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Understanding how species composition varies across space and time is fundamental to ecology. While multiple methods having been created to characterize this variation through the identification of groups of species that tend to co-occur, most of these methods unfortunately are not able to represent gradual variation in species composition. The Latent Dirichlet Allocation (LDA) model is a mixed-membership method that can represent gradual changes in community structure by delineating overlapping groups of species, but its use has been limited because it requires abundance data and requires users to a priori set the number of groups. We substantially extend LDA to accommodate widely available presence/absence data and to simultaneously determine the optimal number of groups. Using simulated data, we show that this model is able to accurately determine the true number of groups, estimate the underlying parameters, and fit with the data. We illustrate this method with data from the North American Breeding Bird Survey (BBS). Overall, our model identified 18 main bird groups, revealing striking spatial patterns for each group, many of which were closely associated with temperature and precipitation gradients. Furthermore, by comparing the estimated proportion of each group for two time periods (1997-2002 and 2010-2015), our results indicate that nine (of 18) breeding bird groups exhibited an expansion northward and contraction southward of their ranges, revealing subtle but important community-level biodiversity changes at a continental scale that are consistent with those expected under climate change. Our proposed method is likely to find multiple uses in ecology, being a valuable addition to the toolkit of ecologists.

Knowledge of land‐use patterns that could affect animal population resiliency or vulnerability to environmental threats such as climate change is essential, yet the interactive effects of land use and climate on demography across space and time can be difficult to study. This is particularly true for migratory species, which rely on different landscapes throughout the year. Unlike most North American migratory waterfowl, populations of northern pintails (Anas acuta; hereafter pintails) have not recovered since the 1980s despite extended periods of abundant flooded wetlands (i.e. ponds). The mechanisms and drivers involved in this discrepancy remain poorly understood. While pintails are similar to other ducks in their dependence on ponds throughout their annual cycle, their extensive use of croplands for nesting differentiates them and makes them particularly vulnerable to changes in agricultural land use on prairie breeding grounds. Our intent was to quantify how changes in land use and ponds on breeding grounds have influenced pintail population dynamics by developing an integrated population model to analyse over five decades (1961–2014) of band‐recovery, breeding population survey, land‐use and pond count data. We focused especially on the interactive effects of pond counts and land use on pintail productivity, while accounting for density‐dependent processes. Pintail populations responded more strongly to annual variation in productivity than survival. Productivity was positively correlated with pond count and negatively correlated with agricultural intensification. Further, a positive interaction between pond count and agricultural intensification was insufficient to overcome the strong negative effect of agricultural intensification on pintail productivity across nearly all pond counts. The interaction also indicated that pintail populations were more negatively impacted by the decrease in ponds associated with climate change under higher agricultural intensification. Our results indicate that pintail populations have become more vulnerable to climate change under intensified land use, which suggests that future conservation strategies must adapt to these altered relationships. The interactive effects of land use and climate on demography should be considered more frequently in animal ecology, and integrated population models provide an adaptable framework to understand vital rates and their drivers simultaneously.

Climate change has been identified as one of the most important drivers of wildlife population dynamics. The in‐depth knowledge of the complex relationships between climate and population sizes through density dependent demographic processes is important for understanding and predicting population shifts under climate change, which requires integrated population models (IPMs) that unify the analyses of demography and abundance data. In this study we developed an IPM based on Gaussian approximation to dynamic N‐mixture models for large scale population data. We then analyzed four decades (1972–2013) of mallard Anas platyrhynchos breeding population survey, band‐recovery and climate data covering a large spatial extent from North American prairies through boreal habitat to Alaska. We aimed to test the hypothesis that climate change will cause shifts in population dynamics if climatic effects on demographic parameters that have substantial contribution to population growth vary spatially. More specifically, we examined the spatial variation of climatic effects on density dependent population demography, identified the key demographic parameters that are influential to population growth, and forecasted population responses to climate change. Our results revealed that recruitment, which explained more variance of population growth than survival, was sensitive to the temporal variation of precipitation in the southern portion of the study area but not in the north. Survival, by contrast, was insensitive to climatic variation. We then forecasted a decrease in mallard breeding population density in the south and an increase in the northwestern portion of the study area, indicating potential shifts in population dynamics under future climate change. Our results implied that different strategies need to be considered across regions to conserve waterfowl populations in the face of climate change. Our modelling approach can be adapted for other species and thus has wide application to understanding and predicting population dynamics in the presence of global change.

Dinorhynchus dybowskyi (Hemiptera: Pentatomidae: Asopinae) is used as a biological control agent against various insect pests for its predatory. In the present study, the complete mitochondrial genome (mitogenome) of the species was sequenced using the next-generation sequencing technology. The results showed that the mitogenome is 15,952 bp long, including 13 protein-coding genes (PCGs), 22 transfer RNAs (tRNAs), two ribosomal RNAs (rRNAs), and a control region. Furthermore, the gene order and orientation of this mitogenome are identical to those of most heteropterans. There are 21 intergenic spacers (of length 1–28 bp) and 13 overlapping regions (of length 1–23 bp) throughout the genome. The control region is 1,291 bp long. The start codon of the PCGs is ATN, except cox1 (TTG), and stop codon is TAA, except nad1 (TAG). The 22 tRNAs exhibit a typical cloverleaf secondary structure, except trnS1, which lacks a dihydrouridine (DHU) arm and trnV, where the DHU arm forms a simple loop. The analyses based on nucleotide sequences of the 13 PCGs by Bayesian Inference and maximum likelihood methods. The results support the monophyly of five superfamilies Aradoidea, Pentatomoidea, Pyrrhocoroidea, Lygaeoidea, and Coreoidea. Within Pentatomoidea, the relationship observed is as follows: (Plataspidae + Urostylididae) + (Pentatomidae + (Acanthosomatidae + (Cydnidae + (Scutelleridae + (Dinidoridae + Tessaratomidae))))), and D. dybowskyi was placed in Pentatomidae and close to Eurydema gebleri.

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The establishment of corridors can offset the negative effects of habitat fragmentation by connecting isolated habitat patches. However, the practical value of corridor planning is minimal if corridor identification is not based on reliable quantitative information about species-environment relationships. An example of this need for quantitative information is planning for giant panda conservation. Although the species has been the focus of intense conservation efforts for decades, most corridor projects remain hypothetical due to the lack of reliable quantitative researches at an appropriate spatial scale. In this paper, we evaluated a framework for giant panda forest corridor planning. We linked our field survey data with satellite imagery, and conducted species occupancy modelling to examine the habitat use of giant panda within the potential corridor area. We then conducted least-cost and circuit models to identify potential paths of dispersal across the landscape, and compared the predicted cost under current conditions and alternative conservation management options considered during corridor planning. We found that due to giant panda's association with areas of low elevation and flat terrain, human infrastructures in the same area have resulted in corridor fragmentation. We then identified areas with high potential to function as movement corridors, and our analysis of alternative conservation scenarios showed that both forest/bamboo restoration and automobile tunnel construction would significantly improve the effectiveness of corridor, while residence relocation would not significantly improve corridor effectiveness in comparison with the current condition. The framework has general value in any conservation activities that anticipate improving habitat connectivity in human modified landscapes. Specifically, our study suggested that, in this landscape, automobile tunnels are the best means to remove current barriers to giant panda movements caused by anthropogenic interferences.