2024
64) Valle, D.; Rifai, S.; Carrero, G.; Meiga, A. In press. An automated procedure to determining construction year of roads using a Least-Cost Path and a Before-After Control-Impact approach. Remote Sensing in Ecology and Evolution
63) Palmer, C.G; Valle, D.; Camp, E.; Bartels, W. L.; Monroe, M. 2024. Inclusive simulation game development to enhance Florida research and management: Cedar Key oyster fishery. Environmental Modeling and Software, 171, 105885.
2023
62) Machado, S.; Valle, D.; Toh, K. B.; Johnson, D. J. 2023. Forest resistance to drought in a humid tropical Dipterocarp forest in the Western Ghats of India. Biotropica, 55: 1093-1100.
61) Fletcher Jr., R. J.; Iezzi, M. E.; Guralnick, R.; Marx, A. J.; Ryan, S. J.; Valle, D. 2023. A framework for linking dispersal biology to connectivity across landscapes. Landscape Ecology, 38: 2487-2500.
60) Valle, D.; Izbicki, R.; Leite, R. 2023. Quantifying uncertainty in land-use land-cover classification using conformal statistics. Remote Sensing of the Environment. 295, 113682.
59) Giroux, A.; Ortega, Z.; Attias, N.; Desbiez, A.; Valle, D.; Oliveira-Santos, L. G. 2023. Activity modulation and selection for forests help giant anteaters to cope with temperature changes. Animal Behaviour. 201, 191-209
58) Machado, C. L.; Horta, M. C.; Valle, D.; Meiga, A. Y. Y.; Seva, A. P. 2023. Patterns and drivers of Human Visceral Leishmaniasis in Pernambuco (Brazil) from 2007 to 2018. PLOS Neglected Tropical Diseases 17(2): e0011108.
57) Cullen, J. A.; Attias, N.; Desbiez, A. L. J.; Valle, D. 2023. Biologging as an important tool to uncover behaviors of cryptic species. PeerJ. 11:e14726.
56) Shimizu, G.; Valle, D.; Izbicki, R. 2023. A new LDA formulation with covariates. Communications in Statistics - Simulation and Computation. p. 1-18
55) Uribe, M. R.; Coe, M. T.; Castanho, A. D. A.; Macedo, M. N.; Valle, D.; Brando, P. M. 2023. Net loss of biomass predicted for tropical biomes in a changing climate. Nature Climate Change 13: 274-281.
2022
54) Valle, D.; Silva, C.; Longo, M.; Brando, P. 2022. The Latent Dirichlet Allocation model applied to airborne LiDAR data: a case study on mapping forest degradation associated with fragmentation and fire in the Amazon region. Methods in Ecology and Evolution. 13 (6).
53) Jumani, S.; Deitch, M; Valle, D.; Machado, S.; Lecours, V.; Kaplan, D.; Krishnaswamy, J.; Howard, J. 2022. A new index to quantify longitudinal river fragmentation: conservation and management implications. Ecological Indicators. 136: 108680
52) Cullen, J.; Poli, C.; Fletcher, R.; Valle, D. 2022. Identifying latent behavioral states in animal movement with M4, a non-parametric Bayesian method. Methods in Ecology and Evolution 13:432-446.
51) Leite, R. V.; Silva, C. A.; Broadbent, E. B.; Amaral, C. H.; Liesenberg, V.; Almeida, D. R. A.; Mohran, M.; Godinho, S.; Cardil, A.; Hamamura, C.; Faria, B. L.; Brancalion, P. H. S.; Hirsch, A.; Marcatti, G. E.; Corte, A. P. D.; Zambrano, A. M. A.; Costa, M. B. .; Matricardi, E. A. T.; Silva, A. L.; Goya, L. R. R. Y.; Valbuena, R.; Mendonca, B. A. F.; Silva Junior, C. H. L.; Aragao, L. E. O. C.; Garcia, M.; Liang, J.; Merrick. T; Hudak, A. T.; Xiao, J.; Hancock, S.; Duncason, L.; Ferreira, M. P.; Valle, D.; Saatchi, S.; Klauberg, C. 2022. Large scale multi-layer fuel load characterization in tropical savanna using GEDI spaceborne lidar data. Remote Sensing of Environment. 268: 112764
50) Valle, D.; Jameel, Y.; Betancourt, B.; Azeria, E.; Attias, N.; Cullen, J. 2022. Automatic selection of number of clusters using Bayesian clustering and sparsity inducing priors. Ecological Applications, 32:e2524.
2021
49) Toh, K. B.; Millar, J.; Psychas, P.; Abuaku, B.; Ahorlu, C.; Oppong, S.; Koram, K.; Valle, D. 2021. Guiding placement of health facilities using malaria criteria and interactive tool. Malaria Journal, 20: 455
48) Burnett, J. L.; Dale, R.; Hou, C.-Y.; Palomo-Munoz, G.; Whitney, K. S.; Aulenbach, S.; Bristol, R. S.; Valle, D.; Wellman, T. 2021. Ten simple rules for creating a scientific web application. PLOS Computational Biology 17(12): e1009574
47) Valle, N., Antonenko, P., Valle, D., Dawson, Huggins-Manley, C. A., Baiser, B. 2021. The influence of task-value scaffolding in a predictive learning analytics dashboard on learners’ statistics anxiety, motivation, and performance. Computers & Education 173: 104288
46) Valle, N., Antonenko, P., Valle, D., Sommer, M., Huggins-Manley, C. A., Dawson, K., Kim, D., Baiser, B. 2021. Predict or describe? How learning analytics dashboard design influences motivation and statistics anxiety in an online statistics course. Educational Technology Research and Development. 69: 1405-1431
45) Valle, D.; Shimizu, G.; Izbicki, R.; Maracahipes, L.; Silverio, D.; Paolucci, L.; Jameel, Y.; Brando, P. 2021. The Latent Dirichlet Allocation model with covariates (LDAcov): A case study on the effect of fire on species composition in Amazonian forests. Ecology and Evolution 11: 7970-7979.
44) Chaves, W. A.; Valle, D.; Santos, A. T.; von Muhlen, E. M.; Wilcove, D. 2021. Investigating illegal activities that affect biodiversity: the case of wildlife consumption in the Brazilian Amazon. Ecological Applications. 31(7). e02402
43) Toh, B.; Valle, D.; Bliznyuk, N. 2021. Improving national level spatial mapping of malaria through alternative spatial and spatio-temporal models. Spatial and Spatio-temporal Epidemiology, 36, 100394.
42) Valle, D.; Laporta, G. 2021. A cautionary tale regarding the use of causal-inference to study how environmental change influences tropical diseases. American Journal of Tropical Medicine and Hygiene 104, 1960-1962.
2020
41) Chaves, W. A.; Valle, D.; Ma, L.; Santos, A. T.; Wilcove, D. 2020. Impacts of rural to urban migration, urganization, and generational change on consumption of wild animals in the Amazon. Conservation Biology, 35(4), 1186-1197.
40) Carrero, G. C.; Fearnside, P. M.; Valle, D.; Alves, C. S. 2020. Deforestation trajectories on a development frontier in the Brazilian Amazon: 35 years of settlement colonization, policy and economic shifts, and land accumulation. Environmental Management 66, 966-984.
39) Millar, J.; Toh, K.; Valle, D. 2020. To screen or not to screen: an interactive framework for minimizing costs of mass malaria treatment interventions. BMC Medicine, 149.
38) Valle, D.; Hyde, J.; Marsik, M.; Perz, S. 2020. Improved inference and prediction for imbalanced binary big data using case-control sampling: a case study on deforestation in the Amazon region. Remote Sensing 12, 1268: doi:10.3390/rs12081268.
37) Jameel, Y.; Valle, D.; Kay, P. 2020. Spatial variation in the detection rates of frequently studied pharmaceuticals in Asian, European and North American rivers. Science of the Total Environment, 724, 137947.
2019
36) Dietzel, K.; Valle, D.; Fierer, N.; U’Ren, J. M.; Barberan, A. 2019. Geographical distribution of fungal plant pathogens in dust across the United States. Frontiers in Ecology and Evolution 7:34. https://doi.org/10.3389/fevo.2019.00304
35) Valle, D.; Kaplan, D. 2019. Quantifying the impacts of dams on riverine hydrology under non-stationary conditions using incomplete data and Gaussian copula models. Science of the Total Environment 677: 599-611.
34) Amratia, P.; Psychas, P.; Abuaku, B.; Ahorlu, C.; Millar, J.; Oppong, S.; Koram, K.; Valle, D. 2019. Characterizing local-scale heterogeneity of malaria risk: a case study in Bunkpurugu-Yunyoo district in northern Ghana. Malaria Journal 18:81
33) Valle, D.; Toh, K.; Millar, J. 2019. Rapid prototyping of decision support tools for conservation. Conservation Biology, 33:6, 1448-1450
32) Valle, D.; Toh, K.; Laporta, G.; Zhao, Q. 2019. Ordinal regression models for zero-inflated and/or over-dispersed count data. Scientific Reports, 9:3046
31) Albuquerque, P.; Valle, D.; Li, D. 2019. Bayesian LDA for mixed-membership clustering analysis: the Rlda package. Knowledge-Based Systems 163: 988-995
2018
30) Hyde, J.; Bohlman, S.; Valle, D. 2018. Transmission lines are an under-acknowledged conservation threat to the Brazilian Amazon. Biological Conservation 228: 343-356.
29) Millar, J.; Psychas, P.; Abuaku, B.; Ahorlu, C.; Amratia, P.; Koram, K.; Oppong, S.; Valle, D. 2018. Detecting local risk factors for residual malaria in northern Ghana using Bayesian model averaging. Malaria Journal, 17:343. doi.org/10.1186/s12936-018-2491-2
28) Simmons, C. S.; Famolare, L.; Macedo, M.; Walker, R. T.; Coe, M.; Scheffers, B.; Arima, E.; Munoz-Carpena, R.; Valle, D.; Fraisee, C.; Moorecroft, P.; Diniz, M.; Diniz, M.; Szlafsztein, C.; Pereira, R.; Ruiz, C.; Rocha, G.; Juhn, D.; Lopes, L. O. C.; Waylen, M.; Antunes, A. 2018. Science in support of Amazonian conservation in the 21st century: the case of Brazil. Biotropica 50(6): 850-858.
27) Valle, D.; Albuquerque, P.; Barberan, A.; Zhao, Q.; Fletcher Jr, R. J. 2018. Extending the Latent Dirichlet Allocation model to presence/absence data: a case study on North American breeding birds and biogeographic shifts expected from climate change. Global Change Biology 1-13.
26) Robertson, E. P.; Fletcher Jr., R. J.; Cattau, C. E.; Udell, B. J.; Reichert, B. E.; Austin, J. D.; Valle, D. 2018. Isolating the roles of movement and reproduction on effective connectivity alters conservation priorities for an endangered bird. Proceedings of the National Academy of Sciences of the United States of America, 115(34): 8591-8596.
2017
25) Chaves, W. A.; Valle, D.; Monroe, M. C.; Wilkie, D. S.; Sieving, K. E.; Sadowsky, B. 2017. Changing wild meat consumption: an experiment in the central Amazon, Brazil. Conservation Letters. doi: 10.1111/conl.12391
24) Valle, D.; Cvetojevic, S.; Robertson, E.; Reichert, B. E.; Hochmair, H. H.; Fletcher Jr., R. J. 2017. Individual movement strategies revealed through novel clustering of emergent movement patterns. Scientific Reports 7, 44052. DOI: 10.1038/srep44052
23) Baiser, B.; Valle, D.; Zelazny, Z.; Burleigh, G. 2017. Non-random patterns of invasion and extinction reduce phylogenetic diversity in island bird assemblages. Ecography 40:001-014.
22) Seva, A. P.; Mao, L.; Ovallos, F. G.; Tucker-Lima, J. M.; Valle, D. 2017. Risk analysis and prediction of visceral leishmaniasis dispersion in Sao Paulo State, Brazil. PLOS Neglected Tropical Diseases 11(2): e0005353
21) Tucker-Lima, J.; Vittor, A.; Rifai, S.; Valle, D. 2017. Does deforestation promote or inhibit malaria transmission in the Amazon? A systematic literature review and critical appraisal of current evidence. Philosophical Transaction of the Royal Society B: Biological Sciences 372:20160125
2016
20) Valle, D.; Millar, J.; Amratia, P. 2016. Spatial heterogeneity can undermine the effectiveness of country-wide test and treat policy for malaria: a case study from Burkina Faso. Malaria Journal, 15:513.
19) Tucker-Lima, J.; Valle, D.; Moretto, E.; Pulice, S.; Zuca, N.; Roquetti, D.; Beduschi, L.; Praia, A.; Okamoto, C.; Carvalhaes, V.; Branco, E.; Barbezani, B.; Labandera, E.; Timpe, K.; Kaplan, D. 2016. A social-ecological database to advance research on infrastructure development impacts in the Brazilian Amazon. Scientific Data. 3:160071 doi: 10.1038/sdata.2016.71
2015
18) Valle, D.; Tucker Lima, J. M.; Millar, J.; Amratia, P.; Haque, U. 2015. Bias in logistic regression due to imperfect diagnostic test results and practical correction approaches. Malaria Journal, 14:434.
2014
17) Valle, D.; Tucker Lima, J. M. 2014. Large-scale drivers of malaria and priority areas for prevention and control in the Brazilian Amazon region using a novel multi-pathogen geospatial model. Malaria Journal, 13:443.
16) Valle, D.; Baiser, B.; Woodall, C.; Chazdon, R. 2014. Decomposing biodiversity data using the Latent Dirichlet Allocation model, a probabilistic multivariate statistical method. Ecology Letters, 17:1591-1601
15) Valle, D. 2014. Response to the critique by Hahn et al. entitled “Conservation and Malaria in the Brazilian Amazon”. American Journal of Tropical Medicine and Hygiene, 90(4), pp. 595-596
2013
14) Valle, D.; Clark, J. 2013. Improving the Modeling of Disease Data from the Government Surveillance System: a Case Study on Malaria in the Brazilian Amazon. PLOS Computational Biology 9(11): e1003312.
13) Valle, D.; Zaitchik, B.; Feingold, B.; Spangler, K.; Pan, W. 2013. Abundance of Water Bodies is Critical to Guide Mosquito Larva Control Interventions and Predict Risk of Mosquito-Borne Diseases. Parasites & Vectors, 6:179.
12) Valle, D.; Clark, J. 2013. Conservation Efforts May Increase Malaria Burden in the Brazilian Amazon. PLoS ONE 8(3): e57519.
11) Zhao, K.; Valle, D.; Popescu, S.; Zhang, X.; Mallick, B. 2013. Hyperspectral remote sensing of plant biochemistry using Bayesian model averaging with variable and band selection. Remote Sensing of Environment, 132, 102-119
< 2012
10) Valle, D.; Berdanier, A. 2012. Computer programming skills for environmental sciences. Bulletin of the Ecological Society of America, 93.
9) Valle, D.; Clark, J.; Zhao, K. 2011. Enhanced understanding of infectious diseases by fusing multiple datasets: a case study on malaria in the Western Brazilian Amazon region. PLOS One, 6(11): e27462
8) Clark, J.; Bell, D.; Hersh, M.; Kwit, M.; Moran, E.; Salk, C.; Stine, A.; Valle, D.; Zhu, K. 2011. Individual-scale variation, species-scale differences: Inference needed to understand diversity. Ecology Letters, 14: 1273-1287
7) Valle, D. 2011. Incorrect representation of uncertainty on the modeling of growth leads to biased estimates of future biomass. Ecological Applications, 21(4), pp. 1031-1036
6) Clark, J. S.; Bell, D. M.; Chu, C.; Courbaud, B.; Dietze, M. C.; Hersh, M.; HilleRisLambers, J.; Ibanez, I.; LaDeau, S.; McMahon, S.; Metcalf, J. E.; Mohan, J. E.; Moran, E.; Pangle, L.; Pearson, S. F.; Salk, C. F.; Shen, Z.; Valle, D.; Wyckoff, P. 2010. High dimensional coexistence based on individual variation: a synthesis of evidence. Ecological Monographs, 80(4), pp. 569-608.
5) Valle, D; Staudhammer, C. L.; Cropper Jr., W. P.; van Gardingen, P. 2009. The importance of multimodel projections to assess uncertainty in projections from simulation models. Ecological Applications 19(7), pp. 1680–1692
4) Valle, D.; Staudhammer, C.; Cropper Jr., W. P. 2007. Simulating nontimber forest product management in tropical mixed forests. Journal of Forestry, 105, 6, 301-306.
3) Valle, D.; Phillips, P.; Vidal, E.; Schulze, M.; Grogan, J.; Sales, M.; van Gardingen, P. 2007. Adaptation of a spatially explicit individual tree-based growth and yield model and long-term comparison between reduced-impact and conventional logging in eastern Amazonia, Brazil. Forest Ecology and Management. 243: 187-198.
2) Valle, D.; Schulze, M.; Vidal, E.; Sales, M.; Grogan, J. 2006. Identifying bias in stand-level growth and yield estimations: a case study in eastern Brazilian Amazonia. Forest Ecology and Management. 236: 127-135.
1) van Gardingen, P.; Valle, D.; Thompson, I. 2006. Evaluation of yield regulation options for primary forest in Tapajós National Forest, Brazil. Forest Ecology and Management. 231: 184-195.
64) Valle, D.; Rifai, S.; Carrero, G.; Meiga, A. In press. An automated procedure to determining construction year of roads using a Least-Cost Path and a Before-After Control-Impact approach. Remote Sensing in Ecology and Evolution
63) Palmer, C.G; Valle, D.; Camp, E.; Bartels, W. L.; Monroe, M. 2024. Inclusive simulation game development to enhance Florida research and management: Cedar Key oyster fishery. Environmental Modeling and Software, 171, 105885.
2023
62) Machado, S.; Valle, D.; Toh, K. B.; Johnson, D. J. 2023. Forest resistance to drought in a humid tropical Dipterocarp forest in the Western Ghats of India. Biotropica, 55: 1093-1100.
61) Fletcher Jr., R. J.; Iezzi, M. E.; Guralnick, R.; Marx, A. J.; Ryan, S. J.; Valle, D. 2023. A framework for linking dispersal biology to connectivity across landscapes. Landscape Ecology, 38: 2487-2500.
60) Valle, D.; Izbicki, R.; Leite, R. 2023. Quantifying uncertainty in land-use land-cover classification using conformal statistics. Remote Sensing of the Environment. 295, 113682.
59) Giroux, A.; Ortega, Z.; Attias, N.; Desbiez, A.; Valle, D.; Oliveira-Santos, L. G. 2023. Activity modulation and selection for forests help giant anteaters to cope with temperature changes. Animal Behaviour. 201, 191-209
58) Machado, C. L.; Horta, M. C.; Valle, D.; Meiga, A. Y. Y.; Seva, A. P. 2023. Patterns and drivers of Human Visceral Leishmaniasis in Pernambuco (Brazil) from 2007 to 2018. PLOS Neglected Tropical Diseases 17(2): e0011108.
57) Cullen, J. A.; Attias, N.; Desbiez, A. L. J.; Valle, D. 2023. Biologging as an important tool to uncover behaviors of cryptic species. PeerJ. 11:e14726.
56) Shimizu, G.; Valle, D.; Izbicki, R. 2023. A new LDA formulation with covariates. Communications in Statistics - Simulation and Computation. p. 1-18
55) Uribe, M. R.; Coe, M. T.; Castanho, A. D. A.; Macedo, M. N.; Valle, D.; Brando, P. M. 2023. Net loss of biomass predicted for tropical biomes in a changing climate. Nature Climate Change 13: 274-281.
2022
54) Valle, D.; Silva, C.; Longo, M.; Brando, P. 2022. The Latent Dirichlet Allocation model applied to airborne LiDAR data: a case study on mapping forest degradation associated with fragmentation and fire in the Amazon region. Methods in Ecology and Evolution. 13 (6).
53) Jumani, S.; Deitch, M; Valle, D.; Machado, S.; Lecours, V.; Kaplan, D.; Krishnaswamy, J.; Howard, J. 2022. A new index to quantify longitudinal river fragmentation: conservation and management implications. Ecological Indicators. 136: 108680
52) Cullen, J.; Poli, C.; Fletcher, R.; Valle, D. 2022. Identifying latent behavioral states in animal movement with M4, a non-parametric Bayesian method. Methods in Ecology and Evolution 13:432-446.
51) Leite, R. V.; Silva, C. A.; Broadbent, E. B.; Amaral, C. H.; Liesenberg, V.; Almeida, D. R. A.; Mohran, M.; Godinho, S.; Cardil, A.; Hamamura, C.; Faria, B. L.; Brancalion, P. H. S.; Hirsch, A.; Marcatti, G. E.; Corte, A. P. D.; Zambrano, A. M. A.; Costa, M. B. .; Matricardi, E. A. T.; Silva, A. L.; Goya, L. R. R. Y.; Valbuena, R.; Mendonca, B. A. F.; Silva Junior, C. H. L.; Aragao, L. E. O. C.; Garcia, M.; Liang, J.; Merrick. T; Hudak, A. T.; Xiao, J.; Hancock, S.; Duncason, L.; Ferreira, M. P.; Valle, D.; Saatchi, S.; Klauberg, C. 2022. Large scale multi-layer fuel load characterization in tropical savanna using GEDI spaceborne lidar data. Remote Sensing of Environment. 268: 112764
50) Valle, D.; Jameel, Y.; Betancourt, B.; Azeria, E.; Attias, N.; Cullen, J. 2022. Automatic selection of number of clusters using Bayesian clustering and sparsity inducing priors. Ecological Applications, 32:e2524.
2021
49) Toh, K. B.; Millar, J.; Psychas, P.; Abuaku, B.; Ahorlu, C.; Oppong, S.; Koram, K.; Valle, D. 2021. Guiding placement of health facilities using malaria criteria and interactive tool. Malaria Journal, 20: 455
48) Burnett, J. L.; Dale, R.; Hou, C.-Y.; Palomo-Munoz, G.; Whitney, K. S.; Aulenbach, S.; Bristol, R. S.; Valle, D.; Wellman, T. 2021. Ten simple rules for creating a scientific web application. PLOS Computational Biology 17(12): e1009574
47) Valle, N., Antonenko, P., Valle, D., Dawson, Huggins-Manley, C. A., Baiser, B. 2021. The influence of task-value scaffolding in a predictive learning analytics dashboard on learners’ statistics anxiety, motivation, and performance. Computers & Education 173: 104288
46) Valle, N., Antonenko, P., Valle, D., Sommer, M., Huggins-Manley, C. A., Dawson, K., Kim, D., Baiser, B. 2021. Predict or describe? How learning analytics dashboard design influences motivation and statistics anxiety in an online statistics course. Educational Technology Research and Development. 69: 1405-1431
45) Valle, D.; Shimizu, G.; Izbicki, R.; Maracahipes, L.; Silverio, D.; Paolucci, L.; Jameel, Y.; Brando, P. 2021. The Latent Dirichlet Allocation model with covariates (LDAcov): A case study on the effect of fire on species composition in Amazonian forests. Ecology and Evolution 11: 7970-7979.
44) Chaves, W. A.; Valle, D.; Santos, A. T.; von Muhlen, E. M.; Wilcove, D. 2021. Investigating illegal activities that affect biodiversity: the case of wildlife consumption in the Brazilian Amazon. Ecological Applications. 31(7). e02402
43) Toh, B.; Valle, D.; Bliznyuk, N. 2021. Improving national level spatial mapping of malaria through alternative spatial and spatio-temporal models. Spatial and Spatio-temporal Epidemiology, 36, 100394.
42) Valle, D.; Laporta, G. 2021. A cautionary tale regarding the use of causal-inference to study how environmental change influences tropical diseases. American Journal of Tropical Medicine and Hygiene 104, 1960-1962.
2020
41) Chaves, W. A.; Valle, D.; Ma, L.; Santos, A. T.; Wilcove, D. 2020. Impacts of rural to urban migration, urganization, and generational change on consumption of wild animals in the Amazon. Conservation Biology, 35(4), 1186-1197.
40) Carrero, G. C.; Fearnside, P. M.; Valle, D.; Alves, C. S. 2020. Deforestation trajectories on a development frontier in the Brazilian Amazon: 35 years of settlement colonization, policy and economic shifts, and land accumulation. Environmental Management 66, 966-984.
39) Millar, J.; Toh, K.; Valle, D. 2020. To screen or not to screen: an interactive framework for minimizing costs of mass malaria treatment interventions. BMC Medicine, 149.
38) Valle, D.; Hyde, J.; Marsik, M.; Perz, S. 2020. Improved inference and prediction for imbalanced binary big data using case-control sampling: a case study on deforestation in the Amazon region. Remote Sensing 12, 1268: doi:10.3390/rs12081268.
37) Jameel, Y.; Valle, D.; Kay, P. 2020. Spatial variation in the detection rates of frequently studied pharmaceuticals in Asian, European and North American rivers. Science of the Total Environment, 724, 137947.
2019
36) Dietzel, K.; Valle, D.; Fierer, N.; U’Ren, J. M.; Barberan, A. 2019. Geographical distribution of fungal plant pathogens in dust across the United States. Frontiers in Ecology and Evolution 7:34. https://doi.org/10.3389/fevo.2019.00304
35) Valle, D.; Kaplan, D. 2019. Quantifying the impacts of dams on riverine hydrology under non-stationary conditions using incomplete data and Gaussian copula models. Science of the Total Environment 677: 599-611.
34) Amratia, P.; Psychas, P.; Abuaku, B.; Ahorlu, C.; Millar, J.; Oppong, S.; Koram, K.; Valle, D. 2019. Characterizing local-scale heterogeneity of malaria risk: a case study in Bunkpurugu-Yunyoo district in northern Ghana. Malaria Journal 18:81
33) Valle, D.; Toh, K.; Millar, J. 2019. Rapid prototyping of decision support tools for conservation. Conservation Biology, 33:6, 1448-1450
32) Valle, D.; Toh, K.; Laporta, G.; Zhao, Q. 2019. Ordinal regression models for zero-inflated and/or over-dispersed count data. Scientific Reports, 9:3046
31) Albuquerque, P.; Valle, D.; Li, D. 2019. Bayesian LDA for mixed-membership clustering analysis: the Rlda package. Knowledge-Based Systems 163: 988-995
2018
30) Hyde, J.; Bohlman, S.; Valle, D. 2018. Transmission lines are an under-acknowledged conservation threat to the Brazilian Amazon. Biological Conservation 228: 343-356.
29) Millar, J.; Psychas, P.; Abuaku, B.; Ahorlu, C.; Amratia, P.; Koram, K.; Oppong, S.; Valle, D. 2018. Detecting local risk factors for residual malaria in northern Ghana using Bayesian model averaging. Malaria Journal, 17:343. doi.org/10.1186/s12936-018-2491-2
28) Simmons, C. S.; Famolare, L.; Macedo, M.; Walker, R. T.; Coe, M.; Scheffers, B.; Arima, E.; Munoz-Carpena, R.; Valle, D.; Fraisee, C.; Moorecroft, P.; Diniz, M.; Diniz, M.; Szlafsztein, C.; Pereira, R.; Ruiz, C.; Rocha, G.; Juhn, D.; Lopes, L. O. C.; Waylen, M.; Antunes, A. 2018. Science in support of Amazonian conservation in the 21st century: the case of Brazil. Biotropica 50(6): 850-858.
27) Valle, D.; Albuquerque, P.; Barberan, A.; Zhao, Q.; Fletcher Jr, R. J. 2018. Extending the Latent Dirichlet Allocation model to presence/absence data: a case study on North American breeding birds and biogeographic shifts expected from climate change. Global Change Biology 1-13.
26) Robertson, E. P.; Fletcher Jr., R. J.; Cattau, C. E.; Udell, B. J.; Reichert, B. E.; Austin, J. D.; Valle, D. 2018. Isolating the roles of movement and reproduction on effective connectivity alters conservation priorities for an endangered bird. Proceedings of the National Academy of Sciences of the United States of America, 115(34): 8591-8596.
2017
25) Chaves, W. A.; Valle, D.; Monroe, M. C.; Wilkie, D. S.; Sieving, K. E.; Sadowsky, B. 2017. Changing wild meat consumption: an experiment in the central Amazon, Brazil. Conservation Letters. doi: 10.1111/conl.12391
24) Valle, D.; Cvetojevic, S.; Robertson, E.; Reichert, B. E.; Hochmair, H. H.; Fletcher Jr., R. J. 2017. Individual movement strategies revealed through novel clustering of emergent movement patterns. Scientific Reports 7, 44052. DOI: 10.1038/srep44052
23) Baiser, B.; Valle, D.; Zelazny, Z.; Burleigh, G. 2017. Non-random patterns of invasion and extinction reduce phylogenetic diversity in island bird assemblages. Ecography 40:001-014.
22) Seva, A. P.; Mao, L.; Ovallos, F. G.; Tucker-Lima, J. M.; Valle, D. 2017. Risk analysis and prediction of visceral leishmaniasis dispersion in Sao Paulo State, Brazil. PLOS Neglected Tropical Diseases 11(2): e0005353
21) Tucker-Lima, J.; Vittor, A.; Rifai, S.; Valle, D. 2017. Does deforestation promote or inhibit malaria transmission in the Amazon? A systematic literature review and critical appraisal of current evidence. Philosophical Transaction of the Royal Society B: Biological Sciences 372:20160125
2016
20) Valle, D.; Millar, J.; Amratia, P. 2016. Spatial heterogeneity can undermine the effectiveness of country-wide test and treat policy for malaria: a case study from Burkina Faso. Malaria Journal, 15:513.
19) Tucker-Lima, J.; Valle, D.; Moretto, E.; Pulice, S.; Zuca, N.; Roquetti, D.; Beduschi, L.; Praia, A.; Okamoto, C.; Carvalhaes, V.; Branco, E.; Barbezani, B.; Labandera, E.; Timpe, K.; Kaplan, D. 2016. A social-ecological database to advance research on infrastructure development impacts in the Brazilian Amazon. Scientific Data. 3:160071 doi: 10.1038/sdata.2016.71
2015
18) Valle, D.; Tucker Lima, J. M.; Millar, J.; Amratia, P.; Haque, U. 2015. Bias in logistic regression due to imperfect diagnostic test results and practical correction approaches. Malaria Journal, 14:434.
2014
17) Valle, D.; Tucker Lima, J. M. 2014. Large-scale drivers of malaria and priority areas for prevention and control in the Brazilian Amazon region using a novel multi-pathogen geospatial model. Malaria Journal, 13:443.
16) Valle, D.; Baiser, B.; Woodall, C.; Chazdon, R. 2014. Decomposing biodiversity data using the Latent Dirichlet Allocation model, a probabilistic multivariate statistical method. Ecology Letters, 17:1591-1601
15) Valle, D. 2014. Response to the critique by Hahn et al. entitled “Conservation and Malaria in the Brazilian Amazon”. American Journal of Tropical Medicine and Hygiene, 90(4), pp. 595-596
2013
14) Valle, D.; Clark, J. 2013. Improving the Modeling of Disease Data from the Government Surveillance System: a Case Study on Malaria in the Brazilian Amazon. PLOS Computational Biology 9(11): e1003312.
13) Valle, D.; Zaitchik, B.; Feingold, B.; Spangler, K.; Pan, W. 2013. Abundance of Water Bodies is Critical to Guide Mosquito Larva Control Interventions and Predict Risk of Mosquito-Borne Diseases. Parasites & Vectors, 6:179.
12) Valle, D.; Clark, J. 2013. Conservation Efforts May Increase Malaria Burden in the Brazilian Amazon. PLoS ONE 8(3): e57519.
11) Zhao, K.; Valle, D.; Popescu, S.; Zhang, X.; Mallick, B. 2013. Hyperspectral remote sensing of plant biochemistry using Bayesian model averaging with variable and band selection. Remote Sensing of Environment, 132, 102-119
< 2012
10) Valle, D.; Berdanier, A. 2012. Computer programming skills for environmental sciences. Bulletin of the Ecological Society of America, 93.
9) Valle, D.; Clark, J.; Zhao, K. 2011. Enhanced understanding of infectious diseases by fusing multiple datasets: a case study on malaria in the Western Brazilian Amazon region. PLOS One, 6(11): e27462
8) Clark, J.; Bell, D.; Hersh, M.; Kwit, M.; Moran, E.; Salk, C.; Stine, A.; Valle, D.; Zhu, K. 2011. Individual-scale variation, species-scale differences: Inference needed to understand diversity. Ecology Letters, 14: 1273-1287
7) Valle, D. 2011. Incorrect representation of uncertainty on the modeling of growth leads to biased estimates of future biomass. Ecological Applications, 21(4), pp. 1031-1036
6) Clark, J. S.; Bell, D. M.; Chu, C.; Courbaud, B.; Dietze, M. C.; Hersh, M.; HilleRisLambers, J.; Ibanez, I.; LaDeau, S.; McMahon, S.; Metcalf, J. E.; Mohan, J. E.; Moran, E.; Pangle, L.; Pearson, S. F.; Salk, C. F.; Shen, Z.; Valle, D.; Wyckoff, P. 2010. High dimensional coexistence based on individual variation: a synthesis of evidence. Ecological Monographs, 80(4), pp. 569-608.
5) Valle, D; Staudhammer, C. L.; Cropper Jr., W. P.; van Gardingen, P. 2009. The importance of multimodel projections to assess uncertainty in projections from simulation models. Ecological Applications 19(7), pp. 1680–1692
4) Valle, D.; Staudhammer, C.; Cropper Jr., W. P. 2007. Simulating nontimber forest product management in tropical mixed forests. Journal of Forestry, 105, 6, 301-306.
3) Valle, D.; Phillips, P.; Vidal, E.; Schulze, M.; Grogan, J.; Sales, M.; van Gardingen, P. 2007. Adaptation of a spatially explicit individual tree-based growth and yield model and long-term comparison between reduced-impact and conventional logging in eastern Amazonia, Brazil. Forest Ecology and Management. 243: 187-198.
2) Valle, D.; Schulze, M.; Vidal, E.; Sales, M.; Grogan, J. 2006. Identifying bias in stand-level growth and yield estimations: a case study in eastern Brazilian Amazonia. Forest Ecology and Management. 236: 127-135.
1) van Gardingen, P.; Valle, D.; Thompson, I. 2006. Evaluation of yield regulation options for primary forest in Tapajós National Forest, Brazil. Forest Ecology and Management. 231: 184-195.
