Because many invasive species, including D. citri, can adapt to the changing climates, our findings can serve as a guide for surveillance, tracking and prevention of D. citri spread in Ghana.

LIES of omission: complex observation processes in ecology

Advances in statistics mean that it is now possible to tackle increasingly sophisticated observation processes. The intricacies and ambitious scale of modern data collection techniques mean that this is now essential. Methodological research to make inference about the biological process while accounting for the observation process has expanded dramatically, but solutions are often presented in field-specific terms, limiting our ability to identify commonalities between methods. We suggest a typology of observation processes that could improve translation between fields and aid methodological synthesis. We propose the LIES framework (defining observation processes in terms of issues of Latency, Identifiability, Effort and Scale) and illustrate its use with both simple examples and more complex case studies.

Predicting future climate change impacts on the potential distribution of the black howler monkey (Alouatta pigra): an endangered arboreal primate

Climate change is one of the main factors affecting biodiversity worldwide at an alarming rate. In addition to increases in global extreme weather events, melting of polar ice caps, and subsequent sea level rise, climate change might shift the geographic distribution of species. In recent years, interest in understanding the effects of climate change on species distribution has increased, including species which depend greatly on forest cover for survival, such as strictly arboreal primates. Here, we generate a series of species distribution models (SDMs) to evaluate future projections under different climate change scenarios on the distribution of the black howler monkey (Alouatta pigra), an endemic endangered primate species. Using SDMs, we assessed current and future projections of their potential distribution for three Social Economic Paths (SSPs) for the years 2030, 2050, 2070, and 2090. Specifically, we found that precipitation seasonality (BIO15, 30.8%), isothermality (BIO3, 25.4%), and mean diurnal range (BIO2, 19.7.%) are the main factors affecting A. pigra distribution. The future climate change models suggested a decrease in the potential distribution of A. pigra by projected scenarios (from - 1.23 to - 12.66%). The highly suitable area was the most affected above all in the more pessimist scenario most likely related to habitat fragmentation. Our study provides new insights into the potential future distribution and suitable habitats of Alouatta pigra. Such information could be used by local communities, governments, and non-governmental organizations for conservation planning of this primate species.

Transnational conservation to anticipate future plant shifts in Europe

To meet the COP15 biodiversity framework in the European Union (EU), one target is to protect 30% of its land by 2030 through a resilient transnational conservation network. The European Alps are a key hub of this network hosting some of the most extensive natural areas and biodiversity hotspots in Europe. Here we assess the robustness of the current European reserve network to safeguard the European Alps' flora by 2080 using semi-mechanistic simulations. We first highlight that the current network needs strong readjustments as it does not capture biodiversity patterns as well as our conservation simulations. Overall, we predict a strong shift in conservation need through time along latitudes, and from lower to higher elevations as plants migrate upslope and shrink their distribution. While increasing species, trait and evolutionary diversity, migration could also threaten 70% of the resident flora. In the face of global changes, the future European reserve network will need to ensure strong elevation and latitudinal connections to complementarily protect multifaceted biodiversity beyond national borders.

Modelling heterogeneity in the classification process in multi-species distribution models can improve predictive performance

Species distribution models and maps from large-scale biodiversity data are necessary for conservation management. One current issue is that biodiversity data are prone to taxonomic misclassifications. Methods to account for these misclassifications in multi-species distribution models have assumed that the classification probabilities are constant throughout the study. In reality, classification probabilities are likely to vary with several covariates. Failure to account for such heterogeneity can lead to biased prediction of species distributions. Here, we present a general multi-species distribution model that accounts for heterogeneity in the classification process. The proposed model assumes a multinomial generalised linear model for the classification confusion matrix. We compare the performance of the heterogeneous classification model to that of the homogeneous classification model by assessing how well they estimate the parameters in the model and their predictive performance on hold-out samples. We applied the model to gull data from Norway, Denmark and Finland, obtained from the Global Biodiversity Information Facility. Our simulation study showed that accounting for heterogeneity in the classification process increased the precision of true species' identity predictions by 30% and accuracy and recall by 6%. Since all the models in this study accounted for misclassification of some sort, there was no significant effect of accounting for heterogeneity in the classification process on the inference about the ecological process. Applying the model framework to the gull dataset did not improve the predictive performance between the homogeneous and heterogeneous models (with parametric distributions) due to the smaller misclassified sample sizes. However, when machine learning predictive scores were used as weights to inform the species distribution models about the classification process, the precision increased by 70%. We recommend multiple multinomial regression to be used to model the variation in the classification process when the data contains relatively larger misclassified samples. Machine learning prediction scores should be used when the data contains relatively smaller misclassified samples.

Are human-induced changes good or bad to dynamic landscape connectivity?

Land managers must find a compromise between rapidly changing landscapes and biodiversity conservation through ecological networks. Estimating ecological networks is a key approach to enhance or maintain functional connectivity by identifying the nodes and links of a graph, which represent habitats and their corresponding functional corridors, respectively. To understand the current state of biodiversity, it is necessary to consider dynamic landscape connectivity while relying on relevant land cover maps. Although a current land cover map is relatively easy to produce using existing data, this is challenging for past landscapes. Here we investigated the impact of changes in landscape connectivity in an urban landscape at a fine scale on the habitat availability of two bird species: the tree pipit Anthus trivialis and the short-toed treecreeper Certhia brachydactyla. These species, exhibiting different niche ecologies, have shown contrasting population trends at a medium-term scale. The occurrences of C. brachydactyla were better correlated with resistance values that maximise the use of corridors, whereas the occurrences of A. trivialis better fitted with intermediate resistance values. The statistical approach indirectly highlighted relevant information about the ecology the capacity of both species to use urban habitats. Landscape connectivity increased for both species over the 24-year study period and may have implications for local abundances, which could explain, at the national scale, the increase in populations of C. brachydactyla, but not the decrease in populations of A. trivialis. Thus, more attention must be paid on rural habitats and their associated species that are more impacted by human activities, but efforts could also be achieved in urban areas especially for highly corridor-dependent species. Studying dynamic landscape connectivity at a fine scale is essential for estimating past and future land cover changes and their associated impacts on ecological networks, to better reconcile human and biodiversity concerns in land management.

Investigating Ecological Interactions in the Tumor Microenvironment using Joint Species Distribution Models for Point Patterns

The tumor microenvironment (TME) is a complex and dynamic ecosystem that involves interactions between different cell types, such as cancer cells, immune cells, and stromal cells. These interactions can promote or inhibit tumor growth and affect response to therapy. Multitype Gibbs point process (MGPP) models are statistical models used to study the spatial distribution and interaction of different types of objects, such as the distribution of cell types in a tissue sample. Such models are potentially useful for investigating the spatial relationships between different cell types in the tumor microenvironment, but so far studies of the TME using cell-resolution imaging have been largely limited to spatial descriptive statistics. However, MGPP models have many advantages over descriptive statistics, such as uncertainty quantification, incorporation of multiple covariates and the ability to make predictions. In this paper, we describe and apply a previously developed MGPP method, the saturated pairwise interaction Gibbs point process model , to a publicly available multiplexed imaging dataset obtained from colorectal cancer patients. Importantly, we show how these methods can be used as joint species distribution models (JSDMs) to precisely frame and answer many relevant questions related to the ecology of the tumor microenvironment.

A brighter shade of future climate on Himalayan musk deer Moschus leucogaster

Himalayan musk deer (Moschus leucogaster) is classified as an endangered species by IUCN with a historically misunderstood distribution due to misidentification with other species of musk deer, Moschus spp. Taking advantage of recent genetic analyses confirming the species of various populations in Nepal and China, we produced an accurate estimate of the species' current and future distribution under multiple climate change scenarios. We collected high-quality occurrence data using systematic surveys of various protected areas of Nepal to train species distribution models. The most influential determinants of the distribution of Himalayan musk deer were precipitation of the driest quarter, temperature seasonality, and annual mean temperature. These variables, and precipitation in particular, determine the vegetation type and structure in the Himalaya, which is strongly correlated with the distribution of Himalayan musk deer. We predicted suitable habitats between the Annapurna and Kanchenjunga region of Nepal Himalaya as well as the adjacent Himalaya in China. Under multiple climate change scenarios, the vast majority (85-89%) of current suitable sites are likely to remain suitable and many new areas of suitable habitat may emerge to the west and north of the current species range in Nepal and China. Two-thirds of current and one-third of future suitable habitats are protected by the extensive network of protected areas in Nepal. The projected large gains in suitable sites may lead to population expansion and conservation gains, only when the threat of overexploitation and population decline is under control.

A stitch in time: The importance of water and sanitation services (WSS) infrastructure maintenance for cholera risk. A geospatial analysis in Harare, Zimbabwe

Understanding the factors associated with cholera outbreaks is an integral part of designing better approaches to mitigate their impact. Using a rich set of georeferenced case data from the cholera epidemic that occurred in Harare from September 2018 to January 2019, we apply spatio-temporal modelling to better understand how the outbreak unfolded and the factors associated with higher risk of being a reported case. Using Call Detail Records (CDR) to estimate weekly population movement of the community throughout the city, results suggest that broader human movement (not limited to infected agents) helps to explain some of the spatio-temporal patterns of cases observed. In addition, results highlight a number of socio-demographic risk factors and suggest that there is a relationship between cholera risk and water infrastructure. The analysis shows that populations living close to the sewer network, with high access to piped water are associated with at higher risk. One possible explanation for this observation is that sewer bursts led to the contamination of the piped water network. This could have turned access to piped water, usually assumed to be associated with reduced cholera risk, into a risk factor itself. Such events highlight the importance of maintenance in the provision of SDG improved water and sanitation infrastructure.

Data integration reveals dynamic and systematic patterns of breeding habitat use by a threatened shorebird

Incorporating species distributions into conservation planning has traditionally involved long-term representations of habitat use where temporal variation is averaged to reveal habitats that are most suitable across time. Advances in remote sensing and analytical tools have allowed for the integration of dynamic processes into species distribution modeling. Our objective was to develop a spatiotemporal model of breeding habitat use for a federally threatened shorebird (piping plover, Charadrius melodus). Piping plovers are an ideal candidate species for dynamic habitat models because they depend on habitat created and maintained by variable hydrological processes and disturbance. We integrated a 20-year (2000-2019) nesting dataset with volunteer-collected sightings (eBird) using point process modeling. Our analysis incorporated spatiotemporal autocorrelation, differential observation processes within data streams, and dynamic environmental covariates. We evaluated the transferability of this model in space and time and the contribution of the eBird dataset. eBird data provided more complete spatial coverage in our study system than nest monitoring data. Patterns of observed breeding density depended on both dynamic (e.g., surface water levels) and long-term (e.g., proximity to permanent wetland basins) environmental processes. Our study provides a framework for quantifying dynamic spatiotemporal patterns of breeding density. This assessment can be iteratively updated with additional data to improve conservation and management efforts, because reducing temporal variability to average patterns of use may cause a loss in precision for such actions.

Factors affecting spatiotemporal patterns of nest site selection and abundance in diamondback terrapins

Determining what factors influence the distribution and abundance of wildlife populations is crucial for implementing effective conservation and management actions. Yet, for species with dynamic seasonal, sex-, and age-specific spatial ecology, like the diamondback terrapin (Malaclemys terrapin; DBT), doing so can be challenging. Moreover, environmental factors that influence the distribution and abundance of DBT in their northernmost range have not been quantitatively characterized. We investigated proximity to nesting habitat as one potential driver of spatiotemporal variation in abundance in a three-step analytical approach. First, we used a scale selection resource selection function (RSF) approach based on landcover data from the National Landcover Database (NLCD) to identify the scale at which DBT are selecting for (or avoiding) landcover types to nest. Next, we used RSF to predict areas of suitable nesting habitat and created an index of nest suitability (NSI). Finally, analyzing visual count data using a generalized linear mixed model (GLMM), we investigate spatiotemporal drivers of relative abundance, with a specific focus on whether similar factors affect offshore abundance and onshore nest site selection. We found the scale of selection for developed and saltmarsh land use classes to be 550 and 600 m and open water land use classes to be 100. Selection was positive for nesting areas proximal to saltmarsh habitat and negative for developed and open water. Expected relative abundance was best explained by the interaction between NSI and day of season, where expected relative abundance was greater within high NSI areas during the nesting season (2.20 individuals, CI: 1.19-3.93) compared to areas of low NSI (1.84 individuals, CI: 1.10-3.10). Our results provide evidence that inferred spatial patterns of suitable nesting habitats explain spatiotemporal patterns of terrapin movement and abundance.

The relict population of Pinna nobilis in the Mar Menor is facing an uncertain future

Pinna nobilis is undergoing one of the most dramatic events suffered by an endangered species. An emerging disease has relegated its populations to coastal lagoons or estuaries with salinities beyond the 36.5-39.7 psu range. The Mar Menor is one of two such locations on the Spanish coastline. Poor environmental conditions and eutrophication and anoxia events, that became critical in 2016, 2019 and 2021, have reduced its population in >99 %. In this work, the spatial distribution of the species within the lagoon and the factors determining its survival along the successive crises of eutrophication are studied using a two-stage (presence/absence estimation and density modelling) Species Distribution Model. A potential area of 200.97 ha and an average density of 1.05 ind.100 m² is estimated for 2020. The viability of the Mar Menor population depends on management actions designed both for the species and to improve the lagoon environmental state.

Ecosystems Services Provided by Bats Are at Risk in Brazil

Ecosystem services (ES) are essential for human society worldwide. ES originate from ecological processes commonly occurring in well-preserved regions. Bats play an essential role in providing such services, primarily insect suppression, plant pollination, and seed dispersal. Human activities have affected Earth’s systems, compromising species and ecosystems and, consequently, the ES provision. Brazil is a country with many bat species but is also one of the world’s leaders in deforestation. Here we aimed to identify regions with high potential for bats ecosystem services provision, assuming that the predicted presence of the species represents the existence of the services. First, we used a Maximum Entropy algorithm to model the distribution of 128 bat species, which correspond to 71% of the Brazilian species. We classify all species into 10 different groups, which resulted from a combination of three body sizes and four predominant trophic guilds (i.e., frugivores, insectivores, nectarivores, and carnivores). The guilds were associated with services of seed dispersion, pest control, pollination, and animal control. Then, we created a 0.5 × 0.5-degree grid to represent an index of ES per guild, which is defined by the sum of the product of the bat’s size weight (1, 2, or 3) by the area occupied of each species in each cell. For comparison, the index was normalized and scaled from 0 to 1. Finally, we used a map of current land use to compare the effects of natural area suppression on the provision of ES in each cell. Our results indicate a substantial reduction in the provision of ES by bats in extensive parts of the central and eastern parts of Brazil, but changes in ES varies among biomes. While the loss of species is an important factor affecting the provision of ES in the Atlantic Forest and the Cerrado, the reduction of species occurrence is most important factor in Amazonia, Caatinga, or Pantanal regions. We suggest that degraded area restoration should be promoted in areas with high ES values and areas near cities and croplands and that a precautionary approach of promoting the conservation of high provider richness should be applied to protect the continuation of bat’s ES.

Identifying, reducing, and communicating uncertainty in community science: a focus on alien species

Community science (also often referred to as citizen science) provides a unique opportunity to address questions beyond the scope of other research methods whilst simultaneously engaging communities in the scientific process. This leads to broad educational benefits, empowers people, and can increase public awareness of societally relevant issues such as the biodiversity crisis. As such, community science has become a favourable framework for researching alien species where data on the presence, absence, abundance, phenology, and impact of species is important in informing management decisions. However, uncertainties arising at different stages can limit the interpretation of data and lead to projects failing to achieve their intended outcomes. Focusing on alien species centered community science projects, we identified key research questions and the relevant uncertainties that arise during the process of developing the study design, for example, when collecting the data and during the statistical analyses. Additionally, we assessed uncertainties from a linguistic perspective, and how the communication stages among project coordinators, participants and other stakeholders can alter the way in which information may be interpreted. We discuss existing methods for reducing uncertainty and suggest further solutions to improve data reliability. Further, we make suggestions to reduce the uncertainties that emerge at each project step and provide guidance and recommendations that can be readily applied in practice. Reducing uncertainties is essential and necessary to strengthen the scientific and community outcomes of community science, which is of particular importance to ensure the success of projects aimed at detecting novel alien species and monitoring their dynamics across space and time.

Wolf spatial behavior promotes encounters and kills of abundant prey

Predators use different spatial tactics to track the prey on the landscape. Three hypotheses describe spatial tactics: prey abundance for prey that are aggregated in space; prey habitat for uniformly distributed prey; and prey catchability for prey that are difficult to catch and kill. The gray wolf (Canis lupus) is a generalist predator that likely employs more than one spatial hunting tactic to match their diverse prey with distinct distributions and behavior that are available. We conducted a study on 17 GPS collared wolves in 6 packs in Riding Mountain National Park, Manitoba, Canada where wolves prey on moose (Alces alces) and elk (Cervus canadensis). We evaluated wolf selection for prey density, habitat selection and catchability on the landscape through within-territory habitat selection analysis. We reveal support for both the prey habitat and prey catchability hypotheses. For moose, their primary prey, wolves employed a mixed habitat and catchability tactic. Wolves used spaces described by the intersection of moose habitat and moose catchability. Wolves selected for the catchability of elk, their secondary prey, but not elk habitat. Counter to our predictions, wolves avoided areas of moose and elk density, likely highlighting the ongoing space race between predator and prey. We illustrate that of the three hypotheses the primary driver was prey catchability, where the interplay of both prey habitat with catchability culminate in predator spatial behaviour in a multiprey system.

Data-integration of opportunistic species observations into hierarchical modeling frameworks improves spatial predictions for urban red squirrels

The prevailing trend of increasing urbanization and habitat fragmentation makes knowledge of species’ habitat requirements and distribution a crucial factor in conservation and urban planning. Species distribution models (SDMs) offer powerful toolboxes for discriminating the underlying environmental factors driving habitat suitability. Nevertheless, challenges in SDMs emerge if multiple data sets - often sampled with different intention and therefore sampling scheme – can complement each other and increase predictive accuracy. Here, we investigate the potential of using recent data integration techniques to model potential habitat and movement corridors for Eurasian red squirrels (Sciurus vulgaris), in an urban area. We constructed hierarchical models integrating data sets of different quality stemming from unstructured on one side and semi-structured wildlife observation campaigns on the other side in a combined likelihood approach and compared the results to modeling techniques based on only one data source - wherein all models were fit with the same selection of environmental variables. Our study highlights the increasing importance of considering multiple data sets for SDMs to enhance their predictive performance. We finally used Circuitscape (version 4.0.5) on the most robust SDM to delineate suitable movement corridors for red squirrels as a basis for planning road mortality mitigation measures. Our results indicate that even though red squirrels are common, urban habitats are rather small and partially lack connectivity along natural connectivity corridors in Berlin. Thus, additional fragmentation could bring the species closer to its limit to persist in urban environments, where our results can act as a template for conservation and management implications.

Towards species-level forecasts of drought-induced tree mortality risk

Predicting species-level responses to drought at the landscape scale is critical to reducing uncertainty in future terrestrial carbon and water cycle projections. We embedded a stomatal optimisation model in the Community Atmosphere Biosphere Land Exchange (CABLE) land surface model and parameterised the model for 15 canopy dominant eucalypt tree species across South-Eastern Australia (mean annual precipitation range: 344-1424 mm yr^-1 ). We conducted three experiments: applying CABLE to the 2017-2019 drought; a 20% drier drought; and a 20% drier drought with a doubling of atmospheric carbon dioxide (CO₂ ). The severity of the drought was highlighted as for at least 25% of their distribution ranges, 60% of species experienced leaf water potentials beyond the water potential at which 50% of hydraulic conductivity is lost due to embolism. We identified areas of severe hydraulic stress within-species' ranges, but we also pinpointed resilience in species found in predominantly semiarid areas. The importance of the role of CO₂ in ameliorating drought stress was consistent across species. Our results represent an important advance in our capacity to forecast the resilience of individual tree species, providing an evidence base for decision-making around the resilience of restoration plantings or net-zero emission strategies.

Climate change increases cross-species viral transmission risk

At least 10,000 virus species have the ability to infect humans but, at present, the vast majority are circulating silently in wild mammals^1,2. However, changes in climate and land use will lead to opportunities for viral sharing among previously geographically isolated species of wildlife^3,4. In some cases, this will facilitate zoonotic spillover-a mechanistic link between global environmental change and disease emergence. Here we simulate potential hotspots of future viral sharing, using a phylogeographical model of the mammal-virus network, and projections of geographical range shifts for 3,139 mammal species under climate-change and land-use scenarios for the year 2070. We predict that species will aggregate in new combinations at high elevations, in biodiversity hotspots, and in areas of high human population density in Asia and Africa, causing the cross-species transmission of their associated viruses an estimated 4,000 times. Owing to their unique dispersal ability, bats account for the majority of novel viral sharing and are likely to share viruses along evolutionary pathways that will facilitate future emergence in humans. Notably, we find that this ecological transition may already be underway, and holding warming under 2 °C within the twenty-first century will not reduce future viral sharing. Our findings highlight an urgent need to pair viral surveillance and discovery efforts with biodiversity surveys tracking the range shifts of species, especially in tropical regions that contain the most zoonoses and are experiencing rapid warming.

Climate change increases cross-species viral transmission risk

At least 10,000 virus species have the ability to infect humans but, at present, the vast majority are circulating silently in wild mammals^1,2. However, changes in climate and land use will lead to opportunities for viral sharing among previously geographically isolated species of wildlife^3,4. In some cases, this will facilitate zoonotic spillover-a mechanistic link between global environmental change and disease emergence. Here we simulate potential hotspots of future viral sharing, using a phylogeographical model of the mammal-virus network, and projections of geographical range shifts for 3,139 mammal species under climate-change and land-use scenarios for the year 2070. We predict that species will aggregate in new combinations at high elevations, in biodiversity hotspots, and in areas of high human population density in Asia and Africa, causing the cross-species transmission of their associated viruses an estimated 4,000 times. Owing to their unique dispersal ability, bats account for the majority of novel viral sharing and are likely to share viruses along evolutionary pathways that will facilitate future emergence in humans. Notably, we find that this ecological transition may already be underway, and holding warming under 2 °C within the twenty-first century will not reduce future viral sharing. Our findings highlight an urgent need to pair viral surveillance and discovery efforts with biodiversity surveys tracking the range shifts of species, especially in tropical regions that contain the most zoonoses and are experiencing rapid warming.

Faunal Assemblages From Lower Bed I (Oldupai Gorge, Tanzania)

Palaeobiological and archeological excavations at the site of Ewass Oldupa, found in the western Plio-Pleistocene rift basin of Oldupai Gorge (also Olduvai Gorge), Tanzania, revealed rich fossiliferous levels and the earliest remains of human activity at Oldupai Gorge, dated to 2 million years ago. This paper provides zooarchaeological taxonomic, taphonomic, and behavioral analyses, applying several methods to explore the setting in which the assemblage was formed. We identified agency behind bone surface modifications, such as cut, tooth and percussion marks, and determined the frequency of carnivore tooth marks as well as their distribution on both discrete specimens and across species. In addition, our work revealed co-occurrence of modifications to include butchering marks and carnivore tooth marks. Ravaging levels were estimated as percentage. The faunal accumulation from Ewass Oldupa contains two cut marked specimens, together with low degrees of percussion and carnivore tooth marks, moderate ravaging, and diagenetic changes suggestive of water flow. Thus, multiple lines of evidence indicate a palimpsest accumulation. Taxonomic diversity is high, with up to 22 taxa representing diverse habitats, ranging from open grassland to wooded bushlands, as well as moist mosaics during Bed I. Overall, this archaeo-faunal assemblage speaks to increased behavioral versatility among Oldowan hominins and interactions with the carnivore guild.

A mechanistic model of snakebite as a zoonosis: Envenoming incidence is driven by snake ecology, socioeconomics and its impacts on snakes

Snakebite is the only WHO-listed, not infectious neglected tropical disease (NTD), although its eco-epidemiology is similar to that of zoonotic infections: envenoming occurs after a vertebrate host contacts a human. Accordingly, snakebite risk represents the interaction between snake and human factors, but their quantification has been limited by data availability. Models of infectious disease transmission are instrumental for the mitigation of NTDs and zoonoses. Here, we represented snake-human interactions with disease transmission models to approximate geospatial estimates of snakebite incidence in Sri Lanka, a global hotspot. Snakebites and envenomings are described by the product of snake and human abundance, mirroring directly transmitted zoonoses. We found that human-snake contact rates vary according to land cover (surrogate of occupation and socioeconomic status), the impacts of humans and climate on snake abundance, and by snake species. Our findings show that modelling snakebite as zoonosis provides a mechanistic eco-epidemiological basis to understand snakebites, and the possible implications of global environmental and demographic change for the burden of snakebite.

Modeling the Marked Presence-only Data: A Case Study of Estimating the Female Sex Worker Size in Malawi

Certain subpopulations like female sex workers (FSW), men who have sex with men (MSM), and people who inject drugs (PWID) often have higher prevalence of HIV/AIDS and are difficult to map directly due to stigma, discrimination, and criminalization. Fine-scale mapping of those populations contributes to the progress towards reducing the inequalities and ending the AIDS epidemic. In 2016 and 2017, the PLACE surveys were conducted at 3,290 venues in 20 out of the total 28 districts in Malawi to estimate the FSW sizes. These venues represent a presence-only data set where, instead of knowing both where people live and do not live (presence-absence data), only information about visited locations is available. In this study, we develop a Bayesian model for presence-only data and utilize the PLACE data to estimate the FSW size and uncertainty interval at a 1.5 × 1.5-km resolution for all of Malawi. The estimates can also be aggregated to any desirable level (city/district/region) for implementing targeted HIV prevention and treatment programs in FSW communities, which have been successful in lowering the incidence of HIV and other sexually transmitted infections.

Spatial Variation in Australian Neonicotinoid Usage and Priorities for Resistance Monitoring

Australia is the third largest exporting country of cereals and a leader in other major commodity crops, yet little data exist on pesticide usage patterns in agriculture. This knowledge gap limits the management of off-target chemical impacts, such as the evolution of pesticide resistance. Here, for the first time, we quantify spatial patterns in neonicotinoid applications in Australia by coalescing land use data with sales and market research data contributed by agrichemical and agribusiness companies. An example application to resistance management is explored through the development of recommendations for the cosmopolitan crop pest, Myzus persicae (Sulzer) (Hemiptera: Aphididae), utilizing spatial statistical models. This novel dataset identified Australian neonicotinoid usage patterns, with most neonicotinoid products in Australia applied as cereal, canola, cotton and legume seed treatments and soil applications in sugarcane. Importantly, there were strong regional differences in pesticide applications, which will require regionally specific strategies to manage off-target impacts. Indeed, the estimated spatial grid of neonicotinoid usage demonstrated a statistically significant influence on the distribution of M. persicae neonicotinoid resistance, indicating off-target impacts are unevenly distributed in space. Future research on neonicotinoid usage will be supported by the spatial grids generated and made available through this study. Overall, neonicotinoid pesticides are widely relied upon throughout Australia's plant production systems but will face increasing pressure from resistance evolution, emerging research on off-target impacts, and stricter regulatory pressures.

Data integration methods to account for spatial niche truncation effects in regional projections of species distribution

Many species distribution models (SDMs) are built with precise but geographically restricted presence-absence data sets (e.g., a country) where only a subset of the environmental conditions experienced by a species across its range is considered (i.e., spatial niche truncation). This type of truncation is worrisome because it can lead to incorrect predictions e.g., when projecting to future climatic conditions belonging to the species niche but unavailable in the calibration area. Data from citizen-science programs, species range maps or atlases covering the full species range can be used to capture those parts of the species' niche that are missing regionally. However, these data usually are too coarse or too biased to support regional management. Here, we aim to (1) demonstrate how varying degrees of spatial niche truncation affect SDMs projections when calibrated with climatically truncated regional data sets and (2) test the performance of different methods to harness information from larger-scale data sets presenting different spatial resolutions to solve the spatial niche truncation problem. We used simulations to compare the performance of the different methods, and applied them to a real data set to predict the future distribution of a plant species (Potentilla aurea) in Switzerland. SDMs calibrated with geographically restricted data sets expectedly provided biased predictions when projected outside the calibration area or time period. Approaches integrating information from larger-scale data sets using hierarchical data integration methods usually reduced this bias. However, their performance varied depending on the level of spatial niche truncation and how data were combined. Interestingly, while some methods (e.g., data pooling, downscaling) performed well on both simulated and real data, others (e.g., those based on a Poisson point process) performed better on real data, indicating a dependency of model performance on the simulation process (e.g., shape of simulated response curves). Based on our results, we recommend to use different data integration methods and, whenever possible, to make a choice depending on model performance. In any case, an ensemble modeling approach can be used to account for uncertainty in how niche truncation is accounted for and identify areas where similarities/dissimilarities exist across methods.

How deregulation, drought and increasing fire impact Amazonian biodiversity

Biodiversity contributes to the ecological and climatic stability of the Amazon Basin^1,2, but is increasingly threatened by deforestation and fire^3,4. Here we quantify these impacts over the past two decades using remote-sensing estimates of fire and deforestation and comprehensive range estimates of 11,514 plant species and 3,079 vertebrate species in the Amazon. Deforestation has led to large amounts of habitat loss, and fires further exacerbate this already substantial impact on Amazonian biodiversity. Since 2001, 103,079-189,755 km² of Amazon rainforest has been impacted by fires, potentially impacting the ranges of 77.3-85.2% of species that are listed as threatened in this region⁵. The impacts of fire on the ranges of species in Amazonia could be as high as 64%, and greater impacts are typically associated with species that have restricted ranges. We find close associations between forest policy, fire-impacted forest area and their potential impacts on biodiversity. In Brazil, forest policies that were initiated in the mid-2000s corresponded to reduced rates of burning. However, relaxed enforcement of these policies in 2019 has seemingly begun to reverse this trend: approximately 4,253-10,343 km² of forest has been impacted by fire, leading to some of the most severe potential impacts on biodiversity since 2009. These results highlight the critical role of policy enforcement in the preservation of biodiversity in the Amazon.

How decisions about fitting species distribution models affect conservation outcomes

Species distribution models (SDMs) are increasingly used in conservation and land-use planning as inputs to describe biodiversity patterns. These models can be built in different ways, and decisions about data preparation, selection of predictor variables, model fitting, and evaluation all alter the resulting predictions. Commonly, the true distribution of species is unknown and independent data to verify which SDM variant to choose are lacking. Such model uncertainty is of concern to planners. We analyzed how 11 routine decisions about model complexity, predictors, bias treatment, and setting thresholds for predicted values altered conservation priority patterns across 25 species. Models were created with MaxEnt and run through Zonation to determine the priority rank of sites. Although all SDM variants performed well (area under the curve >0.7), they produced spatially different predictions for species and different conservation priority solutions. Priorities were most strongly altered by decisions to not address bias or to apply binary thresholds to predicted values; on average 40% and 35%, respectively, of all grid cells received an opposite priority ranking. Forcing high model complexity altered conservation solutions less than forcing simplicity (14% and 24% of cells with opposite rank values, respectively). Use of fewer species records to build models or choosing alternative bias treatments had intermediate effects (25% and 23%, respectively). Depending on modeling choices, priority areas overlapped as little as 10-20% with the baseline solution, affecting top and bottom priorities differently. Our results demonstrate the extent of model-based uncertainty and quantify the relative impacts of SDM building decisions. When it is uncertain what the best SDM approach and conservation plan is, solving uncertainty or considering alterative options is most important for those decisions that change plans the most.

Citizen Science Data Collection for Integrated Wildlife Population Analyses

Citizen science, or community science, has emerged as a cost-efficient method to collect data for wildlife monitoring. To inform research and conservation, citizen science sampling designs should collect data that match the robust statistical analyses needed to quantify species and population patterns. Further increasing the contributions of citizen science, integrating citizen science data with other datasets and datatypes can improve population estimates and expand the spatiotemporal extent of inference. We demonstrate these points with a citizen science program called iSeeMammals developed in New York state in 2017 to supplement costly systematic spatial capture-recapture sampling by collecting opportunistic data from one-off observations, hikes, and camera traps. iSeeMammals has initially focused on the growing population of American black bear (Ursus americanus), with integrated analysis of iSeeMammals camera trap data with systematic data for a region with a growing bear population. The triumvirate of increased spatial and temporal coverage by at least twofold compared to systematic sampling, an 83% reduction in annual sampling costs, and improved density estimates when integrated with systematic data highlight the benefits of collecting presence-absence data in citizen science programs for estimating population patterns. Additional opportunities will come from applying presence-only data, which are oftentimes more prevalent than presence-absence data, to integrated models. Patterns in data submission and filtering also emphasize the importance of iteratively evaluating patterns in engagement, usability, and accessibility, especially focusing on younger adult and teenage demographics, to improve data quality and quantity. We explore how the development and use of integrated models may be paired with citizen science project design in order to facilitate repeated use of datasets in standalone and integrated analyses for supporting wildlife monitoring and informing conservation.

How to make use of unlabeled observations in species distribution modeling using point process models

Species distribution modeling, which allows users to predict the spatial distribution of species with the use of environmental covariates, has become increasingly popular, with many software platforms providing tools to fit such models. However, the species observations used can have varying levels of quality and can have incomplete information, such as uncertain or unknown species identity.In this paper, we develop two algorithms to classify observations with unknown species identities which simultaneously predict several species distributions using spatial point processes. Through simulations, we compare the performance of these algorithms using 7 different initializations to the performance of models fitted using only the observations with known species identity.We show that performance varies with differences in correlation among species distributions, species abundance, and the proportion of observations with unknown species identities. Additionally, some of the methods developed here outperformed the models that did not use the misspecified data. We applied the best-performing methods to a dataset of three frog species (Mixophyes).These models represent a helpful and promising tool for opportunistic surveys where misidentification is possible or for the distribution of species newly separated in their taxonomy.

Analysis of local habitat selection and large-scale attraction/avoidance based on animal tracking data: is there a single best method?

BACKGROUND

New wildlife telemetry and tracking technologies have become available in the last decade, leading to a large increase in the volume and resolution of animal tracking data. These technical developments have been accompanied by various statistical tools aimed at analysing the data obtained by these methods.

METHODS

We used simulated habitat and tracking data to compare some of the different statistical methods frequently used to infer local resource selection and large-scale attraction/avoidance from tracking data. Notably, we compared spatial logistic regression models (SLRMs), spatio-temporal point process models (ST-PPMs), step selection models (SSMs), and integrated step selection models (iSSMs) and their interplay with habitat and animal movement properties in terms of statistical hypothesis testing.

RESULTS

We demonstrated that only iSSMs and ST-PPMs showed nominal type I error rates in all studied cases, whereas SSMs may slightly and SLRMs may frequently and strongly exceed these levels. iSSMs appeared to have on average a more robust and higher statistical power than ST-PPMs.

CONCLUSIONS

Based on our results, we recommend the use of iSSMs to infer habitat selection or large-scale attraction/avoidance from animal tracking data. Further advantages over other approaches include short computation times, predictive capacity, and the possibility of deriving mechanistic movement models.