LAND COVER EFFECTS ON REGENERANTS DENSITY AND RICHNESS IN RESTORATION TREATMENTS IN THE ATLANTIC RAINFOREST BIOME

Luz, Alexia Rodrigues Campos; Massi, Klécia Gili; Pocius, Olidan; Campos, Marina Merlo Sampaio de; Santiami, Edson Luís

doi:10.53661/1806-9088202448263729

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Scientific Article • Rev. Árvore 48 • 2024 • https://doi.org/10.53661/1806-9088202448263729 copy

LAND COVER EFFECTS ON REGENERANTS DENSITY AND RICHNESS IN RESTORATION TREATMENTS IN THE ATLANTIC RAINFOREST BIOME

EFEITOS DA COBERTURA DO SOLO NA ABUNDÂNCIA E RIQUEZA DE REGENERANTES EM TRATAMENTOS DE RESTAURAÇÃO NO BIOMA MATA ATLÂNTICA

Authorship SCIMAGO INSTITUTIONS RANKINGS

ABSTRACT

There is good evidence that the results of a restoration program depend largely on the landscape context. In restoration projects, it is crucial to consider the previous land uses and covers and landscape configuration as they have a significant impact on the entire process, especially seed dispersal and natural regeneration of sites. Thus, the objective of this work was to verify the effect of landscape (i.e., land use) on restoration success of total planting, seed sowing and natural regeneration sites in the Atlantic Forest biome, southeast Brazil. The methodology employed was based on demonstration units of restoration. The focus was on regenerant richness and abundance indicators, along with land use and land cover data (class area) from 2010, 2015, and 2020, obtained from the MapBiomas Project. Generalized linear models and correlation analysis were used for the study. We hypothesize that a landscape with more forest and natural regeneration cover (mosaic of agriculture and pasture) would positively affect regenerants in restored sites, which we only observed for natural regeneration treatment sites. In active restoration treatment sites, mosaic of agriculture and pasture was negatively and farming was positively associated to regenerants. We also found greater density and richness of regenerants in natural regeneration treatment sites than in seed sowing and seedling planting. The influence of the surrounding landscape, particularly mosaic, played a crucial role in this success. In addition, land use cover history, as we observed in classes in the last five and ten years, did explain regenerants in studied sites. Thus, results show that the characterization of landscape context and previous land-use history is essential to understand the limitations to succession and define cost-effective restoration strategies.

Keywords:
Land use; Regeneration; Surrounding landscape

Table 1 Demonstration units (DUs), your average central coordinate and its hydrographic basin, climate, biome, soil, land use and relief in studied sites of Conservador da Mantiqueira restoration program, southeast Brazil.

DUs	Hydrographic basin	Climate	Biome	Soil	Land use	Relief
Cruzeiro (22°39’00’’S, 44°57’12’’ W)	Rio Paraíba do Sul	Cfa	Atlantic rainforest	Red-yellow Latosols	Agriculture (pasture), urban areas, native vegetation	Hilly to mountains (15 a 24 degrees)
Inconfidentes (22°18’23’’S, 46°19’47’’W)	Rio Grande	Cwb	Atlantic rainforest	Latosols, Gleysols, Cambisols	Agriculture (pasture and coffee), urban areas, native vegetation	Hilly to mountains
Machado (21°42’11’’S, 45°52’55’’W)	Rio Grande	Cwb	Atlantic rainforest	Red-yellow Latosols and Dystrophic red Latosols	Agriculture (pasture and coffee), urban areas, native vegetation	Hilly to mountains (8 to 45%)
Muzambinho (21°20’55’’S, 46°32’05’’W)	Rio Grande	Cwb	Atlantic rainforest	Dystrophic red Latosols	Agriculture (pasture and coffee), urban areas, native vegetation	Hilly to mountains (8 to 45%)
Piracicaba (22°48’13’’S, 47°48’31’’O)	Rio Piracicaba	Aw	Atlantic rainforest	Red-yellow Argisols and Neosols	Agriculture (sugar cane), urban areas	Flat

Table 2 Average percentage cover of forest, mosaic and farming in 2020, in the last five and ten years in natural regeneration, seedling planting and seed sowing treatments in Conservador da Mantiqueira restoration program, southeast Brazil.

	Natural regeneration	Seedling planting	Seed sowing
Forest 2020	10.03	12.45	8.97
Forest in the last 5 years	0.07	0.14	-0.51
Forest in the last 10 years	-0.26	-0.02	-1.33
Mosaic 2020	29.80	31.23	29.82
Mosaic in the last 5 years	2.61	3.60	3.56
Mosaic in the last 10 years	-1.50	-0.67	-0.64
Farming 2020	33.71	28.86	35.28
Farming in the last 5 years	-2.83	-3.82	-3.11
Farming in the last 10 years	1.01	0.28	1.23
Regenerants density	18.38	9.28	11.20
Regenerants richness	3.20	1.85	2.60

Table 3 Regenerant density (A, B and C) and richness (D, E and F) of the natural regeneration treatment (A and D), seedling planting (B and E) and seed sowing (C and F). Full and Reduced models. P-values <0.05 represent significant variables.

	Forest				Mosaic				Farming
	Full		Reduced		Full		Reduced		Full		Reduced
Predictors	Estimates	p	Estimates	p	Estimates	p	Estimates	p	Estimates	p	Estimates	p
Predictors	(SE)		(SE)		(SE)		(SE)		(SE)		(SE)
A
(Intercept)	2.71	<.001	2.72	<.001	2.63	<.001	2.65	<.001	2.65	<.001	2.65	<.001
(Intercept)	(0.21)		(0.20)		(0.17)		(0.17)		(0.20)		(0.18)
2020 (current)	-0.30	0.413			0.20	0.499			0.52	0.523	0.45	0.056
2020 (current)	(0.37)				(0.30)				(0.81)		(0.24)
5 years	-0.42	0.436	-0.64	0.089	0.46	0.171	0.70	<.001	-0.91	0.051	-0.88	<.001
5 years	(0.53)		(0.38)		(0.33)		(0.19)		(0.47)		(0.21)
10 years	0.72	<.001	0.71	0.002	-0.33	0.17			-0.06	0.914
10 years	(0.22)		(0.23)		(0.24)				(0.52)
Observations	21		21		21		21		21		21
Nagelkerke's R2	0.60		0.56		0.82		0.76		0.76		0.76
p-value	0.011		0.006		< .001		< .001		0.001		< .001
B
(Intercept)	2.29	<.001	2.23	<.001	1.51	<.001	1.50	<.001	1.62	<.001	1.73	<.001
(Intercept)	(0.26)		(0.28)		(0.37)		(0.36)		(0.33)		(0.36)
2020 (current)	-0.61	0.070			-1.35	0.004	-1.24	0.002	3.01	<.001	1.47	<.001
2020 (current)	(0.34)				(0.47)		(0.40)		(0.85)		(0.21)
5 years	0.70	0.184			0.16	0.765			-0.04	0.882
5 years	(0.53)				(0.53)				(0.26)
10 years	-0.43	0.284	-0.06	0.843	-0.79	0.048	-0.92	<.001	-1.51	0.068
10 years	(0.40)		(0.29)		(0.40)		(0.26)		(0.83)
Observations	21		21		21		21		21		21
Nagelkerke's R2	0.09		0.004		0.72		0.72		0.70		0.62
p-value	0.710		0.854		0.002		< .001		0.002		< .001
C
(Intercept)	2.37	<.001	2.41	<.001	2.31	<.001	2.34	<.001	2.29	<.001	2.31	<.001
(Intercept)	(0.23)		(0.20)		(0.21)		(0.20)		(0.23)		(0.21)
2020 (current)	-0.62	0.070			-0.44	0.511			1.24	0.060	0.55	0.002
2020 (current)	(0.34)				(0.67)				(0.66)		(0.18)
5 years	0.73	0.238			0.45	0.438			-0.93	0.017	-0.55	<.001
5 years	(0.62)				(0.58)				(0.39)		(0.16)
10 years	-0.22	0.606	0.08	0.717	-0.07	0.849	-0.43	<.001	-0.60	0.247
10 years	(0.43)		(0.22)		(0.39)		(0.12)		(0.52)
Observations	21		21		21		21		21		21
Nagelkerke's R2	0.14		0.012		0.34		0.27		0.43		0.38
p-value	0.556		0.681		0.134		0.037		0.061		0.042
D
(Intercept)	1.11	<.001	1.11	<.001	1.11	<.001	1.11	<.001	1.09	<.001	1.12	<.001
(Intercept)	(0.15)		(0.14)		(0.14)		(0.13)		(0.13)		(0.13)
2020 (current)	-0.45	0.110	-0.33	0.079	-0.10	0.694			0.80	0.170
2020 (current)	(0.28)		(0.19)		(0.25)				(0.59)
5 years	0.16	0.570			0.43	0.072	0.33	0.006	-0.71	0.073	-0.31	0.010
5 years	(0.27)				(0.24)		(0.12)		(0.40)		(0.12)
10 years	0.29	0.070	0.32	0.007	0.10	0.654			-0.53	0.153
10 years	(0.16)		(0.12)		(0.22)				(0.37)
Observations	21		21		21		21		21		21
Nagelkerke's R2	0.41		0.39		0.39		0.38		0.51		0.31
p-value	0.067		0.034		0.077		0.010		0.024		0.022
E
(Intercept)	0.58	0.014	0.62	0.004	0.28	0.271	0.28	0.247	0.39	0.109	0.43	0.056
(Intercept)	(0.24)		(0.21)		(0.26)		(0.24)		(0.24)		(0.22)
2020 (current)	-0.19	0.614			-0.71	0.073	-0.71	0.016	1.16	0.087	0.65	<.001
2020 (current)	(0.37)				(0.40)		(0.30)		(0.68)		(0.20)
5 years	0.50	0.400			-0.003	0.994			0.07	0.651
5 years	(0.59)				(0.35)				(0.16)
10 years	-0.39	0.309	-0.09	0.629	-0.54	0.067	-0.54	0.002	-0.47	0.506
10 years	(0.39)		(0.19)		(0.30)		(0.17)		(0.71)
Observations	21		21		21		21		21		21
Nagelkerke's R2	0.15		0.02		0.67		0.67		0.64		0.59
p-value	0.432		0.578		< .001		< .001		0.002		< .001
F
(Intercept)	0.92	<.001	0.94	<.001	0.89	<.001	0.91	<.001	0.92	<.001	0.92	<.001
(Intercept)	(0.19)		(0.17)		(0.17)		(0.17)		(0.20)		(0.17)
2020 (current)	0.09	0.842			-0.45	0.285			0.41	0.583
2020 (current)	(0.47)				(0.42)				(0.75)
5 years	-0.57	0.295	-0.08	0.606	0.70	0.124	0.27	0.024	-0.41	0.246	-0.21	0.201
5 years	(0.55)		(0.16)		(0.46)		(0.12)		(0.35)		(0.16)
10 years	0.46	0.111			0.34	0.391			-0.24	0.617
10 years	(0.29)				(0.40)				(0.48)
Observations	21		21		21		21		21		21
Nagelkerke's R2	0.18		0.02		0.32		0.23		0.19		0.13
p-value	0.394		0.553		0.127		0.050		0.357		0.149

Sociedade de Investigações Florestais Universidade Federal de Viçosa, CEP: 36570-900 - Viçosa - Minas Gerais - Brazil, Tel: (55 31) 3612-3959 - Viçosa - MG - Brazil
E-mail: rarvore@sif.org.br

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[1] *Corresponding author.