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1. An empirical evaluation of camera trap study design: How many, how long and when?

   https://doi.org/10.1111/2041-210X.13370  

   Kays, Roland; Arbogast, Brian S.; Baker‐Whatton, Megan; Beirne, Chris; Boone, Hailey M.; Bowler, Mark; Burneo, Santiago F.; Cove, Michael V.; Ding, Ping; Espinosa, Santiago; et al (April 2020, Methods in Ecology and Evolution)

   Abstract Camera traps deployed in grids or stratified random designs are a well‐established survey tool for wildlife but there has been little evaluation of study design parameters.We used an empirical subsampling approach involving 2,225 camera deployments run at 41 study areas around the world to evaluate three aspects of camera trap study design (number of sites, duration and season of sampling) and their influence on the estimation of three ecological metrics (species richness, occupancy and detection rate) for mammals.We found that 25–35 camera sites were needed for precise estimates of species richness, depending on scale of the study. The precision of species‐level estimates of occupancy (ψ) was highly sensitive to occupancy level, with <20 camera sites needed for precise estimates of common (ψ > 0.75) species, but more than 150 camera sites likely needed for rare (ψ < 0.25) species. Species detection rates were more difficult to estimate precisely at the grid level due to spatial heterogeneity, presumably driven by unaccounted habitat variability factors within the study area. Running a camera at a site for 2 weeks was most efficient for detecting new species, but 3–4 weeks were needed for precise estimates of local detection rate, with no gains in precision observed after 1 month. Metrics for all mammal communities were sensitive to seasonality, with 37%–50% of the species at the sites we examined fluctuating significantly in their occupancy or detection rates over the year. This effect was more pronounced in temperate sites, where seasonally sensitive species varied in relative abundance by an average factor of 4–5, and some species were completely absent in one season due to hibernation or migration.We recommend the following guidelines to efficiently obtain precise estimates of species richness, occupancy and detection rates with camera trap arrays: run each camera for 3–5 weeks across 40–60 sites per array. We recommend comparisons of detection rates be model based and include local covariates to help account for small‐scale variation. Furthermore, comparisons across study areas or times must account for seasonality, which could have strong impacts on mammal communities in both tropical and temperate sites. 

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2. Global patterns and drivers of alpine plant species richness

   https://doi.org/10.1111/geb.13297  

   Testolin, Riccardo; Attorre, Fabio; Borchardt, Peter; Brand, Robert_F; Bruelheide, Helge; Chytrý, Milan; De_Sanctis, Michele; Dolezal, Jiri; Finckh, Manfred; Haider, Sylvia; et al (March 2021, Global Ecology and Biogeography)

   Abstract AimAlpine ecosystems differ in area, macroenvironment and biogeographical history across the Earth, but the relationship between these factors and plant species richness is still unexplored. Here, we assess the global patterns of plant species richness in alpine ecosystems and their association with environmental, geographical and historical factors at regional and community scales. LocationGlobal. Time periodData collected between 1923 and 2019. Major taxa studiedVascular plants. MethodsWe used a dataset representative of global alpine vegetation, consisting of 8,928 plots sampled within 26 ecoregions and six biogeographical realms, to estimate regional richness using sample‐based rarefaction and extrapolation. Then, we evaluated latitudinal patterns of regional and community richness with generalized additive models. Using environmental, geographical and historical predictors from global raster layers, we modelled regional and community richness in a mixed‐effect modelling framework. ResultsThe latitudinal pattern of regional richness peaked around the equator and at mid‐latitudes, in response to current and past alpine area, isolation and the variation in soil pH among regions. At the community level, species richness peaked at mid‐latitudes of the Northern Hemisphere, despite a considerable within‐region variation. Community richness was related to macroclimate and historical predictors, with strong effects of other spatially structured factors. Main conclusionsIn contrast to the well‐known latitudinal diversity gradient, the alpine plant species richness of some temperate regions in Eurasia was comparable to that of hyperdiverse tropical ecosystems, such as the páramo. The species richness of these putative hotspot regions is explained mainly by the extent of alpine area and their glacial history, whereas community richness depends on local environmental factors. Our results highlight hotspots of species richness at mid‐latitudes, indicating that the diversity of alpine plants is linked to regional idiosyncrasies and to the historical prevalence of alpine ecosystems, rather than current macroclimatic gradients. 

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3. Considerations for fitting occupancy models to data from eBird and similar volunteer-collected data

   https://doi.org/10.1093/ornithology/ukad035  

   Hochachka, Wesley_M; Ruiz-Gutierrez, Viviana; Johnston, Alison (July 2023, Ornithology)

   Abstract An occupancy model makes use of data that are structured as sets of repeated visits to each of many sites, in order to estimate the actual probability of occupancy (i.e. proportion of occupied sites) after correcting for imperfect detection using the information contained in the sets of repeated observations. We explore the conditions under which preexisting, volunteer-collected data from the citizen science project eBird can be used for fitting occupancy models. Because the majority of eBird’s data are not collected in the form of repeated observations at individual locations, we explore 2 ways in which the single-visit records could be used in occupancy models. First, we assess the potential for space-for-time substitution: aggregating single-visit records from different locations within a region into pseudo-repeat visits. On average, eBird’s observers did not make their observations at locations that were representative of the habitat in the surrounding area, which would lead to biased estimates of occupancy probabilities when using space-for-time substitution. Thus, the use of space-for-time substitution is not always appropriate. Second, we explored the utility of including data from single-visit records to supplement sets of repeated-visit data. In a simulation study we found that inclusion of single-visit records increased the precision of occupancy estimates, but only when detection probabilities are high. When detection probability was low, the addition of single-visit records exacerbated biases in estimates of occupancy probability. We conclude that subsets of data from eBird, and likely from similar projects, can be used for occupancy modeling either using space-for-time substitution or supplementing repeated-visit data with data from single-visit records. The appropriateness of either alternative will depend on the goals of a study and on the probabilities of detection and occupancy of the species of interest. 

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4. Integrated community occupancy models: A framework to assess occurrence and biodiversity dynamics using multiple data sources

   https://doi.org/10.1111/2041-210X.13811  

   Doser, Jeffrey W.; Leuenberger, Wendy; Sillett, T. Scott; Hallworth, Michael T.; Zipkin, Elise F. (February 2022, Methods in Ecology and Evolution)

   Abstract 1. The occurrence and distributions of wildlife populations and communities are shifting as a result of global changes. To evaluate whether these shifts are negatively impacting biodiversity processes, it is critical to monitor the status, trends and effects of environmental variables on entire communities. However, modelling the dynamics of multiple species simultaneously can require large amounts of diverse data, and few modelling approaches exist to simultaneously provide species and community‐level inferences. 2. We present an ‘integrated community occupancy model’ (ICOM) that unites principles of data integration and hierarchical community modelling in a single framework to provide inferences on species‐specific and community occurrence dynamics using multiple data sources. The ICOM combines replicated and nonreplicated detection–nondetection data sources using a hierarchical framework that explicitly accounts for different detection and sampling processes across data sources. We use simulations to compare the ICOM to previously developed hierarchical community occupancy models and single species integrated distribution models. We then apply our model to assess the occurrence and biodiversity dynamics of foliage‐gleaning birds in the White Mountain National Forest in the northeastern USA from 2010 to 2018 using three independent data sources. 3. Simulations reveal that integrating multiple data sources in the ICOM increased precision and accuracy of species and community‐level inferences compared to single data source models, although benefits of integration were dependent on the information content of individual data sources (e.g. amount of replication). Compared to single species models, the ICOM yielded more precise species‐level estimates. Within our case study, the ICOM had the highest out‐of‐sample predictive performance compared to single species models and models that used only a subset of the three data sources. 4. The ICOM provides more precise estimates of occurrence dynamics compared to multi‐species models using single data sources or integrated single‐species models. We further found that the ICOM had improved predictive performance across a broad region of interest with an empirical case study of forest birds. The ICOM offers an attractive approach to estimate species and biodiversity dynamics, which is additionally valuable to inform management objectives of both individual species and their broader communities. 

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5. Regional Occupancy Is Negatively Related to Richness Across Time and Space

   https://doi.org/10.1111/geb.70009  

   Shipley, B_R; Saupe, E_E (February 2025, Global Ecology and Biogeography)

   ABSTRACT AimBiological diversity is shaped by processes occurring at different spatial and temporal scales. However, the direct influence of the spatial and temporal scale on patterns of occupancy is still understudied. Today, occupancy is often negatively correlated with species richness, but it is unknown whether this relationship is scale dependent and consistent through time. Here, we use datasets of contemporary and paleontological communities to explore the occupancy‐richness relationship across space and time, examining how scale influences this relationship. LocationVarying spatial extents with global coverage. TimeVaries from 7 mya to 2021 CE. Taxaforaminifera, mammals, birds, fish, and plants. MethodsWe gathered datasets spanning different spatial, temporal, and taxonomic extents. We binned each dataset into distinct time periods and spatially subsampled them into regional pools of varying sizes. We calculated regional occupancy and richness for each pool, measuring the strength of the relationship between the two. Using linear mixed models, we related the occupancy‐richness relationship to the size of the regional pools, overall species richness, and climatic changes through time. ResultsWe observed nearly ubiquitous negative occupancy‐richness relationships across taxa, spatial scale, and time. The size of the regional pools and time bins had no consistent effects on the strength of the relationship, but the strength of the negative relationship varied substantially among taxa, with foraminifera and North American pollen showing weaker relationships than mammals and birds. Changes in this relationship through time were not driven by climatic perturbations but by the species richness observed across all regional pools. ConclusionsPatterns of regional richness and occupancy are consistently negatively related and independent of spatial and temporal scale and of direct climatic changes. However, differences in the ecology of species (e.g., dispersal ability) and changes in biodiversity and community composition through time may cause fluctuations in the strength of the occupancy‐richness relationship. 

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